What is Data Scraping?

Data scraping—especially on a large scale—is a complex process involving multiple stages, tools, and considerations. At a high level, data scraping refers to the act of identifying a website or other source that contains desirable information and using software to pull the target information from the site in large volumes.

Sources for data can range from e-commerce sites and social media platforms to public databases and product review sites. Targeted data is usually text-based. Data scraping generally targets structured data from databases and similarly structured formats. Web scraping is a kind of data scraping that targets and extracts unstructured data from web pages.

As more businesses become reliant on data analytics for operations, business intelligence, and decision-making, the demand for both raw and processed data is on the rise. Gathering up-to-date and reliable data using traditional methods can be time-consuming and expensive—especially for smaller businesses with limited user bases. Using automated tools to “scrape” data from multiple sources, businesses can cast a wider net for the kind and amount of information they gather.

There are a number of approaches to data scraping and a wide variety of tools. Depending on the use case, there are also legal and ethical concerns to keep in mind around what data is gathered and how it is used.

How Data Scraping Works

Data scraping is done using code that searches the website or other source and retrieves the sought-after information. While it’s possible to write the code manually, numerous programming libraries—both free and proprietary—contain prewritten code in a number of programming languages that can be used to automate the task.

The programmer defines search criteria that tells the code what to look for. The code then communicates with the targeted data source by sending countless requests for data, interpreting the source’s response, and meticulously sifting through those responses to pick out the data that meets the criteria. Results can include databases, spreadsheets, or plain text files, for example, which can all be further cleaned for analysis.

Every website or data source is structured differently. While some web scrapers are capable of navigating a wide variety of layouts automatically, being unprepared could cause them to improperly scrape or miss some data, leading to incomplete or inaccurate sets. A web inspector tool can map and navigate all the parts and resources of a web page, including its HTML code, JavaScript elements, and web applications, better preparing the web scraper for what it will encounter.

Popular Data Scraping Techniques

Data can be scraped in more than one way—while no technique is outright better than another, each tends to work best in the specific scenario for which it was designed. Here’s a look at some of the most popular data scraping techniques.

API Access

Application programming interfaces, or APIs, are considered direct bridges between online websites or applications and outside communicators. Many websites with high-density data offer free or paid access to their own, integrated APIs, letting them provide data access while controlling how and how often site data is scraped.

If a website or application has an API, it’s best to use it over any alternative scraping method. API access  ensures consistency and reduces the risk of violating the website’s terms of service (ToS). This is particularly important when scraping user-generated data on social media platforms, as some of it may be protected under personal information privacy laws and regulations.

DOM Parsing

Document object model (DOM) parsing provides a hierarchical representation of a web page’s data and structure. There’s a tool for this—DOM Parser is a JavaScript library capable of parsing XML and HTML documents by navigating and mapping a web page as a hierarchical, tree-like structure to locate the most important elements.

DOM parsing makes it possible to more interactively select which elements to scrape using class names, IDs, or nested relationships. It also ensures that the relations and dynamics between the various data points aren’t lost in the extraction process.

HTML Parsing

In HTML parsing, the data scraping tool reads the target web page’s source code, usually written in HTML, and extracts specific data elements that might not otherwise be accessible using another technique—for example, distinguishing data based on tags, classes, and attributes.

HTML parsing enables users to more easily navigate the complex structure of a website, granting access to as much data as possible and ensuring precise and reliable extraction.

Vertical Aggregation

Vertical aggregation is a specialized type of data scraping that works as a more comprehensive approach across various websites and platforms in the same niche. Instead of scraping a wide set of data once, vertical aggregation lets you focus data scraping efforts over a set period of time.

For example, vertical aggregation could be used to scrape job listings from different employment sites or the change in prices and discounts on e-commerce sites. The collected data is up-to-date and best used to support decision-making processes in niche-specific data fields.

Data Scraping Use Cases

Accurate, up-to-date data is a goldmine of knowledge and information for enterprises. Depending upon how it was processed and analyzed, it can be used for a wide range of purposes. Here are some of the most common business use cases for data scraping.

Brand, Product, and Price Monitoring

For businesses that want to keep watch over their brand and products online as well as their competitors’ brands and products, data scraping provides a high-volume means of monitoring everything from social media mentions to promotions and pricing information. Using data scraping to gather up-to-the-minute data allows them to adjust and adapt strategies in real time.

Consumer Sentiment Analysis

The success of products and services can hinge on consumer perceptions. By scraping reviews, comments, and discussions from online review sites and platforms, businesses can gauge the pulse of the consumer. Aggregating this data paints a clearer picture of overall sentiment—positive, neutral, or negative—to assist companies in refining their offerings, addressing concerns, and amplifying strengths. It acts as a feedback loop, helping brands maintain their reputation and cater better to their consumer base.

Lead Generation

Automating the extraction of data and insights from professional networks, directories, and industry-specific websites gives businesses a valuable way to find clients and customers online. This proactive approach facilitates outreach by giving sales and marketing teams a head start. Scraping massive amounts of data and running it through an analytical model enables businesses to connect with the right prospects more efficiently than manually searching potential leads.

Market Research

Having up-to-date and relevant data is paramount to successful marketing. Data scraping lets businesses collect vast amounts of data about competitors, market trends, and consumer preferences. When cleaned, processed, and analyzed for patterns and trends, data can provide insights that drive marketing campaigns and strategies by identifying gaps in the market and predicting upcoming trends.

Legal and Ethical Considerations of Data Scraping

Data scraping is a broad term that encompasses a lot of different techniques and use cases with varying intent. In the U.S., it’s generally legal to scrape publicly available data such as job postings, reviews, and social media posts, for example.

Scraping personal data may conflict with regional or jurisdictional regulations like the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act. The definition of personal information may vary depending on the policy. The GDPR, for example, forbids the scraping of all personal data, while the CCPA only prohibits non-publicly available data—anything made available by the government is not covered.

As a general rule, it’s a good idea to be cautious when scraping personal data, intellectual property, or confidential information. In addition, some websites explicitly state that they don’t allow data scraping.

There are also ethical concerns about the effects of data scraping. For example, sending too many automated requests to a particular website using a data scraping tool could slow or crash the site. It could also be misconstrued or flagged as a distributed denial of service (DDOS) attack, an intentional and malicious effort to halt or disrupt traffic to a site.

Data Scraping Mitigation

Websites can employ a variety of measures to protect themselves from unauthorized data scraping outside of their dedicated API. Some of the most common include the following:

• Rate limiting—limiting certain types of network traffic to reduce strain and prevent bot activity.
• CAPTCHAs—requiring users to complete an automated test to “prove” they are a human visitor.
• Robots.txt—a text file containing instructions defining what content bots and crawlers can use and what’s off limits.
• Intelligent traffic monitoring—using automated tools to monitor traffic for tell-tale bot patterns and behaviors.
• User-agent analysis—monitoring any software that tries to retrieve content from a website and preventing suspected scraping tools.
• Required authentication—not allowing access to any unauthorized user or software.
• Dynamic website content—web content that changes based on user behavior that can recognize and block scraping tools.

Data Scraping Tools

Data scraping tools consist of code written in a range of programming languages. Python is the most popular language for this purpose, because of its ease of use, dynamic type language and accessible syntax, and community support. It also offers a number of data scraping libraries. Other popular languages for data scraping include JavaScript and R. Here are a few of the most commonly used data scraping tools.

Beautiful Soup

Beautiful Soup is a Python library of prepackaged open-source code that parses HTML and XML documents to extract information. It’s been around since 2004 and provides a few simple methods as well as automatic encoding options.

Scrapy

Scrapy is another free, open-source Python framework for performing complex web scraping and crawling tasks. It can be used to extract structured data for a wide range of uses, and can be used for either web scraping or API scraping.

Octoparse

Octoparse is a free, cloud-based web scraping tool. It provides a point-and-click interface for data extraction, allowing even non-programmers to efficiently scrape data from a wide range of sites, and uses an advanced machine learning algorithm to locate data.

Parsehub

Parsehub is a cloud-based app that provides an easy-to-use graphical user interface, making it possible for non-programmers to use it intuitively to find the data they want. There’s a free version with limits. The standard version is $149 per month, and the Professional version costs $499 per month.

Data Scraping vs. Data Crawling

Data scraping and data crawling both concern the extraction of information from websites. Data scraping focuses on extracting specific information from numerous web pages on various sites. Data crawling is a broader process, primarily used by search engines.

Web crawlers, also referred to as spiders, systematically scour the web to collect information about each website and web page rather than the information contained within the pages themselves. This information is then indexed for search engine and archival purposes.

Bottom Line: What is Data Scraping?

Data analytics is increasingly critical for businesses looking for a competitive advantage, more streamlined operations, better business intelligence, and data-driven decision-making. At the same time, we’re producing more data than ever before—from online shopping to social media, information about behaviors, interests, and preferences is widely available to anyone who knows where to look.

Data scraping is a way for enterprises to use automated tools to cast a wide net that gathers massive volumes of data that meets the specifications they define. It’s useful for a wide range or purposes, and prebuilt code libraries serve as easy-to-use data scraping tools that make the process feasible for even non-technical users.

Because data scraping can involve personal information, there are legal and ethical concerns. Any enterprise data scraping effort should take regional and jurisdictional regulations into account, and should be reviewed on an ongoing basis to keep pace with changing policies.

Learn more about the pros and cons of big data, of which data scraping is just one component. 

• Best for data visualizations and dashboards: Tableau
• Best for Microsoft application users: Microsoft Power BI
• Best for data modeling: Looker
• Best for collaboration: SAS Viya
• Best for SMBs and smaller budgets: Zoho Analytics
• Best for data integration: Sisense
• Best for discovering insights: Qlik

Read on to find more details about each of our picks, how they compare on features and price, and how we chose them.

Comparing the Top Data Analytics Tools

Our review of data analytics software looked at how each platform handles the core functions needed by most enterprise users: data analytics tasks, automation and advanced analytics, security, customer support, and pricing.

Jump to:

• Key Features of Data Analytics Software
• Considerations for Purchasing Data Analytics Tools
• Common Types of Data Analytics Tools
• Methodology
• Bottom Line: Top Data Analytics Tools and Software

Consumers generate a wealth of data about their preferences, experiences, and activities every day that is captured through website visits, online reviews, surveys, customer loyalty programs, and more. Businesses can use this data to better understand their customers and tailor products and services to the ever-changing marketplace. In order to do so, they need to be able to collect, analyze and use this vast amount of data.

The data analytics marketplace is crowded with a wide range of systems promising an even wider range of features. To help narrow your choices, here are our top seven data analytics tools geared toward enterprise users and requirements.

Tableau

Best for data visualizations and dashboards

Tableau has continued to refine and diversify its wide variety of visualizations while releasing new connectors to additional data sources since its acquisition by Salesforce in 2019. The ease of adding new data sets to the platform, along with automated insights and user-friendly chart and dashboard creation tools makes it a top contender in the business intelligence (BI) marketplace.

Tableau comes in two versions: Tableau Cloud, a cloud-based SaaS (Software as a Service) tool; and Tableau Server, which allows companies to host the software within their own IT structure.

Key Features

• Drag-and-drop chart, data visualization and dashboard creation
• More than 30 connectors to a variety of data sources
• Marketplace of pre-built dashboard extensions
• Self-service data preparation
• Augmented analytics powered by AI/ML, including automated modeling and natural language queries
• Add-ons for data management, embedded analytics and more

Pros

• Easy to create visualizations and dashboards
• Pre-built connections to a wide variety of data sources, including big data platforms

Cons

• No automatic or scheduled report data refreshes
• Breadth of features mean it can be overwhelming for new users

Pricing

• Pricing is per user, per month, based on certain types of users. At least one Creator license ($75 per user per month) is required for every deployment.
• Explorer licenses ($42 per user per month) allow for self-service analytics and Viewer licenses ($15 per user per month) allow users to interact with dashboards and visualizations
• 14-day free trial available

Microsoft Power BI

Best for Microsoft application users

The user experience for Microsoft Power BI will feel somewhat familiar to many business users on the Microsoft software ecosystem. Sharing data and reports between MIcrosoft Power BI and Office 365 applications is seamless, and it is also easy to leverage the power of Azure to scale data analytics operations as business grows.

Key Features

• Customized dashboards and interactive visualizations
• Access to hundreds of data connectors
• Self-service data preparation and modeling tools for Premium package subscribers
• AI-powered augmented analytics for Premium package subscribers
• Natural-language search

Pros

• Deep integration with other Microsoft products
• Familiar, user-friendly navigation and controls
• Hundreds of pre-built visualizations available on Microsoft AppSource
• Low-cost solution for smaller businesses

Cons

• Add-ons required for data sharing and working with larger datasets
• Data refresh limitations with lower-priced packages

Pricing

• Pricing is per user, per month, depending on package selected
• Power BI Pro is $10 per user, per month
• Power BI Premium is $20 per user, per month
• Basic Power BI access is free

Looker

Best for data modeling

Google Looker is a browser-based data analytics tool built on the Google Cloud infrastructure. A full-featured platform, Looker’s proprietary LookML modeling language provides a flexible approach to building complex data models. LookML allows users to create reusable code blocks and data models, streamlining the modeling process. Its integration with Google Looker Studio allows users to create and share a wide variety of dashboards and visualizations.

Key Features

• LookML modeling language, which abstracts SQL queries
• Dashboards and visualizations via an integration with Looker Studio
• Predictive modeling and machine learning

Pros

• Can build complex data models
• Integrations with numerous data sources
• Advanced data governance and security features

Cons

• High price tag
• Learning curve for users who are not experienced with LookML or SQL
• Reported slowness when working with large data sets

Pricing

• Three pricing tiers corresponding to the number of API calls per month
• Standard edition starts at $5,000 per month and includes one production instance, 10 Standard Users, 2 Developer Users and up to 1,000 API calls per month
• 30-day free trial available

SAS Viya

Best for collaboration

SAS Viya is a cloud-native data analytics platform that leverages SAS’s industry-leading analytic expertise and the power and security of Azure. The platform is highly scalable and offers numerous integration with other tools and systems to extend its functionality.

Key Features

• Data preparation and cataloging
• Model deployment and monitoring
• Conversational AI

Pros

• Cloud-native architecture allows access to data from anywhere
• Easy to share and collaborate on dashboards and visualizations
• Can handle large volumes of data

Cons

• Cost
• Limited support options and training

Pricing

• The vendor does not make specific pricing available—contact sales directly
• Customer reviews mention that the software is expensive

Zoho Analytics

Best for small and budget-conscious businesses

Zoho Analytics is an easy-to-use self-help analytics platform with pricing that won’t break the bank for SMBs and enterprises with lower data volumes. It provides a modular approach to data analytics, so that it can grow as data programs mature. The tool has a range of data connectors and integration options and can connect with both cloud-based and on-premises data sources. Some of the more technical data analytics features require a Premium subscription or add-on software.

Key Features

• Integrations with more than 250 data sources
• Data preparation and management via the Zoho DataPrep app (available with Premium subscriptions)
• Collaboration tools for reports
• Ask Zia natural language queries

Pros

• User-friendly dashboard and report creation
• Scalable and modular solution that can grow with your business

Cons

• Limited data visualization options
• Online reviews say that training videos and resources could be improved

Pricing

• Standard plan is $48 per month for five users, with additional users billed at $6.40 per user per month
• Premium plan is $115 per month and includes 15 users
• Enterprise plan is $455 per month and includes 50 users
• 15-day free trial available

Sisense

Best for data integration

Sisense is a BI tool that brings together disparate sources of data to create useful interactive dashboards and visualizations that can be drilled down into to expose actionable insights. These reports can then be embedded in a wide range of applications. Sisense is built as a cloud-native product but can also be run on-premises or as a hybrid solution.

Key Features

• Data blending tools to merge data from disparate sources
• Interactive dashboards and visualizations
• AI-driven insights
• Embeddable in a wide variety of applications

Pros

• Connects to a wide variety of data sources
• Drill-down charts and dashboards
• Easy to embed and share visualizations

Cons

• Reports of performance issues when working with extremely large data sets
• Significant learning curve for new users

Pricing

• Pricing is not available, as each deployment is a custom quote based on a company’s requirements
• Online sources note that it costs around $25,000 per year
• Free trial available

Qlik

Best for discovering insights

Qlik is a data analytics and BI platform that leverages an AI-powered associative engine when importing data from different sources to speed up the process of combining the data and surface new actionable insights.

Key Features

• ML-powered augmented analytics with intelligent alerts
• Interactive charts and visualizations
• Mobile-first design with iOS and Android apps

Pros

• Associative engine streamlines data processing
• Automated workflows

Cons

• Can get expensive quickly when you need to scale up the number of users
• Mobile-optimized design means some charts are lower resolution

Pricing

• Standard starts at $20 per user, per month with a minimum of 10 users
• Premium starts at $2,700 per month
• Free trial available for the Standard plan

Key Features of Data Analytics Software

The market of data analytics platforms is broad, and there are tools to fit just about every business need. While they each have their own strengths and weaknesses, there are a number of key features that each needs to meet.

Data Management

Data management refers to the process of gathering, storing, securing and using data within an analytics platform. It is helpful for the software to be able to bring in and use different types of data from a variety of sources.

Data Preparation

Data preparation is the process of cleaning and reformatting data before it is used for analysis. The quality of your data directly impacts the quality of your analysis.

Data Modeling

In data modeling, a platform helps define the different types of data you collect and spells out the relationships between these disparate data sets.

Data Governance

Data governance helps insure the quality and security of your data through a set of defined standards and policies. Data analytics platforms help data governance efforts by automating and monitoring these standards and policies.

Dashboards and Visualizations

Dashboards and visualizations are the most useful tools for communicating the insights uncovered in your data with non-technical business users. Dashboards help businesses monitor their key performance indicators and other important metrics, while visualizations can help convey important insights at a glance.

Scalability

As a business grows, so too does the amount of data it collects and needs to analyze. That’s why scalable data analytics platforms are important, so they can grow with your business as your data grows.

Security

Much of the data businesses collect is sensitive or proprietary, and therefore data analytics platforms need to have robust security controls to prevent theft or misuse of their data. This includes features such as granular access controls, end-to-end encryption and logging and audit tools.

Considerations for Purchasing Data Analytics Tools

When selecting a data analytics tool, the primary consideration is how you will use it. Do you need it to help you optimize your marketing or strategic decision making? Do you need it for research or mathematical analysis? Do you need to track sensitive information, like healthcare data? Certain data analytics tools specialize in specific industries or focus on certain types of data analysis. Once you’ve identified your need, there are other considerations to help you narrow down your choices.

Cost

Since many data analytics tools are priced based on the number of users or the amount of data in your platform, it helps to develop realistic requirements of just who needs to use the system, what they need to do and how much data you will be analyzing.

Ease of Use

The usability of platforms varies widely. Some systems require deep expertise in data manipulation or knowledge of certain programming languages to get the most out of them, while others are designed as low- or no-code solutions.

Natural language processing in some systems allows users to explore data through conversational queries. It is helpful to understand the types of users who need access to the data in your platform and their level of technical expertise.

Data Handling

Platforms also differ in their ability to handle large data sets or to ingest data from a variety of sources. If your data is spread across multiple systems, look for a platform with a wide variety of data connectors. If your business deals with big data, be sure to read reviews of the software you are considering to see if there are any performance issues when dealing with large data sets.

Data Preparation

Data analytics software varies in its ability to tackle data preparation tasks. Some software requires add-ons or integrations to clean and prepare data, while others are able to do these tasks natively. As your data grows more complex, you will need to look for a platform that can handle a variety of data models and easily manage complex models.

Data Governance

Similarly, as the complexity and volume of data grows, look for a platform that includes data governance functions that let you implement policies and practices and monitor your data to ensure compliance.

Customer Support and Education

Depending on the expertise of your staff, you should also look into each platform’s support and training availability. Some platforms have steep learning curves for all but the most technical staff. Training options are particularly helpful if you want the platform to be used effectively by a wide range of stakeholders.

Security

If you collect sensitive data or data that is governed by privacy laws, security is of the utmost importance to your company. That is why you should look at the security features of each platform, such as encryption, user access controls, what types of system activities are logged and data privacy features.

Desktop vs. Cloud

Data analytics platforms often can be hosted in a variety of environments, from the cloud to on-premises to hybrid, but many of the tools have differences in how they function on a desktop versus in a cloud environment.

In addition, while cloud-hosted services do offer enterprise-level security, you may still need to have an on-premises-hosted solution, depending on the data you collect or the way your IT is governed. You also need to think about your maintenance and support resources when making a hosting decision, as well as your backup and recovery capabilities.

What are Common Types of Data Analytics Tools?

There are numerous types of data analytics tools on the market and the boundaries between them can be somewhat blurry, as many of them can be used for a variety of industries and tasks. Examples of how some companies are using these tools to transform their operations and services can easily be found online.

Business Intelligence 

Many of the data analytics tools on the market are business intelligence tools, focused on bringing together your data for the purpose of better understanding your market and for making strategic business decisions. Business intelligence tools can also be used in education, healthcare and IT settings to optimize service delivery to students, patients and clients. Most business intelligence platforms have robust dashboards and data visualization capabilities so that the data can be accessed and understood by a wide variety of stakeholders.

Data Science

Similar to business intelligence platforms, data science platforms are often used by companies looking to transform their data. Many of the new data science platforms use machine learning to develop models for activities like predictive analytics.

Statistical Analysis

Researchers, economists, scientists, students and engineers need workhorse platforms to do deep statistical analysis of data. These programs include popular statistical analysis models, like regressions and Bayesian procedures.

Collaboration

While other types of data analytics platforms facilitate collaboration between users, there are some products on the market specifically designed for data scientists to collaborate with each other. These platforms allow users to share notebooks, code, and data to help with workflows.

Methodology: How We Chose These Picks

Datamation does not rank recommended solutions based on any vendor partnerships, only on features and capabilities. We may also analyze comprehensive user reviews and the vendors’ own data on their products to determine whether we think that product is a good fit for our audience.

We considered six key composite criteria when evaluating data analytics solutions: pricing, core features, support, dashboards and visualizations, augmented analytics, and security.  We then analyzed the products using a weighted scoring rubric. Our scoring system ranks products from 0 to 5, and the best solutions are chosen from that short list. Percentages represent the weight of the total score for each product.

Core Features | 20 Percent

The core features of a data analytics platform include data management, data processing, data preparation, data modeling, data governance, collaboration and scalability.

Pricing | 20 Percent

In evaluating the top seven data analytics solutions, pricing considerations include the advertised cost, the price of add-ons and options, available pricing tiers, any upgrades or discounts, and whether a free tier and/or trial version is available.

Support | 10 Percent

The support evaluation included the availability of training and onboarding, phone and online support, a knowledge base, a user community and videos and webinars.

Dashboards and Visualizations | 20 Percent

We looked at how the systems handled dashboard creation and data visualizations, including whether they could be creating using drag-and-drop functionality, as well as the ability to interact with the data via natural language processing.

Augmented Analytics | 10 Percent

The number of solutions using artificial intelligence and machine learning to optimize their analysis is growing, so we looked at how systems used these tools, whether they could handle predictive analytics and whether they could surface automated insights from the data.

Security | 20 Percent

We looked at data access controls, logging and auditing, encryption, how the systems handle data masking, the results of security audits (SOC 1 Type 2, SOC 2 Type 2, ISO 27001), and whether they facilitate GDPR and CCPA compliance.

Bottom Line: Top Data Analytics Tools and Software

The universe of data analytics platforms is vast and diverse, which means that there are numerous tools available that fit your company’s individual needs. The seven platforms here all provide strong analytics tools for working with a wide range of data, and all excel in different ways.

Read next: The 10 Best Dashboard Software Solutions of 2023

What are IoT Protocols and Standards?

IoT protocols are established rules about how IoT devices should work and communicate. Standards are similar to protocols, but are used more widely—across an entire industry, for example. Together they ensure that all IoT devices have a minimum level of compatibility with one another and with other related devices and applications.

For instance, a manufacturer might use two different IoT sensors from different brands. As long as both companies follow the same guidelines, the sensors will work on the same network. IoT protocols and standards typically function in a single layer as a distinct part of a larger network—most commonly in the application and middleware layers of a standard five-layer network architecture, although not exclusively. For example, Bluetooth and Wi-Fi operate on the network layer.

Commercial IoT Standards and Protocols

Commercial IoT is a huge, still-growing industry. Interest in smart home tech is creating high demand for devices in the consumer electronics market. As a result, protocols and standards are emerging to ensure consumers get a streamlined, user-friendly experience. While some of these standards are also used in industrial applications, their biggest benefits stand out most in commercial settings. A few commercial IoT standards and protocols are so widely used they have become ubiquitous—like Bluetooth and Wi-Fi, for example.

Bluetooth

It’s hard to imagine consumer electronics today without the Bluetooth standard for wireless device-to-device communication. Every new smartphone, tablet, and laptop includes Bluetooth support as a standard feature.

Bluetooth was one of the first IoT communication protocols to open the door for a boom in consumer IoT devices, such as smartwatches and wireless headphones. It uses wireless personal area networks (WPANs), allowing for short-range data transmission using radio waves.

Bluetooth was originally standardized by the world’s largest technical professional organization, the IEEE, in 2005 under standard IEEE 802.15.1. Though updates ceased in 2018, Bluetooth remains an extremely popular IoT protocol—particularly among consumer electronics.

Data Distribution Service (DDS)

The Data Distribution Service (DDS) protocol and standard is designed for communication across hardware and software platforms. Its main benefits include easy scalability, high reliability, and low-latency connectivity. DDS is great for ensuring all the IoT components in a system can maintain high-quality data transfers.

DDS is popular across commercial and industrial IoT applications. Originally published in 2004 by the Object Management Group, which maintains it today, it is a middleware protocol for standardizing machine-to-machine communication using the publisher-subscriber model.

Matter

Matter is a communication and interoperability standard designed to address the issue of smart home device communication between brands. Many commercial device manufacturers want consumers to buy all their smart home devices from one brand. This isn’t necessarily in the consumer’s best interest, but poor communication between products from different companies may force them to pick a single brand.

Matter ensures that smart home devices from participating manufacturers work together natively. It benefits both manufacturers and consumers. Since companies don’t have to be a one-stop shop, they can instead focus on making great smart thermostats, for example, without worrying about losing money to a competing brand that also makes other products.

Wi-Fi

Wi-Fi is among the oldest IoT standards and one of today’s most well-known and widely used. Its invention dates back to 1942, when actress and inventor Hedy Lamarr patented frequency hopping. It evolved over the decades until the first WiFi standard was created in 1997.

This first set of standards established the Wi-Fi we know today. The IEEE 802.11 family of standards outlines how communication over wireless local area networks (WLANs) should work. It also establishes a minimum data transfer speed of 2 megabytes per second. The IEEE continues to maintain the 802.11 standards, and Wi-Fi is still found in most consumer electronics and commercial IoT devices, such as smart home appliances and sensors.

XMPP

Extensible Messaging and Presence Protocol (XMPP) was originally developed for human-to-human communication in 2002. In the 20-plus years since, it has evolved into a machine-to-machine communication protocol popularly used by smart appliances.

Today, XMPP is an open-source protocol maintained by the XMPP Standards Foundation. It’s a lightweight middleware system that standardizes communication and XML data. XMPP runs in the application layer, where it can provide near-real-time data transfers. This responsiveness, combined with XMPP’s high accessibility, makes it ideal for communicating with smart home devices like appliances.

Industrial IoT Standards and Protocols

The industrial IoT market is among the strongest-performing in the world, which should come as no surprise given the countless applications of IoT in manufacturing, logistics, and construction. Industrial IoT (IIoT) is considered its own distinct niche.

IIoT standards and protocols are becoming increasingly important as businesses grow to rely on their IoT devices more. For instance, a manufacturer in a smart factory might use IIoT sensors to send maintenance alerts, which could affect employee safety. IoT communication standards ensure sensors send real-time alerts successfully, regardless of the brand or model.

Constrained Application Protocol (CoAP)

Constrained Application Protocol, or CoAP, is a protocol that allows IoT devices to use HTTP without excessive power consumption. Launched in 2013, it’s  popular for machine-to-machine (M2M) communication—particularly in industrial applications like supply chain environments.

CoAP lets industrial users include a wider variety of IoT devices in their networks without being restricted by low power capabilities or bandwidth. Its main drawback is a lack of security features. CoAP is somewhat exposed on its own and needs the additional datagram transport layer security (DTLS) protocol to ensure secure data transmission.

Lightweight M2M (LWM2M)

Lightweight M2M, or LWM2M, is a protocol specifically for remote device management in IoT or machine-to-machine environments. It is purpose-built for IoT sensors, making it a highly useful protocol for industrial applications. Its light weight means it doesn’t require much power, storage, or computing resources to run.

LWM2M was originally published in 2017 and is still active and maintained by OMA SpecWorks. The 2020 update to the protocol added compatibility with edge networking and 5G, making LWM2M a cutting-edge standard for today’s industrial environment. LWM2M works over TCP/TLS, MPTT, and HTTP.

MQTT

MQTT is an application-layer protocol for machine-to-machine communication using the publisher-subscriber model. It was developed in 1999 and is a popular open-source protocol for standardizing communication between industrial IoT devices.

MQTT is particularly well-suited for IIoT sensors due to its lightweight nature and tolerance for low bandwidth. Since it doesn’t require much memory space, MQTT is highly compatible with the full range of IIoT devices. It essentially acts as a bridge to applications.

Zigbee

Zigbee is a highly popular network protocol specifically for mesh networks used in automation. Consumer and industrial devices use Zigbee, although its emphasis on automation and various applications makes it ideal for business. It was developed by the Connectivity Standards Alliance, which also created Matter.

Zigbee’s top benefits include low power consumption and a high degree of flexibility. It’s designed for short range, similar to Bluetooth. One feature that’s particularly beneficial in the industrial space is its high level of security. Zigbee includes encryption and authentication by default while staying lightweight. This means industrial users can build a mesh network of IoT devices with security features without using excessive power and computing resources.

Security IoT Standards and Protocols

Cybersecurity standards have always played an important role in the IoT’s development and growth. Some communication-related protocols include security features, but this isn’t always the case. A growing pool of IoT protocols and standards is designed to emphasize cybersecurity. Some of these are add-on rulesets for other offerings—for instance, Wi-Fi Protected Access 2 is one of today’s leading network security protocols to add to Wi-Fi.

Ascon (NIST)

Ascon is the National Institute of Standards and Technology’s (NIST) official standard for IoT encryption, selected in 2023. It is now the formal standard in the U.S. for securing IoT devices and communications.

Ascon is a collection of cryptographic algorithms that provide highly secure encryption without requiring high amounts of power and computing. Implementing Ascon can help IoT device manufacturers be more proactive about preventing cyberattacks and vulnerabilities rather than just responding to them.

DTLS

Datagram Transport Layer Security, or DTLS, is a security protocol for encrypted communications. A datagram is a standard data transfer unit, such as a single message—they are commonly used in gaming, streamed video, or videoconferencing applications.

Designed by the Internet Engineering Task Force, DTLS secures wireless communications so senders and receivers know their messages won’t be intercepted or spied on. It’s a commonly used protocol across commercial and industrial spaces.

Z-Wave

Z-Wave is a proprietary alternative to protocols like Bluetooth and Wi-Fi designed for encrypted mesh network communications, offering more security than its open-source counterparts. It functions on various low-level radio frequencies.

Z-Wave is popular among smart home automation systems, particularly those focusing on security. It is primarily used in consumer electronics and commercial applications but can also be used in industrial environments.

Bottom Line: Understanding IoT Protocols and Standards

IoT devices are a common part of people’s lives. They’re in our homes, our doctors’ offices, our oceans and skies, and businesses increasingly rely on them for a wide range of purposes. Day in and day out, these devices generate massive volumes of data used for business intelligence, competitive analysis, more efficient manufacturing, consumer feedback, and more. Dozens of protocols and standards run in the background to ensure that these devices and sensors work smoothly and securely and can communicate with each other effectively—understanding these protocols can help enterprises make better purchase decisions and build more secure, robust IoT networks.

Read next: Top 7 IoT Analytics Platforms

This article explores the benefits and transformative potential of digital transformation across various industries.

What is Digital Transformation?

Digital transformation will look different for every company. It’s part technology initiative and part culture change, with a heavy dose of workflow re-imagining thrown in.

For example, if a company started with digitization—the term for transferring paper and analog data to a digital format without making any changes to business processes—it might then realize that the convenience of their new format could be matched with new ways of doing business. That moment in a digital transition is when digital transformation happens.

Here’s a good way to think about it: digital transformation is creating fundamental changes in how a business operates and the value it provides customers by introducing technology into all areas of its work.

Thinking beyond going paperless, digital transformation could mean automating processes to create efficiencies and improve accuracy, or integrating all new customer relationship management and finance and accounting systems across an organization. Digital transformation is a broad term encompassing many aspects of how businesses implement technologies and the processes around them.

The goal is not the technology itself, but the improvements it can bring to how the work is done and the value it brings to the organization and its customers—an important distinction.

Learn more: 5 Digital Transformation Examples

Enterprise Digital Transformation Through AI

Larger enterprises have engaged with digital transformation practices for years, watching the latest technology trends and brainstorming how new developments could advance their business model. One transformative advancement for both industry and consumers has come in the form of streaming or multimedia that users can access at any time via the internet. Major companies in the television, film, gaming, and music industries are some of the biggest trailblazers in the digital transformation world.

Spotify is an example of constant digital transformation. The company’s goal is to create the most personalized user experience (UX) possible for listeners, and it has consistently done that through data analytics, artificial intelligence (AI), and machine learning.

Here are just a couple examples of how the company has used new technologies to tailor customer experiences (CX):

Made For You Playlists

Based on a user’s behavioral trends and previous listens, Spotify creates “made for you” playlists to match the different artists, genres, and tempos that it believes they will like most. It does this through a process called collaborative filtering, Spotify compares both qualitative and quantitative data from different users with similar listening patterns and makes recommendations to each based on the habits of those similar listeners.

Release Radar

This Spotify feature alerts users to new music from artists they already love or artists Spotify thinks they will love. Once again, Spotify uses collaborative filtering, but also natural language processing to find out what you like and listen in on the online chatter about recent releases. If a new album is considered high quality and matches a certain genre, it may end up in your release queue. This is also great exposure for new artists who match the tastes of Spotify listeners.

Streaming is a prime example of how digital transformation happens at the enterprise level, and it is one of the clearest to the millions of users who frequently engage with streaming technologies.

Digital Transformation In Customer Service And Engagement

Companies of varying sizes and industries have adjusted their customer service approach to meet customers virtually and in real-time. With a growing number of available social media platforms, smart applications, and simple website development and management tools, many organizations are using these resources to digitally transform the way they talk to customers—here are a few examples.

Social Media

Social media has served as a primary tool for connecting with other people virtually, but it continues to expand into customer service and e-commerce. Some of the examples of digital transformation that most major companies have implemented on their social media channels include the following:

• Answering customer questions and handling complaints in the comments of social media posts
• Posting challenges, events, and discounts for customers
• Selling products through native e-commerce features on Instagram and Facebook
• Finding other fun ways to start conversations with customers and other companies.

Chatbots

Chatbots are common website add-ons that companies use to digitally transform customer service. Bypassing traditional contact center models, chatbots help customers at any time of day with a pre-programmed bot that answers their questions or helps them navigate the website. Chatbots also allow customers to self-segment their personal data for your company’s database, making their experience more personalized and giving you more insight into your customer base.

Self-Help Knowledge Bases

Self-help knowledge bases provide another way to bypass traditional contact center models. By creating an FAQ library with short articles and multimedia resources, both your customers and your employees can research their questions. This self-service option lessens the strain on customer service representatives. Several major customer relationship management (CRM) platforms, such as HubSpot and Salesforce, enable this feature through service hubs.

Learn more: How to Use a Knowledge Management System to Improve Customer Service

Digital Transformation in the COVID-19 Era

The COVID-19 pandemic forced a lot of digital transformation upon a lot of companies. The widespread lockdowns and stay-at-home orders drove new uses of tools and technologies for business to maintain customer relationships in the chaos. Here are a few ways we saw digital transformation save industries and institutions from total collapse during the pandemic:

Widespread Use Of Video Conferencing Platforms

Stay-at-home orders sent non-essential employees away from the office to work from home, which completely disrupted the traditional in-office meeting workflow. Video conferencing platforms already had a place in many businesses, but during the pandemic, they became a staple that transformed the way we meet with colleagues. You can share your screen to guide someone through a process, type questions into a chat while others are presenting, and even host quick video meetings for mental health checks with faraway teammates. The widespread adoption of this technology has changed the way we work, enabling the rise of remote work.

Virtual conferencing platforms also gained major traction, considering you can’t host industry speaking conferences if no one is allowed to travel. Many hosting organizations launched virtual event platforms to keep their events going during COVID-19, hosting live and pre-recorded speaker events where people could pay for digital access.

Past conferences involved extensive event planning, catering, hotels, and air-travel fees, but through this forced digital transformation, many organizations are discovering that you don’t need a physical venue to host an informational event.

E-Learning

The way students learn and teachers teach has changed dramatically during the pandemic. Some students are learning virtually, some are in person, and many are learning in a hybrid model. This shift in the learning experience required several points of digital transformation, such as:

• Using learning management systems (LMS) to assign and turn in homework and tests
• Launching video conferencing platforms for teacher lectures and student presentations
• Ensuring students have internet access and computers or tablets that can securely connect to the school’s network

Many students are heading back to in-person learning. However, due to the digital transformation school systems had to make during the pandemic, classrooms will likely incorporate more virtual learning elements into lesson plans, particularly for students who struggle with accessibility in a traditional classroom environment.

Virtual Dining Options

Restaurants were some of the businesses hit hardest by pandemic restrictions, but many of them pivoted and maintained a revenue stream through digital transformation. Restaurants expanded their carry-out and delivery options, partnering with food delivery applications and expanding their customer-facing order applications.

As in-person dining slowly returned, many restaurants found a new form of digital transformation necessary. In order to comply with health department standards and to stay open, they began the digitalization of their menus. This move requires customers to scan a QR code and view the menu on their personal device, instead of receiving a physical copy. As COVID restrictions are fully lifted, these digital transformations may decrease in popularity, but many are likely to stick around because of their convenience for both restaurant staff and customers.

Learn more: The Primary Reasons Digital Transformation Efforts Fail

Bottom Line: Digital Transformation

Digital transformation is a means for businesses to add value to the products and services they provide customers by changing not just the technology they use, but their processes and ways of thinking about how to use that technology. It’s less about implementation than it is about effecting cultural change within the company. Enterprises interested in exploring new avenues of digital transformation can engage with consulting firms to help identify opportunities and redefine their processes, or they can start on a smaller scale with internal efforts. The only wrong way is to not explore digital transformation at all—the future is digital, and companies that fail to embrace it risk falling behind.

Read next: Top 7 Digital Transformation Companies

What Is a Hypervisor and How Does it Work?

Traditional computers run one operating system, or OS, at a time. This makes them more stable, as the hardware receives only limited processing requests, but it’s also limiting. A hypervisor is a type of software that enables multiple instances of operating systems to run on the same physical resources. These multiple instances are called virtual machines.

Hypervisors work by separating the host machine’s OS, software, and applications from the underlying physical hardware and resources, allowing for multiple “guest” operating systems to share the same hardware resources without being in connection or communication with one another.

Each guest OS operates as if it has all the host’s resources to itself. The hypervisor manages available resources so that each guest OS has reliable access to enough processing power, memory, and storage to function properly. It allocates resources according to the requirements of the guest system and the applications running on it, but also according to the virtual environment’s administrator settings.

The hypervisor also ensures that activities in one virtual environment do not affect others, maintaining the privacy, independence, and security of each virtual machine.

Benefits of Hypervisors

Cloud computing has driven a rapid growth in the hypervisor market. The following are some of the benefits of hypervisors.

• Cost-Effectiveness. Companies can save resources using hypervisors by reducing the need for hardware and physical storage space. Instead of running different applications on separate machines, a hypervisor allows for multiple virtual machines to operate on a single hardware platform, leading to significant cost savings.
• Efficiency and Scalability. Hypervisors increase efficiency and scalability by facilitating the migration of virtual machines and digital assets and operations between different host machines. This feature is especially beneficial in cloud computing, where resources need to be scaled up or down based on demand.
• Host Isolation. Hypervisors allow for the complete isolation of each virtual machine. This capability is crucial because if one virtual machine fails or gets compromised by outside malicious actors, the others remain unaffected, ensuring business continuity.

Types of Hypervisors

There are two ways to deploy hypervisor technology. The choice depends on the location of the hypervisors relative to the hardware resources and OS.

Type 1 Hypervisors

Type 1 hypervisors, also known as native or bare-metal hypervisors, run directly on the host machine’s hardware. This enables them to control the hardware and effectively manage guest systems. They allow for high performance and are often used in enterprise environments where efficiency and resource optimization is paramount.

Type 2 Hypervisors

Type 2 hypervisors, or hosted hypervisors, run atop a conventional OS just like other computer software. While less efficient than Type 1, they’re easier to set up and manage, making them more suitable for smaller environments or individual use.

Hypervisor Use Cases

There are multiple scenarios for using hypervisors. Here are a few of the most popular.

Server Consolidation

Hypervisors play a critical role in server consolidation, allowing companies to reduce their physical server count by moving workloads to virtual machines. This leads to fewer costs, energy consumption, and cooling needs. They can also improve performance and reduce necessary labor.

Testing and Development

Developers can use hypervisors to create isolated virtual environments for testing and development without needing additional hardware resources. By creating a virtual environment on the primary host, developers can simulate various conditions to test their latest software or applications at a fraction of the cost.

Virtual Desktop Infrastructure

Hypervisors support Virtual Desktop Infrastructure (VDI), allowing employees to access their work desktops remotely without the need to install and maintain a separate device per employee.

What are Cloud Hypervisors?

The backbone of modern cloud computing, cloud hypervisors enable the creation of multiple virtual machines, similar to multi-tenant architecture, on which cloud services run over an internet connection. The technology provides the scalability and flexibility that cloud services require to meet varying customer demands without the need to acquire and maintain numerous physical servers.

Cloud hypervisors are essential for businesses of all sizes, from small startups to large enterprises, as they offer an easy way to build and manage cloud-based applications and services for clients and staff.

Additionally, cloud hypervisors support the automated management of resources, reducing operational costs by allowing businesses to scale up or down based on demand. By using hypervisors to build their cloud environments, businesses can focus on their core business operations while enjoying the benefits of a flexible and secure cloud computing experience.

Security Considerations with Hypervisors

As with all connected technologies, hypervisors are subject to security risks. Here are a few of the main concerns.

Vulnerability to Attacks

As the controlling element of a virtual environment, a hypervisor can become a target for cyberattacks. It’s essential to keep all software updated with the latest security patches.

Isolation Failures

If a hypervisor fails to maintain isolation between virtual machines, it could lead to data leaks or breaches.

Unauthorized Access

Without proper access control and administration, a hypervisor can be manipulated to gain unauthorized access to virtual machines connected to the same host.

Hypervisors vs. Containers

While both hypervisors and containers enable software to run reliably when moved from one computing environment to another, they function differently.

Hypervisors virtualize host hardware to run multiple operating systems, while containers virtualize the OS to run multiple instances of the same application. The main difference between hypervisors and containers is that hypervisors are more isolated from each other, while containers are more lightweight and portable.

Generally, hypervisors tend to be best suited for larger applications that require more resources, while containers are best used for smaller applications or microservices. Containers also have the advantage of providing greater flexibility, allowing applications to be moved quickly and easily between different environments.

When choosing between a hypervisor and a container, consider the size and scope of the application as well as the security requirements.

Bottom Line: Hypervisors

Hypervisors play a vital role in virtualization, providing cost savings, flexibility, and scalability. Enterprises are increasingly turning to hypervisor technology to help create, manage, and use virtual machines for a growing range of uses. As they continue to evolve, they’re becoming more efficient and secure, with greater compatibility, and are moving toward lightweight solutions designed for specific tasks and work across different hardware platforms.

Read next: What is Multi-Tenant Architecture?

Table Of Contents

• What Are DevOps Tools?
• Types of DevOps Tools
  • Tools for Collaboration
  • Tools for Planning
  • Tools for Configuration Management
  • Tools for Automated Testing
  • Tools for Database DevOps
• 20 Popular Tools for DevOps
• Bottom Line: DevOps Tools

What Are DevOps Tools?

DevOps tools are software and apps that facilitate the practices and principles of DevOps, a collaborative approach combining software development and IT operations. DevOps covers the entire software development lifecycle, which usually includes seven repeated steps:

Planning. In this phase, teams set goals for projects and strategize how to meet them. Planning tools can assist with creating tables, timelines, and charts.

Coding. In the next phase, software code is written to the specifications laid out in the planning phase. Tools used in this phase can help with writing and testing code.

Constructing. In this phase, software developers introduce the code and the project is rebuilt to accommodate it as needed. Version control tools can be a big help in this phase as new code is called for, reviewed, approved, and uploaded.

Testing. In this phase, code is tested for bugs and performance—rather than “one and done,” this phase is repeated until the desired results are achieved. Automated tools can run 24/7 to provide a more complete and efficient testing regiment.

Deploying. In DevOps the entire team works to ready the project for production, unlike more traditional software environments where this phase would be strictly the domain of the operations team. Deployment dashboards and automated deployment tools can support this process.

Operating. In this phase, the development project is tested in a production environment, including user testing to provide critical feedback. Automation tools like Cucumber and Robot Framework can test for operation issues.

Monitoring. In this phase, observation provides insight into how well the project works, how it is used, and how it can be improved in future iterations. Server monitoring tools like Headspin can detect issues and send alerts.

Types of DevOps Tools

When thinking about your company’s DevOps workflow, look for opportunities to introduce tools that can help streamline efficiency, promote collaboration, automate testing, and help manage systems. Most DevOps tools fall into the five general categories outlined below.

Tools for Collaboration

Collaboration tools foster communication and teamwork and facilitate remote work and geographically dispersed teams. They can help teams communicate throughout the project lifecycle, facilitate file-sharing, and track employee processes.

Tools for Planning

Planning tools help coordinate and organize tasks, set and track timelines, plan workflows, and align project goals. They can keep teams on track throughout the project lifecycle, not just in the planning stage, and provide up-to-date information on deadlines, priorities, and goals.

Tools for Configuration Management

Configuration management tools process and maintain the consistency of a system’s performance, requirements, and design. This allows insight into the entire system throughout the software development life cycle.

Tools for Automated Testing

Automation testing tools provide a framework for developers to create and run tests consistently and repeatedly for more efficient and accurate results.

Database Tools

Database tools help companies gather, update, transfer, and store company data. These tools often streamline and automate processes to ensure functions are managed efficiently.

20 Popular DevOps Tools

While some tools are purpose-built for the DevOps lifecycle, tools built for a wider range of industries and environments can also be useful in this context. Here’s our roundup of 20 of the most popular tools for DevOps on the market today:

1. Slack

Slack is a cloud-based team collaboration tool built primarily around a live chat technology similar to Internet Relay Chat but with more controls, direct chat, drag-and-drop file sharing, and development tool integration. Slack can improve communication and collaboration to ensure the development and operations teams are on the same page.

2. Microsoft Teams

Microsoft Teams is a communication and collaboration platform that integrates with all Microsoft software and systems. It offers one-on-one chat capabilities, group channels, file sharing, and video conferencing. Microsoft Teams help with team collaboration through messaging services, video calls, and application sharing to improve work quality and communication.

3. Open DevOps

Open DevOps software is part of Jira, Atlassian’s popular software development tool used in Agile development, a key component of DevOps. Open DevOps helps teams focus on building and operating projects while integrating partnering tools such as Bitbucket, Opsgenie, and Confluence. These tools can save time on coding reviews by testing and identifying any unexpected changes and minimizing content switching during the project development by adding comments, viewing attachments, making edits, and even detecting vulnerabilities.

4. Jenkins

Jenkins is a self-contained, open-source automation server often used for collaboration and integration in DevOps. Jenkins’ integration capabilities support code sharing, code review, notifications and reporting, and collaboration and communication within teams. It offers hundreds of plugins to support building, deploying, and automating any project.

5. Monday.com

Monday.com is a cloud-based platform that allows teams to create applications and project management software and can help teams collaborate, communicate, plan, and manage their DevOps processes. Project management within monday.com includes keeping tasks visible, budgeting, and creating a timeline to uniting teams and their systems.

6. Trello

Trello is a project management tool that uses organization boards, custom to-do lists, and cards to represent tasks. Its built-in automation can handle repetitive tasks like creating agendas, adding team members, and tracking due dates. Companies choose how to visualize projects with dashboards, timelines, tables, and more. Trello offers over 200 integrations with other DevOps software.

7. Azure DevOps

Best for Microsoft users, Azure DevOps is a planning tool that helps developers, project managers, and contributors create better application projects. It includes agile planning boards; Azure Pipelines to support continuous improvement/continuous deployment (CI/CD); GitHub Advanced Security for Azure DevOps; Azure Test Plans for manual and exploratory testing; Azure Artifacts universal package repository; and a marketplace with over 1,000 extensions.

8. Chef

Cloud configuration management tool Chef configures and manages server applications and utilities. Its capabilities describe a series of resources for applications like Hadoop or MySQL, such as dependencies, packages, files, and services. It works at the cloud scale to optimize application delivery, secure cloud assets, move configuration to compliance management, and help achieve compliance audits.

9. Ansible

Ansible is a configuration management and automation tool for software deployment and client configuration covering Unix, Linux, and Windows. It uses JavaScript Object Notation (JSON) and yet another markup language (YAML) instead of Infrastructure as Code (IAC) and works both on internal systems through OpenStack as well as Amazon EC2. It can configure systems, deploy applications, and help support system updates within advanced workflows and focuses on the security and reliability aspects of this technology as an integrated and streamlined software.

10. Puppet

Puppet Enterprise manages infrastructure as code (IAC), an IT infrastructure provisioning process in which systems are automatically built and provisioned with code instead of a scripting process. Because Puppet helps with the coding, the process is easily repeatable. Puppet makes for easier versioning, automated testing, and continuous delivery and can respond to problems or errors quickly.

11. Apache JMeter

Apache JMeter is an open-source pure Java automated testing tool used for DevOps and designed to test a system’s functional behavior while measuring automation performance. Apache JMeter is used to test both static and dynamic resources within web applications by simulating heavy loads on multiple servers and networks to analyze overall performance.

12. Cucumber

Cucumber is a behavior-driven development (BDD) automation testing tool that supports many programming languages. Cucumber includes multiple tools like CucumberStudio, which allows a software development team to co-design the acceptance tests; Cucumber School to help employees effectively learn the system; and Cucumber Open to execute automation testing.

13. HeadSpin

HeadSpin is a data science-driven platform that provides mobile testing, monitoring, and analytic solutions. It provides insight into the performance, user experience, and behavior of mobile applications across devices, networks, and locations. HeadSpin identifies problems and fixes them to improve application performance so your business can be ahead of its competition. HeadSpin’s DevOps not only completes automated testing but also enhances collaboration between teams and offers CI/CD integration.

14. Robot Framework

Robot Framework is an open-source automation framework used for both automation testing and robotic process automation (RPA). It allows testing with hybrid and mobile applications throughout business software with both iOS and Android. Robot Framework’s capabilities can include infrastructures implemented with Python, Java, or other programming languages. Robot Framework can be integrated with most automation solutions and has libraries and tools that have been developed as separate projects.

15. DbVisualizer

DbVisualizer is a database tool that manages, develops, and designs to simplify database object management, administration, exploration, and SQL query development. When working in a DevOps environment, it supports simple coding, flexible integration, customization for planning tools, and meets high-security standards to mitigate project vulnerabilities. DbVisualizer can be leveraged for both planning and database DevOps functions.

16. Liquibase

Liquibase is an open-source database management tool that lets users manage database changes as a code, facilitate automated deployments, and ensure consistency across environments. It fits into the software development lifecycle by providing tools like Changeset and Changelog to help track databases. The tool integrates easily with other tools and providers such as Atlassian, Discourse, and YourKit.

17. Toad by Quest

Toad is a database management tool created by Quest that streamlines database development, administration, and performance-tuning tasks. Toad is often used to simplify workflows, coding, and automate repetitive tasks and promotes collaboration across teams. It can be used for cloud projects to give companies the flexibility they need to create the best software development lifecycle.

18. AWS Database Migration Service

AWS Database Migration Service is Amazon Web Services (AWS)’s database migration tool. It works in DevOps by migrating databases to simplify the process of database schema conversion and data migration while maintaining company infrastructure with integrity. The tool is known to maintain minimal downtime and integrate with tools such as Oracle, SQL Server, MongoDB, and other databases.

19. Docker

Docker is a database tool focusing mainly on containers. In DevOps it can be used to package apps and dependencies on any Linux server, from virtual systems to cloud-based instances. Docker for DevOps allows an isolated application to be deployed to many servers across the internet.

20. GitHub

GitHub is a top software development tool that uses AI to accelerate project development. GitHub uses many tools, including GitHub Codespaces, to help build a DevOps environment quickly to help code, build, and test from anywhere. Another tool, GitHub Copilot, is an AI programmer to help give coding suggestions to users. GitHub Actions helps automate workflows to improve how processes are completed, and collaboration features include a mobile tool to connect employees and teams on the go and at the office.

Bottom Line: DevOps Tools

Many tools offer a wide range of features and advantages for businesses looking to add collaboration, planning, configuration management, automated testing, and database processes into their DevOps software development lifecycles. Some of these tools are purpose-built for DevOps, others are used in a wide range of industries and environments but can help make teams more efficient. Identify the types of tools you might benefit from most, and consider how they fit with your budget and existing tool stack.

Table Of Contents

• Top Robotics Companies
  • UiPath
  • ABB Ltd
  • Universal Robots
  • KUKA
  • FANUC
• Benefits of Robotics
• How To Choose a Robotics Company
• Bottom Line: Top Robotics Companies

Top Robotics Companies

All the robotics companies covered here offer feature-rich services in differing areas of focus, from mobile robots that integrate with your infrastructure to sensors that can monitor for and respond to different conditions. While your business’s specific needs and budget will be a key factor in finding the right company, pricing is another factor—and comparing pricing can be a challenge.

Because robotics is not an off-the-shelf solution, customization means pricing can vary widely. We’ve gathered publicly available pricing information where possible, but for accurate quotes you’ll need to contact the vendors.

UiPath

UiPath is a leading provider of robotic process automation (RPA) technology, sometimes referred to as “software robotics,” and provides both automation and operations to customers. UiPath’s RPA platform uses automation and artificial intelligence (AI) to streamline business practices. It offers visual design tools and integration capabilities, and allows bots to interact with different systems, applications, and databases. UiPath provides solutions for many industries, giving businesses direct control with a drag-and-drop user interface.

Features

• AI and Machine Learning (ML): UiPath integrates AI and ML capabilities with software bots to handle unstructured data, make better decisions, and adapt to unexpected changes, improving automation potential for users.
• Robust Integration: UiPath bots can interact with a broad range of systems, applications, and databases, including web and desktop applications, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and more.
• Screen Scraping: UiPath’s screen-scraping enables bots to extract data from business applications including legacy systems and virtual environments to fuel the RPA technology.

Pricing

UiPath provides three pricing options: There is a free option for personal use. The pro plan starts at $420 a month; for enterprise plan pricing, contact sales.

ABB Ltd

As one of the leading robotics and machine automation suppliers, ABB Robotics offers a large range of robots, software, and services. ABB Robotics and Discrete Automation has a wide portfolio of tools in a wide range of sizes, payloads, and reach capabilities, and provides robotics, machine automation, digital services, and other innovative solutions for many industries.

Features

• Vision And Sensor Integration: ABB Robotics emphasizes the seamless integration of vision systems and sensors into robotics tools to facilitate data exchange and coordinated workflow within the automation setup.
• Flexibility: ABB’s robots are designed to be flexible and capable of performing various tasks including assembly, welding, material handling, painting, inspection, and more.
• Affordability: While prices can range based on solutions, ABB Ltd offers robotics options for businesses with a lower budget.

Pricing

ABB Ltd has a pricing page; price ranges from $33,000 to $90,500 depending upon maximum reach, maximum payload, arms, and types.

Universal Robots

Universal Robots, part of Teradyne Inc., aims to “create a world where people work with robots, not like robots.” To meet this goal, it uses collaborative robots, or cobots, that can automate repetitive tasks and shift them away from human workers. The cobots are meant to share work with human employees, making its automation possible to be used for a variety of applications.

Features

• Build-Your Own-Application: Users can choose from 400 components, kits and solutions, software tools, and safety accessories for robot integration, giving a wide range of options.
• Robot Collaboration: The company can connect operators to robots for successful automation by building a program, selecting functions, and executing them line by line, or via physical interaction by manually moving the robots’ arms.
• No Coding, Easy Setup: Universal Robots are designed to be used without robotics or coding experience. The company offers classes and has experts on call.

Pricing

Universal Robots does not have a pricing page. Based on user reviews, customers should budget between $50,000 and $100,000 depending on cobots and accessories.

KUKA

KUKA provides industrial robots for factory automation, with customers ranging from BMW and Ford to Siemens, Airbus, and Volkwsagen. The company also offers cobots with various payload capacities and reaches, and builds them to size and specifications for a wide range of deployments in automotive, electronics, food and beverage, foundry and foreign, medical, plastics, and other industrial applications. KUKA also sells used robots to help make entry into robotics more affordable for smaller companies.

Features

• Motion Control: KUKA robots have advanced mobile control systems that enable precise movements, optimizing performance, speed, and productivity.
• Programming Interface: KUKA robots provide an user-friendly interface during programming and operations, facilitating a wider range of tasks.
• Robot Controllers: The company sells both manual and autonomous robots that self-navigate, minimizing intervention.

Pricing

Pricing varies based on a wide range of factors. Used robots are available on the KUKA Marketplace.

FANUC

FANUC offers a range of industrial robotics products and services including computer numerical control (CNC) machining systems and factory automation solutions. It has more than 100 models of robots and cobots for manufacturing in a wide range of industries designed to be easy-to-operate and flexible, with payloads up to 5,000 pounds.

Features

• Wide Range: Existing models make it easier for customers to find the right fit for their specific industrial application based on payload capacities, capabilities, and configurations.
• Customer Support: The company provides a wide range of support services for customers, including system installation, training, maintenance, and technical support.
• Robust And Durable: Known for robust construction and durability, FANUC robots are designed for demanding industrial environments.

Pricing

FANUC does not provide pricing details. Contact sales for quotes. Customer reviews list prices in a range from $6,000 to $200,000.

Benefits Of Robotics

Robotic tools provide comprehensive support in a wide range of industrial applications.

• Enhanced Productivity: By automating routine tasks, robotic tools enable your company to devote more time to critical tasks.
• Consistent Performance: Once an automation framework is established, robotic tools operate consistently, minimizing human intervention and distraction.
• Improved Safety: Incorporating robotics provides safety measures, especially when dealing with heavy machinery. Human employees are vulnerable to potential harm from objects in the workplace, but robots can be repaired.
• Accelerated Efficiency: Robots achieve constant productivity, minimizing downtime.

How To Choose A Robotics Company

There are several criteria to weigh when looking to choose the right robotics provider.

Type of Robotics

Depending on the industry and the specific tasks the robot will undertake, it is important to explore various types of robots. Popular types include autonomous mobile robots (AMRs), automated guided vehicles (AGVs), articulated robots, humanoids, cobots, and hybrids. AGVs are beneficial for delivery and transportation services, while humanoids are well-suited for research.

Maintainability and Availability

Consider the reliability, repairability, and availability of robotics tools—factors like battery duration and part complexity can have an impact on efficiency. Providers should offer support for maintenance and repairs to help avoid downtime.

Cost

While it can be difficult to compare prices with robotics tools, establish a budget that incorporates the initial investment as well as upkeep, repair, and downtime costs. Narrow down providers by their ability to meet your robotic needs, and then work with their sales teams to receive an exact cost.

Bottom Line: Top Robotics Companies

The top robotic companies offer a wide range of features and advantages for businesses looking to automate processes. While these robots offer numerous benefits, there are also potential disadvantages to consider, such as initial investment costs, integration complications, and the need for coding professionals. However, with proper training, planning, and maintenance, these robots can revolutionize industry automation and drive your business toward increased productivity and competitiveness.

One area in which they are especially useful is customer service, where they can improve the accuracy and efficiency of call center and help desk personnel, facilitate customer self-service, and speed up everything from employee training to problem-solving and information recovery.

Organizations looking to implement knowledge management for customer service or other uses have a number of options from which to choose. While budget will play a part in an software selection decision, it’s just one of many factors to consider, and this guide ranks the best knowledge management systems by use case to help you see how they compare to your own particular needs.

• Best for Collaboration: Confluence
• Best for Multi-Channel: ZenDesk for Service
• Best for SMBs: Zoho
• Best for Self Service: Jira
• Best for Sales and CRM Integration: Salesforce
• Best for Agent Assistance: KMS Lighthouse
• Best for Customer Engagement: Verint

Top Knowledge Management Software at a Glance

Knowledge management software is very much in demand, with Gartner reporting that 74 percent of customer service and support leaders have set a priority of improving knowledge and content delivery to customers and employees. The recent boom in artificial intelligence (AI) is affecting this market, like so many others, with systems that incorporate AI features and chatbots becoming increasingly popular.

Each of the top systems takes a slightly different approach to knowledge management, offering a mix of features and benefits. Here’s a quick look at how they compare.

Jump to:

• Key features of knowledge management software
• Benefits of working with knowledge management software
• Bottom Line

Confluence

Best for Collaboration

Atlassian’s Confluence is all about content collaboration across Android, iOS, Linux, and Windows devices. This cloud-based system enables companies to publish, organize, and access knowledge from a single place, and is especially well-suited to helping organizations collaborate on knowledgebase data across multiple channels.

Features

• Works across multiple channels on Android, iOS, Linux, and Windows devices
• Cloud-based
• Lets users create documents, publish, organize, and access knowledge from a single place
• Collaboration features include feedback on new documents, keeping track of versions, sharing documents, exporting PDFs, and copy/pasting images
• Includes project management and Jira integration

Pros

• Can collaborate with Asana, Slack, Miro Board, Google Sheets, and other tools
• Good ease of use
• Enterprise-grade permission handling

Cons

• Lack of a flowchart builder
• Dated user interface
• Lack of Microsoft Teams integration

Pricing

Confluence costs $5.75 per user, per month for the standard version and $11 for premium. The price goes down by almost half after 1,000 licenses. A free “lite” version for up to 10 users lacks enterprise features and includes just 2 GB of storage.

ZenDesk for Service

Best for Multi-Channel

Zendesk for Service provides an open, flexible platform designed to enable customer self-service. It helps organizations provide personalized documentation across any channel, can scale to the large-enterprise size, and has an integrated Help Desk ticketing system.

Features

• Users can interact via phone, email, chat, and social media
• Easy to implement, use, and scale
• Integrated ticketing system
• Includes AI and automation for faster issue-resolution
• Facilitates customer self-service

 Pros

• Offers a unified workspace with a contextual interface
• Omnichannel support

 Cons

• Can be too complex to use for SMBs
• Expensive
• Can be difficult to integrate, especially for small businesses

Pricing

ZenDesk starts at $49 per user, per month. For the self-service customer portal, AI, customizable tickets, and multilingual support, the price rises to $79. The professional version at $99 also includes a live agent activity dashboard, integrated community forums, private conversation threads, and more.

Zoho

Best for SMBs

Zoho Desk can manage all customer support activities and is context aware. It has integrated Voice over IP (VoIP) features and comes with analytics and AI tools as well as a ticketing system, making it a good choice for SMBs and mid-sized enterprises.

Features

• iOS and Android compatible
• Provides features for interacting with agents through VoIP and social media
• Agent, manager, and customer-specific features
• Includes a ticketing system
• Strong reporting capabilities
• Tracks customer requests across channels

Pros

• Cloud-based system is easy to use and makes ticket-tracking easy
• Users can manage tickets and everything else in one place
• Includes AI-based chat and analytics

Cons

• Not designed for large enterprises
• Some customization and integration limitations

Pricing

Zoho is free for up to three users. The Professional plan costs $12 per user, per month, and the Enterprise plan costs $25 per user, per month.

Jira

Best for Self Service

Jira Service Management is a tool for self-service knowledge management for employees and customers. It helps trace knowledge usage frequency and can identify content gaps and flawed articles. AI-powered search is available as well as good editing and formatting capabilities.

Features

• Tracks document changes, incident runbooks, and playbooks so teams can continuously learn and improve
• Helps monitor knowledge usage to identify content gaps, optimize articles, and see which articles deflect the most requests
• Provides a federated knowledge base

 Pros

• Self-service management of knowledge articles
• Provide companies and employees with relevant articles quickly
• AI-powered search that surfaces relevant knowledge articles

Cons

• Knowledge management is one facet of a much larger suite; may not be suitable for people who only need knowledge management.

Pricing

Jira Service Management is free for up to three users. Its premium plan starts at $47 per user, per month. A custom enterprise plan is also available.

Salesforce Service Cloud

Best for Sales and CRM integration

Salesforce Service Cloud is part of the vast Salesforce universe. Its aim is to help customers find answers quickly across any channel, which it accomplishes by empowering agents with the best answers to questions. This multichannel solution also incorporates AI.

Features

• Centralized knowledgebase for all agent and customer information
• Uses analytics to identify which knowledge articles are working and to identify new articles that need to be created
• Automatically suggests articles for conversations
• Can share across multiple channels
• Can embed knowledge articles into a website, portal, community, and mobile app

Pros

• Can quickly deliver answers to customers by adding the knowledgebase to the Salesforce agent workspace
• Integrates fully with Salesforce customer relationship management (CRM)
• Uses AI chat bots to recommend articles
• Integrated computer telephony capabilities

Cons

• May be too much for companies that just want knowledge management, as it contains case management, service console, service contracts, computer telephony integration, web services, and more.

Pricing

Salesforce Service Cloud only provides knowledge management in the starter ($25 per user, per month) and unlimited ($300 per user per month) versions.

KMS Lighthouse

Best for Agent Assistance

KMS Lighthouse is all about knowledge management, and seeks to improve first-interaction resolution by intelligently directing agents to the right answer and reduce call center operational costs.

Features

• Built-in intelligence can cut agent training time in half to onboard agents and employees
• Lighthouse call center knowledgebase serves as a “single point of truth” to help call center agents speed up calls and avoid inaccuracies
• Lighthouse Chat enables agents to communicate and collaborate with knowledge-sharing via instant messaging and links to articles and relevant content

Pros

• AI provides instant responses to agents and customers during search
• Can function like a personal assistant to answer on-the-job questions
• Makes all product/service knowledge easy to tap into and compare to help with upselling and cross-selling

Cons

• Integration can be a challenge
• Needs better reporting

Pricing

KMS Lighthouse starts at $25 per user, per month.

Verint

Best for Customer Engagement

Verint Knowledge Management integrates across business operations with self-service contact center capabilities designed to help staff engage better with customers. Automated knowledge is embedded directly in tools and workflows.

Features

• Uses context from customer history to personalize results, resulting in the right knowledge appearing with little to no searching.
• Helps agents find answers via search using everyday language

Pros

• Guides decision trees help to resolve complex issues
• Helps agents understand what customers are looking for
• New content is automatically analyzed and optimized for search, removing the burden of manual tagging and linking

Cons

• Vendor is not transparent about pricing models
• Customer reviews say it is expensive

Pricing

Verint does not publicize its pricing models.

Key Features of Knowledge Management Software

While each platform takes a slightly different approach to knowledge management, all of the systems in this article share some common features.

Cloud-based

Knowledge management repositories should include all of the business’s articles and sources of knowledge, but locking it all on-premises can be limiting. Cloud-based systems integrate with other systems more easily and can better facilitate search and sharing among users and customers.

Multichannel

Knowledge management software should make it easy to collaborate across multiple channels, such as phone, email, chat, social media or other channels. Information should be always accessible, anywhere, on any channel, on tablets and mobile devices, and on PCs and laptops.

AI Chat

AI is being incorporated into a great many tools and IT systems, and knowledge management is no exception. Its best use case is in chatbots that provide users and agents with answers to questions, summarize information, and provide sales data.

Help Desk

Knowledge management systems can be tightly integrated with a help desk as well as with customer contact center systems, though not all users need this functionality, making it a selection point to narrow down choices when considering systems.

Price

Generally speaking, the more features and capabilities a knowledge management package includes, the higher the cost. Lower costs systems may suffice for organizations that need limited features. Those that need enterprise capabilities, help desk integration, and advanced AI and should expect to pay more.

Knowledge Management System Benefits

A knowledge management system can benefit a business in a number of ways. Here are a few of the most common:

• Provides all enterprise knowledge in one place
• Offers powerful search capabilities to find information quickly
• Helps customer service agents answer customer questions
• Lets customers access knowledgebase for self-service
• Makes it easy and fast to update information
• Improves both accuracy and consistency
• Helps with training new employees

Methodology

The items on this were chosen based on analyst evaluations, user reviews, and assessment of a wide range of lists suggested by knowledge management experts.

Bottom Line: Top Knowledge Management Systems

While knowledge management systems have broad utility for many aspects of an organization’s work, they can be especially useful to help reduce costs of customer service, facilitate self-service, and speed up everything from employee training to problem-solving and information recovery. Organizations should select knowledge management software based rigidly on their specific business needs. Some need all the bells and whistles that come with enterprise-class systems, such as scalability, help desk integrations, and more, while others will only need specific knowledge management functionality. Choose the system that best meets your specific needs without charging for unnecessary features.

For the greater part of the last decade, three tools have come to dominate this category: Ansible, Progress Chef, and Puppet. On a general level, here’s how they stack up:

• Ansible: Lightweight and easy to use; ideal for supporting a wide range of environments and configurations
• Progress Chef: Powerful, secure, and highly customizable; ideal for organizations that require an advanced automation platform
• Puppet: Feature-laden with a large user base and following; ideal for enterprises looking for a premium automation solution

In this article, we’ll delve into each of these automation tools’ respective strengths and shortcomings across eight key categories: pricing, ease-of-use, quality of support and documentation, breadth of features, integrations, supported environments, size/makeup of customer base, and community size.

Ansible vs. Chef vs. Puppet at a Glance

Best for Pricing: Ansible and Progress Chef

When it comes to pricing, premium versions of Ansible and Progress Chef fall in the same range—$137 to $140 a year per node—while Puppet Premium is on the high end at $199 a year per node. It’s worth noting that all three are all available as free open source projects, though the commercial versions come with additional features and support. Organizations with large scale deployments and complex infrastructures may be better suited with the premium versions.

Best for Ease of Use: Ansible

Ansible takes the lead in this category for myriad reasons. From its minimal setup/configuration and agentless architecture to declarative model and use of YAML syntax, the automation platform was designed to be lightweight and easy to use.

On the other hand, Progress Chef and Puppet are notorious for their steep learning curves and require users to learn a domain-specific language (DSL) and understand how to configure/deploy agents on target systems. That said, they also provide more granular control and flexibility for custom use cases at the cost of additional complexity.

Best for Support and Documentation: Ansible

Ansible boasts excellent documentation, featuring comprehensive guides, modules, and examples. Furthermore, its extensive user community contributes to the solution’s continuously expanding knowledge base. Progress Chef and Puppet also provide comprehensive documentation, albeit not as novice-friendly as Ansible’s materials. In terms of support, Puppet, Progress Chef, and Ansible all offer enterprise support as well as community-driven forums to assist users in resolving issues.

Best for Features: Three-way tie

When it comes to the breadth of features, all three automation tools provide robust capabilities.

• Ansible focuses on simplicity and ease of use, providing a wide range of modules for infrastructure provisioning, configuration management, and application deployment.
• Progress Chef emphasizes infrastructure as code, enabling users to define and manage their infrastructure using the Chef DSL.
• Puppet excels in configuration management, featuring a powerful domain-specific language for defining system states.

Ansible Feature Highlights

Some of Ansible’s more notable features include the following:

• Agentless Architecture: Ansible’s standout feature is its agentless architecture, which eliminates the need to install and manage agents on target systems. Ansible connects to remote systems using secure shell (SSH) or other remote protocols, making it lightweight, easy to set up, and non-intrusive to the existing infrastructure.
• YAML-based Playbooks: Ansible uses YAML-based playbooks to define and orchestrate automation tasks. Playbooks provide a human-readable and easily understandable format, allowing users to describe the desired state of systems and execute complex workflows using simple and concise syntax.
• Push-based Model: Ansible follows a push-based model, where the control machine pushes configurations and tasks to the target systems. This model enables real-time execution of tasks, making Ansible well-suited for ad-hoc automation, configuration management, and application deployment.
• Built-in Idempotence: Ansible enforces “idempotent” operations, which means that executing the same playbook multiple times results in the same desired state. This feature eliminates the risk of unintended configuration changes and allows for safe and predictable automation across various systems and environments.
• Extensibility and Integrations: Ansible offers a wide range of modules and plugins, allowing users to extend its functionality and integrate with existing tools and technologies. Ansible can seamlessly integrate with cloud platforms, networking devices, databases, and more, making it a versatile automation tool that can span across different infrastructure components.

Progress Chef Feature Highlights

Some of Progress Chef’s more notable features include the following:

• Chef Infra: Progress Chef’s Infra platform enables developers and operators to define infrastructure configurations using a declarative language, providing version control, repeatability, and consistency across environments.
• Chef Habitat: Progress Chef includes Chef Habitat, a tool that simplifies the packaging, deployment, and management of applications. Habitat provides a framework for creating platform-agnostic application artifacts, enabling easy application migration and seamless deployment across different environments.
• Chef Automate: Progress Chef offers Chef Automate, a comprehensive platform and dashboard that provides visibility and control across the entire automation infrastructure. The platform offers real-time analytics, compliance reporting, and a centralized dashboard for managing infrastructure, applications, and security.
• Cookbook Library: Progress Chef boasts an expansive community-driven cookbook library, which is a collection of pre-built, reusable configuration and automation scripts. This library enables developers to leverage the expertise and best practices of the Chef community, accelerating the deployment of infrastructure and applications.
• Enterprise Security and Compliance: Progress Chef places a strong emphasis on security and compliance and provides features like role-based access control (RBAC), encrypted data transmission, and built-in audit trails to ensure the security of your infrastructure. Additionally, Progress Chef helps organizations adhere to various industry standards and regulations and streamline the process of meeting security and governance requirements.

Puppet Feature Highlights

Some of Puppet’s more notable features include the following:

• Declarative Language: Puppet’s declarative language, Puppet DSL, enables users to describe the desired end state of their infrastructure and configurations, rather than focusing on the specific, step-by-step instructions, thereby simplifying the management and maintenance of complex systems.
• Puppet Forge: Puppet offers Puppet Forge, a vast repository of pre-built modules that provide ready-to-use configurations and automation scripts. This extensive library allows users to leverage community-contributed content, saving time and effort in developing and implementing configurations from scratch.
• IaC Support: Puppet supports IaC by allowing users to define and manage infrastructure configurations in a version controlled, tracked database or repository (e.g., GitHub) as if it were application code.
• Puppet Enterprise: Puppet provides Puppet Enterprise, a centralized and enterprise-grade solution for configuration management. The platform extends the capabilities of the open-source Puppet tool with additional features like a graphical user interface (GUI), RBAC, event inspector, and other advanced orchestration capabilities.
• Master-agent Architecture: Puppet’s master-agent model consists of a Puppet master server controlling and managing the configurations of multiple Puppet agent nodes. This architecture allows for efficient and scalable management of distributed systems, ensuring consistent configurations across a network of nodes.

Best for Integrations: Ansible

Ansible boasts an extensive range of integrations, with support for various cloud platforms, infrastructure providers, and third-party tools through its vast collection of modules. Similarly, Progress Chef and Puppet also offer integrations with popular cloud platforms and tooling.

Ansible Integration Highlights

Some of Ansible’s more notable integrations include the following:

• Cloud Platforms: Ansible integrates with popular cloud platforms like AWS, Microsoft Azure, Google Cloud Platform (GCP), and VMware vSphere, and supports automatic provisioning, deployment, and management of cloud resources, including virtual machines, networking, and storage.
• Version Control Systems: Ansible seamlessly integrates with version control systems like Git/GitHub, enabling users to store and manage Ansible playbooks and configurations in a version-controlled repository.
• Networking Devices: Ansible integrates with network devices from leading vendors like Cisco, Juniper, Arista, and F5 Networks, and provides modules specifically designed for network automation, enabling users to manage and configure network devices programmatically.
• Continuous Integration/Continuous Deployment (CI/CD) Tools: Ansible integrates with popular CI/CD tools like Jenkins, GitLab CI/CD, and Atlassian Bamboo, allowing users to incorporate Ansible automation into their CI/CD pipelines, as well as automate the deployment of applications and infrastructure as part of the continuous delivery process.
• Databases: Ansible provides modules for interacting with leading databases like MySQL, PostgreSQL, Oracle, and MongoDB, allowing users to automate tasks related to database provisioning, configuration, and data management.
• Container Orchestration Platforms: Ansible integrates with container orchestration platforms like Kubernetes and Docker Swarm, allowing users to automate the deployment and management of containers, including provisioning, scaling, and updating containerized applications

Progress Chef Integration Highlights

Some of Progress Chef’s more notable integrations include the following:

• Cloud Platforms: Progress Chef Automate integrates with major cloud platforms such as AWS, Microsoft Azure, and Google Cloud Platform (GCP). This integration enables users to automate the provisioning, configuration, and management of cloud resources and services.
• Container Orchestration Platforms: Progress Chef Automate integrates with container orchestration platforms like Kubernetes and Docker Swarm. It provides capabilities for managing and automating the deployment of containerized applications, including configuration management and scaling.
• CI/CD Tools: Progress Chef Automate integrates with popular CI/CD tools such as Jenkins, GitLab CI/CD, and Atlassian Bamboo. These integrations enable users to incorporate Progress Chef Automate into their CI/CD pipelines, automating the deployment and configuration of applications as part of the continuous delivery process.
• Monitoring and Logging Tools: Progress Chef Automate supports integration with monitoring and logging tools like Prometheus, Elasticsearch, and Splunk. These integrations allow security teams to collect and analyze data from Progress Chef Automate for monitoring, troubleshooting, and compliance purposes.

Puppet Integration Highlights

Some of Puppet’s more notable integrations include the following:

• Cloud Platforms: Puppet integrates with major cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and VMware vSphere. These integrations enable users to manage and automate the provisioning, configuration, and management of cloud resources.
• CI/CD Tools: Puppet integrates with popular continuous improvement/continuous deployment (CI/CD) tools such as Jenkins, GitLab CI/CD, and Atlassian Bamboo. These integrations allow users to incorporate Puppet into their CI/CD pipelines, automating the deployment and configuration of infrastructure and applications as part of the continuous delivery process.
• Version Control Systems: Puppet integrates with version control systems like Git and Subversion. These integrations allow users to manage Puppet code, manifests, and modules using version control, facilitating collaboration, change tracking, and version management.
• Networking Devices: Puppet supports integration with network devices from various vendors, including Cisco, Juniper Networks, Arista Networks, and F5 Networks. These integrations enable users to automate network device configurations, provisioning, and management.
• Container Orchestration Platforms: Puppet integrates with container orchestration platforms such as Kubernetes and Docker Swarm. These integrations enable users to manage and automate the configuration and deployment of containerized applications.

Best for Supported Environments: Three way tie

All three tools support a wide array of operating systems and environments. As an agentless solution, Ansible can manage heterogeneous environments effortlessly, including Windows, Linux, and network devices. Progress Chef and Puppet, with their agent-based approach, can manage a diverse range of systems and are well-suited for complex environments.

Best for Customer Base: Ansible 

Despite being the youngest of the three automation tools, Ansible has gained significant traction in recent years and has amassed a large, rapidly growing user base. The tool’s ease-of-use and extensive community support are key ingredients driving its popularity. Progress Chef and Puppet also have substantial customer bases, but more so with larger organizations and enterprises.

Best for Community Size: Ansible

Ansible has a thriving, active community that actively contributes to its wealth of resources in the form of blogs, forums, and GitHub repositories. Similarly, Progress Chef and Puppet also have active, sizable communities, though not as large or vocal as Ansible’s crowd.

Top 3 DevOps Configuration Tooling Alternatives

Chef, Ansible, and Puppet aren’t the only automation tooling players in the game. The following alternatives provide similar features and capabilities, and may warrant further investigation

if none of the leading three platforms suit your organization’s needs.

AWS CloudFormation (and Cloud Vendor-provided Tools)

If you’re looking to automate an AWS-based cloud infrastructure, AWS CloudFormation is a formidable alternative to Puppet, Chef, and Ansible. In fact, CloudFormation—like Ansible—enables you to define IaC using JSON or YAML templates However, CloudFormation naturally a more AWS-centric solution for describing and provisioning AWS resources like EC2 instances, load balancers, and databases, along with their configurations and dependencies. Unsurprisingly, similar services like Microsoft’s Azure Resource Manager (ARM) and Google Cloud Deployment Manager (GCDM) also offer automation capabilities that excel in their native vendors’ environments.

SaltStack

Another long standing alternative to Puppet and Progress Chef, SaltStack is an open-source configuration management and orchestration tool known for its scalability and performance. The tool uses a master-minion architecture that has a master node communicating with multiple minion nodes to manage configurations and execute tasks.

Terraform

While not a direct alternative to Ansible, Puppet, or Progress Chef, Terraform does possess some overlapping capabilities for infrastructure provisioning and management. The solution enables users to define infrastructure as code using a declarative language, and supports multiple cloud platforms—AWS, Azure, Google Cloud, as well as on-premises infrastructure. With Terraform, you can define the desired state of your infrastructure and execute plans to create, modify, or delete resources accordingly.

Bottom Line: Ansible vs. Chef. vs. Puppet

In short, Ansible excels in pricing, ease-of-use, and community size, making it an ideal choice for organizations seeking a user-friendly automation tool for basic IT environments. Progress Chef and Puppet provide more granular control mechanisms and robust features, making them suitable for complex environments with specific requirements. By carefully evaluating these three tools based on pricing, ease-of-use, documentation, features, integrations, supported environments, customer base, and community, you’ll be better positioned to make an informed decision that aligns with your organization’s needs and goals.

We’ve rounded up the best intrusion prevention systems to help make the selection process less daunting. Here are our top picks:

• Cisco Secure Next-Generation Intrusion Prevention System: Best for Comprehensive Network Security
• Fidelis Network: Best for Advanced Threat Detection Response
• Palo Alto Networks Advanced Threat Prevention: Best for ZeroDay Exploits
• Trellix Intrusion Prevention System: Best for On-Prem and Virtual Networks

Top Intrusion Prevention System Comparison At-a-Glance

Here’s a look at how the top IPSs compared based on key features.

Jump to:

1. Key Intrusion Prevention System Features
2. How to Choose an IPS
3. Frequently Asked Questions (FAQs)

Cisco Secure Next-Generation Intrusion Prevention System

Best for comprehensive network security

Cisco offers advanced threat protection solutions with Cisco Secure IPS. This cloud-native platform offers robust security with unified visibility and intuitive automation. It gathers and correlates global intelligence in a single view and can handle large traffic volumes without impacting the network performance.

This highly flexible solution can be easily deployed across different network environments as its open architecture supports Amazon Web Services (AWS), VMWare, Azure, and other hypervisors.

Features

• Enhanced visibility with Firepower Management Center
• Constantly updated early-warning system
• Flexible deployment options for inline inspection or passive detection
• Cisco Threat Intelligence Director for third-party data ingestion

Pros

• Real-time data inputs optimize data security
• Easy integration without major hardware changes
• High scalability with purpose-built solutions

Cons

• Expensive for small-scale organizations
• Initial integration challenges

Pricing

Cisco offers free trials for most products, including its IPS, but does not make its pricing readily available. For details, contact Sales Support.

Fidelis Network

Best for Advanced Threat Detection Response

Fidelis Network improves security efficiency by detecting advanced threats and behavioral anomalies, employing a proactive cyber-defense strategy to more quickly detect and respond to threats before they can affect a business. Fidelis Network can bolster data security with rich insights into bi-directional encrypted traffic.

This specific network defense solution helps prevent future breaches with both real-time and retrospective analysis.

Features

• Patented Deep Session Inspection for data exfiltration
• Improved response with the MITRE ATT&CK framework and intelligence feed from Fidelis Cybersecurity
• Unified network detection and response (NDR) solution for simplified network security
• Customizable real-time content analysis rules for proactive network security

Pros

• Faster threat analysis and improved security efficiency
• Deeper visibility and threat detection with more than 300 metadata attributes
• Single-view and consolidated network alerts with rich cyber terrain mapping

Cons

• Complex configuration and setup
• High-traffic environments cause network latency
• Tighter integration with other tools is required

Pricing

Fidelis Network offers a 15-day free trial, and will schedule a demo before it to show off the system’s capabilities and features.

Palo Alto Networks Advanced Threat Prevention 

Best for Zero-Day Exploits

Palo Alto Networks’ Advanced Threat Prevention is based on purpose-built, inline deep learning models that secure businesses from the most advanced and evasive threats. Powered by multi-pronged detection mechanisms that efficiently take care of unknown injection attacks and zero-day exploits, this infinitely scalable solution blocks command and control (C2) attacks in real time without compromising performance.

Features

• ML-Powered NGFWs for complete visibility
• Customized protection with Snort and Suricata signature support
• Real-time analysis with enhanced DNS Security Cloud Service
• Latest security updates from Advanced WildFire

Pros

• Ultra low-latency native cloud service
• Combined App-ID and User-ID identification technologies
• Customized vulnerability signatures
• Complete DNS threat coverage

Cons

• Overly complex implementation for simple configurations
• High upfront costs

Pricing 

Palo Alto Networks offers free trials, hands-on demos, and personalized tours for its products and solutions, but does not make its pricing models publicly available. Contact sales for details.

Trellix Intrusion Prevention System

Best for On-Prem and Virtual Networks

Trellix Intrusion Prevention System offers comprehensive and effective security for business networks, offering two variants: Trellix Intrusion Prevention System and Trellix Virtual Intrusion Prevention System. The virtual variant takes care of the private and public cloud requirements, and secures virtualized environments using advanced inspection technologies.

Features

• Botnet intrusion detection across the network
• Enhanced threat correlation with network threat behavior analysis
• Inbound and outbound SSL decryption
• East-west network visibility

Pros

• Both signature-based and signature-less intrusion detection
• Unified physical and virtual security
• Maximum security and performance (scalability up to 100 Gbps)
• Shared licensing and throughput model

Cons

• Older variants and models still exist
• Confusion pricing options
• High rates of false positives

Pricing

Schedule a demo to learn whether Trellix meets specific requirements. The vendor does not make pricing models publicly available; contact sales.

Key IPS Features

When deciding on an intrusion prevention system, make sure the features and capabilities match specific needs. Key features include the following:

Real-time alerts

Proactive threat detection and prompt incident response require real-time visibility. Timely alerts help implement preventive measures before any significant damage to the security posture. Advanced IPSs have real-time monitoring capabilities to identify potential vulnerabilities and minimize the impact of security incidents.

Integration with other security systems

Intrusion prevention systems cannot operate in isolation. For the efficient protection of the entire business security infrastructure, they must integrate with other security solutions and platforms for a coordinated response. This also helps with the centralized management of security incidents.

Type of intrusion detection

There are mainly two types of intrusion detection: network-based and host-based. While network-based intrusion detection examines and analyzes the network traffic for vulnerabilities, host-based intrusion detection checks individual systems like servers, endpoints, or particular assets.

Automatic updates

Automatic updates can help ensure an IPS adapt to the continuously evolving threat landscape of new threats and newly discovered vulnerabilities. They can also help keep pace with changing compliance and regulatory requirements and implement the latest security patches.

Threat intelligence

Threat intelligence helps an IPS enhance detection capabilities and minimize vulnerabilities with efficient mitigation strategies. With threat intelligence capabilities, IPS solutions access timely and actionable information to develop effective response strategies.

How to Choose an IPS

Here are some factors to consider when choosing an IPS:

Configuration type

There are broadly four types of IPS configurations depending on the network environment, security policies, and requirements where they will be implemented: network-based, host-based, wireless, and network behavior analysis system. Multiple configurations can also support complex pathways.

Detection capabilities

Intrusion prevention systems use different detection techniques to identify malicious activities—primarily signature-based, anomaly-based, and protocol-based. Signature-based detection helps detect consistent cyber threat patterns from a static list of known signatures, while anomaly-based detection can detect abnormalities within normal activity patterns. Protocol-based systems offer the flexibility to set references for benign protocol activities.

Integration options

Intrusion prevention systems can be integrated using dedicated hardware and software, or incorporated within existing enterprise security controls. Businesses that don’t want to upgrade system architecture or invest in products or resources can rely on managed service providers for security, but an IPS purchased and installed on the network offers more control and authority.

Frequently Asked Questions (FAQs)

What is the difference between intrusion detection systems and intrusion prevention systems?

Intrusion detection systems help detect security incidents and threats and send alerts to the Security Operations Center (SOC). Issues are investigated by security personnel and countermeasures executed accordingly. Essentially, they’re monitoring tools. While intrusion prevention systems also detect potential threats and malicious incidents, they automatically take appropriate actions, making them highly proactive, control-based cybersecurity solutions.

How do intrusion prevention systems help businesses?

Intrusion prevention systems are key to enterprise security as they help prevent serious and sophisticated attacks. Some of the key benefits of IPS for businesses are:

• Reduced strain on IT teams through automated response
• Customized security controls as per requirements
• Improved performance by filtering out malicious traffic

Do intrusion prevention systems affect network performance?

Intrusion prevention systems may slow down the network in the case of inadequate bandwidth and capacity, heavy traffic loads, or computational burdens.

Methodology

In order to provide an objective and comprehensive comparison of the various IPSs available in the market, we followed a structured research methodology. We defined evaluation criteria, conducted market research, collected data on each solution, evaluated and scored them, cross-verified our findings, and documented the results. Additionally, we considered user reviews and feedback to gain valuable insights into the real-world performance and customer satisfaction of each intrusion prevention solution.

Bottom Line: Top Intrusion Prevention Systems

The top intrusion prevention systems all work to protect enterprise networks from the ever-present, always evolving threat of cyberattack, but some stand out for different use cases. Selecting the right one will depend on the organization’s security needs, goals, and budget. Regular evaluation and updates are crucial to staying ahead of evolving threats and ensuring a robust security posture—the right IPS can enhance network security, protect sensitive data, and safeguard a business against potential cyber threats.