Jump to:

• Data Simulation Features
• Benefits of Data Simulation
• Data Simulation Use Cases
• Types of Data Simulation Models
• Data Simulation Software Providers
• Bottom Line: What Is Data Simulation?

Data Simulation Features

Simulated data can be used to help validate and test complex systems before applying them to authentic data. Simulated data is also complete, and rarely has any gaps or inconsistencies, making it suitable for checking the validity and quality of an analytics system under ideal conditions. While this all can be done using real-life data, with data simulation it comes at a fraction of the cost, and without all the legal and ethical concerns that may arise in handling and storing user data.

Data simulations are attractive to individuals, teams, and enterprises that work with data for myriad reasons beyond just affordability. Its features can be considered in three main areas—flexibility, scalability, and replicability:

• Flexibility. Since the data is manufactured, it can be adjusted to simulate a wide range of scenarios and conditions without ethical constraint, allowing a system to be studied in more depth. This is particularly useful when testing out large-scale simulation models and predictive models. It’s also of benefit when visualizing complex data, making it possible to test for accuracy in extreme situations.
• Scalability. In addition to data quality, data volume plays a critical role in training machine learning and artificial intelligence models. The scalability of simulated data elevates its value for such use cases—since the data is artificial, it can be generated as needed to reflect the randomness and complexity of real-world systems.
• Replicability. Similar circumstances and conditions can be reproduced in a different simulated dataset to ensure consistency in testing. This consistency is crucial for validating models and hypotheses, as it allows you to test them repeatedly and refine them based on the results.

Benefits of Data Simulation

Data simulation is just one tool in an enterprise’s larger data management toolbox. Depending on the use cases, there are numerous benefits to using it in the place of actual data—here are the most common.

Enhanced Decision Making

Data simulation can inform decision-making by simulating various conditions or events and predicting outcomes based on actions. This provides insight into hypothetical scenarios, allowing for the creation of suitable protocols for all possibilities.

Cost Efficiency

Using data simulation instead of harvested data is more cost-effective, as it reduces the need for physical testing and active data collection. Simulating different scenarios and observing their outcomes provides valuable insights without the need for costly and labor-intensive data collection efforts.

Improved Model Validity

Data simulation can aid in model testing and refinement. Creating a virtual representation of a real-world system makes it possible to test different models and refine them based on the results, leading to more accurate models that are better at predicting scenarios in great detail.

Risk Reduction

Data simulation can provide data on crises and potential issues, allowing organizations to identify pitfalls or challenges before they occur in the real world. This foresight can help mitigate risks and avoid costly mistakes.

Learn the best practices for effective data management.

Data Simulation Use Cases

Data simulation can be used in numerous applications across a wide variety of industries. But some industries rely more on data than others, making data simulation particularly beneficial for them.

Finance

In the finance industry, data simulation is primarily used for risk assessment and investment portfolio simulations. Analysts can test different scenarios to gauge potential risks and returns associated with a particular transaction or investment strategy. This helps them make more informed investment decisions and manage client portfolios more effectively.

Healthcare

Data simulation can be used in healthcare to train models for drug testing and epidemiological predictions. Data mimicking patterns of diseases spreading, for example, enables epidemiologists and healthcare professionals to estimate their impact and plan response plans accordingly. Drug simulations provide the opportunity to assess a drug’s efficacy and safety before beginning human trials.

Retail and Marketing

Data simulation can be used to predict customer behavior and optimize stock for purchasing trends in retail and e-commerce. By simulating customer behavior, retailers and marketers can predict purchasing trends and optimize stock levels accordingly, leading to improved customer satisfaction and increased profits.

Types of Data Simulation Models

There are multiple types of data simulation models, each with its own unique features and capabilities. Here are the most common:

• Monte Carlo simulations. This type of simulation uses random sampling to obtain results for uncertain situations and is widely used in finance, physics, and engineering to model complex systems and predict behavior.
• Agent-based modeling. This type of simulation focuses on the actions and interactions of individual, autonomous agents within the data systems and is particularly useful for studying complex systems where the behavior of the system as a whole is influenced by the behavior of individual components.
• System dynamics. System dynamics helps to understand non-linear feedback loops in more complex systems and is often used in economics, environmental science, and public policy to simulate complex systems and predict their behavior.
• Discrete-event simulations. These models focus on individual events in the system and how they affect the outcome, and are widely used in operations research, computer science, and logistics to simulate processes and systems.

Learn more: Data Modeling vs. Data Architecture

Data Simulation Software Providers

Various providers offer data simulation solutions, including commercial software such as MATLAB, Simul8, and AnyLogic Cloud. These tools provide a wide range of features, including graphical user interfaces, scripting languages, and extensive libraries of mathematical and statistical functions.

Open-source data simulation solutions often come in the form of libraries in languages such as Python and R. They’re freely available, widely used in the scientific community, and offer extensive libraries of mathematical and statistical functions. Because they’re highly customizable, they can be tailored to specific needs. Other open source simulation tools include OpenModelica, OpenSimulator, and Logisim.

Bottom Line: What Is Data Simulation?

Data simulation is a powerful tool for studying complex systems and predicting their behavior. It lets you simulate a wide range of scenarios, predict their outcomes, and test different models and hypotheses. Whether you’re a data scientist, a business leader, or a policy maker, data simulation can provide you with the insights you need to make informed decisions.

By using data simulation, you can enhance your decision-making, improve your models, and reduce your risks. With its flexibility, scalability, and replicability, data simulation is a valuable tool for anyone interested in understanding complex systems and making accurate predictions.

Read What is a Digital Twin? to learn how enterprises use virtual environments as another means of simulating real world conditions to test and monitor systems under controlled conditions.

Data governance is a holistic approach to your data—at its core, it refers to the set of rules and permissions by which the data is orchestrated and accessed. Master data management (MDM) is the overlap of technology and business operations to enforce the policies of data governance, ensuring the uniformity, accuracy, and accessibility of data.

This article explores the differences between data governance and master data management, where they overlap, and how they can be used together to help businesses ensure their data is accessible, reliable, and secure.

Jump to:

• The Challenge of Managing and Governing Data
• What is Data Governance?
• What Is Master Data Management (MDM)?
• Data Governance vs. Master Data Management: Key Differences
• Role of Technology in Data Governance and MDM
• Can Data Governance and Master Data Management Work Together?
• Bottom Line: Data Governance vs. Master Data Management

The Challenge of Managing and Governing Data

The challenges of managing and governing data go beyond the cost of storage to retain the massive volumes that businesses have come to rely upon for decision-making, operations, and competitive advantage. They also include security—through policies like the European Union’s General Data Privacy Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA), the regulatory landscape has redefined the baseline for adequate data protection and management practices.

Maintaining data quality is another concern. The accuracy and reliability of data are critical. If either suffers, the decisions they inform aren’t grounded in truth.

The intertwined concepts of data governance and master data management serve distinctive roles. Differentiating between them is essential to mastering your data landscape.

Learn the 10 Best Practices for Effective Data Management.

What is Data Governance?

Data governance is a holistic approach to the how, why, and by whom of the data in the care of your organization. At its core, data governance refers to the set of rules and permissions that determine how the data is handled and who gets to access it. These rules have an effect on the data’s availability, usability, integrity, and security.

Not just a technical endeavor, it’s also directly related to business processes and organizational culture. Think of data governance as a rulebook, dictating processes and policies to ensure the data is strictly accessed by authorized individuals and isn’t used in any way that could harm the organization, its stakeholders, or clients.

The Core Principles of Data Governance

While every organization’s data governance policies will depend upon its needs and goals, here are the core principles they should follow:

• Data Quality—Ensures the data is maintained to be as accurate, reliable, and actionable as possible.
• Data Access—Makes the data readily available to all who need access to it, creating a streamlined process for access requests and approval procedures.
• Data Privacy—Protects the data and personal information of clients, employees, and stakeholders.
• Data Protection—Maintains the security of the data from potential threats and malicious actors using security measures to protect data assets.
• Regulatory Compliance—Ensures that the data handling methods meet legal or jurisdictional requirements.

Benefits of Data Governance

In addition to helping organizations improve the processes around the core principles listed above by improving quality, enhancing security, and ensuring regulatory compliance, data governance also helps in other areas of the business.

For example, more accurate and reliable data better informs decision-making at all levels, leading to better outcomes. It also increases the agility of the business—in fluctuating market conditions, organizations are able to pivot more readily when they have a solid grip on their data.

Data governance isn’t purely an IT strategy. It’s an organizational imperative that ensures data, in all its forms, is treated with the importance and security it entails.

What Is Master Data Management (MDM)?

Master data management (MDM) is the overlap of technology and business operations to enforce the policies of data governance, ensuring the uniformity, accuracy, and accessibility of the enterprise’s data. By consolidating the data, MDM ensures it’s consistently synchronized across the various systems, bridging data islands to create a seamless flow of up-to-date data and information.

MDM combines software and other tools, processes, and the mechanisms outlined by an organization’s data governance policies to ensure that master data is governed and coordinated across the enterprise with high accuracy and integrity.

Components of MDM

MDM isn’t a single entity, but a combination of multiple technical and operational components. Each of the following plays a role in the data management process:

• Data Integration—Integrates incoming data from legacy systems, CRMs, ERPs, and external data feeds into a central repository to offer a comprehensive view.
• Data Quality—Ensures that the consolidated data maintains its quality by removing duplicates, rectifying errors, and filling in gaps to ensure a complete set.
• Data Governance—Controls who can access the data, how it’s categorized, and how often it gets updated.
• Business Process Management—Updates data seamlessly as processes change (for example, during a product launch or merger).

Benefits of Master Data Management

Master data management helps organizations by creating a single, reliable repository of information. This means higher quality data that’s devoid of redundancies and inaccuracies. It also streamlines business processes by consolidating the data and providing a unified view to remove roadblocks or discrepancies and allow for smoother operations in the supply chain, finance, and consumer relations management.

With reliable access to data-backed decisions, business leaders can craft strategies that are grounded in reality and aligned with goals and aspirations. MDM can also increase operational agility by ensuring businesses are highly adaptable, using real-time data access to shift strategies and responses as needed.

Master data management is not just an IT tool, it’s a strategic imperative. As organizations grapple with growing data complexities, MDM stands out as the beacon, ensuring data coherence, quality, and agility.

Data Governance vs. Master Data Management: Key Differences

While largely interconnected, master data management and data governance serve different facets of the data ecosystem. Here’s a look at what sets them apart.

Purpose and Objectives

Data governance focuses primarily on policies ensuring data integrity, security, and overall quality, treating it as one of the organization’s valuable assets. MDM, on the other hand, zeroes in on the various fragments of the data and how each of them should be precisely managed for comprehensive and reliable access to the data around the clock.

Processes Involved

The policies created by data governance are enforced organization-wide, often taking into account regulatory compliance. MDM is more process-centric, delving into data integration, quality assurance, and aligning data with business operations.

Stakeholders and Responsibilities

Data governance includes data owners, stewards, users, and stakeholders. It takes their input, both directly and indirectly, as to how the data is going to be kept accurate and safe from privacy violations. MDM, on the other hand, is solely concerned with input from the IT and business operations, allowing them to integrate data sets into necessary processes, upholding data standards, and refining business processes.

Role of Technology in Data Governance and MDM

Technology stands at the forefront of efficient data governance and master data management, and is often the catalyst for their effectiveness.

Organizations have access to specialized tools and platforms, allowing them to amass large amounts of data from varied sources while maintaining its integrity and long-term usefulness. But beyond the collection and storage of data, businesses need technological solutions to help them process and dissect data and extract actionable insights in real-time.

Combined with the advancements of processing algorithms and machine learning, the automation of repetitive tasks within data governance and MDM has become more affordable, making it more accessible to smaller organizations with limited resources.

The use of technology in data governance and management doesn’t only streamline operations—it also significantly reduces the rates of human error. On the security front, technological solutions allow for robust encryption of the data, alongside a myriad of protective and mitigative measures. This ensures the data remains shielded from potential breaches and leaks. It’s further aided by monitoring and reporting software that keeps an eye on the data both in-rest and in-transit at all times.

Can Data Governance and Master Data Management Work Together?

The fusion of data governance and MDM can lead to maximized outcomes. This is most evident when considering the overlap between the need to set and enforce solid policies while managing the data for processing and accessibility. Data governance and MDM are not only complementary—they thrive when implemented in unison.

Combining both concepts allows organizations to come up with frameworks that emphasize best practices while championing clear roles that uphold data quality and integrity. Through such integration, organizations can foster a seamless and efficient data management strategy that leverages the strengths of both disciplines.

Bottom Line: Data Governance vs. Master Data Management

Data governance is about managing data as an asset, while MDM is about creating a single, unified view of data. Despite the unique approaches of data governance and master data management and their myriad differences, they’re closely related and perform better when combined.

By understanding the key differences and similarities between these two disciplines, you can make more informed decisions about your data strategy and achieve better outcomes that allow you to effectively manage and govern your data, refining raw material into valuable insights that drive business growth and innovation.

Read about the 10 Best Master Data Management Tools for 2023 to learn how enterprises are implementing their own MDM strategies.

Jump to:

• How do Data Pipelines Work?
• The Importance of a Data Pipeline
• Data Pipeline Types
• Benefits of Data Pipelines
• Data Pipeline Examples and Use Cases
• Bottom Line: What is a Data Pipeline?

How do Data Pipelines Work?

A data pipeline has four main functions—ingesting, processing, storing, and outputting data—that work in concert to accomplish the task of moving data from one place to another while applying transformational changes to it.

• Ingestion. Data from one or multiple sources enters the data pipeline—it could be a live stream of data, or data that has been accumulating in a database for a period of time.
• Processing. Raw data is transformed and processed into a format that’s ready for analysis—the format depends upon the end goal.
• Storage. Processed data is delivered to its permanent storage location—a data warehouse or a data lake, for example.
• Output. Processed data is communicated to end-users—analysts, applications, or other data systems, for example.

Workflow of a Data Pipeline

The workflow of a data pipeline is the series of steps and processes it follows as it migrates data from the source database to the destination. Despite following the same primary steps and using—more or less—the same components, every data pipeline’s workflow can vary depending on the business’s needs and requirements.

Factors such as data format, volume, and desired processing complexity all influence the design, leading to custom configurations for different applications. Throughout the journey from the data source, processing, and output, sophisticated mechanisms within the pipeline are responsible for detecting and rectifying failures, gaps, or errors in the data, ensuring the final product’s quality and integrity.

There are five main components critical to every data pipeline.

Storage

Storage is where the data, either raw or processed, resides, and ranges in size and type depending on the need. Typically, data pipelines use traditional databases, cloud repositories, or modern distributed storage systems that can handle live data.

Pre-Processing

Before analysis, data—especially if it’s raw—needs to be cleaned, normalized, and transformed in preparation. The pre-processing stage encompasses all these operations, transforming all input data into a format more amenable to analytics.

Analytics 

At this stage, pre-processed data undergoes rigorous examination using various statistical tools, often using machine learning algorithms. The goal is to extract meaningful patterns, trends, and actionable insights.

Applications

This component integrates the data with various applications, from business intelligence tools to artificial intelligence and machine learning models. It also enables users to access and apply data insights to real-world scenarios.

Delivery

The final stage of a data pipeline is how the data or insights reach end-users. It could be done through dashboards, reports, or automated systems, allowing data pipelines to bridge the gap between data processing and consumption.

Read about the best tools for data analytics.

The Importance of a Data Pipeline

Data pipelines aren’t just a complementary addition to an organization’s data ecosystem—they play a critical role in data management, data processing, and data migration, ensuring data quality and adequate integration. They meticulously handle errors and inconsistencies in raw data coming from various sources and at varying rates.

By harmonizing the data, a pipeline offers a holistic perspective of the business that can be used to fuel decision-making. Additionally, raw data can be morphed through advanced processing into actionable insights and information that cater to the business’s specific needs, circumstances, and place in the industry.

Data Pipeline Types

While they tend to follow a standard format, data pipelines come in various types depending on how they process data.

Batch Data Pipelines

Batch data pipelines process data in batches or groups at predetermined intervals. This type is best used to deal with large quantities of data—however, they tend to lack real-time responsiveness. Examples include daily sales reports and monthly financial statements.

Real-Time Data Pipelines

Real-time data pipelines process data as soon as it’s generated by the source. This allows them to provide immediate insights in real-time—but this can be resource-intensive and more complex to set up. Examples include real-time stock market predictions and social media feed analytics.

Hybrid Data Pipelines

As the name suggests, hybrid data pipelines are a combination of batch and real-time data pipelines. They can seamlessly switch between batch and real-time processing on-demand. Examples include systems that require analysis of legacy data as well as real-time alerts.

Benefits of Data Pipelines

Data pipelines offer a host of benefits, making them a valuable asset for any data-driven organization. Here are some of the most common:

• Enhanced decision-making. Data pipelines facilitate decision-making through data-driven insights and advanced predictive analytics of both legacy and real-time data. They provide businesses with a clear picture of their operations, helping them identify trends, patterns, and anomalies.
• Time and cost-efficiency. After the initial setup, data pipelines automate the data processing and migration process, reducing the need for frequent manual intervention and leading to significant time and cost savings and improved accuracy and reliability.
• Improved data management. Choosing the right type of data pipeline can contribute to improved data management and governance. It can also help businesses comply with various data regulations, reducing the risk of non-compliance penalties.

Data Pipeline Examples and Use Cases

While other options and alternatives exist, data pipelines remain the most-used solution across various industries, thanks to their flexibility and customizability.

e-Commerce Data Pipelines

In the e-commerce industry, data pipelines enable companies to track behavior of consumers while they browse the business’s website. This enables the business to personalize marketing and promotions according to each customer’s unique experience. Additionally, it can aid with inventory management, ensuring optimal stock levels and reducing out-of-stock situations.

Financial Services Data Pipelines

Data pipelines can help aid in real-time fraud detection and risk assessments, both of which are crucial in the financial services sector. They analyze large volumes of transaction data in order to identify suspicious activity and assess the risk of each process, contributing to safer and more secure financial operations.

Bottom Line: What is a Data Pipeline?

Data pipelines are systems that ingest, process, store, and output data into the target locations. They ensure data quality and integration whilst enhancing decision-making and improving overall operational efficiency. From e-commerce to finance, data pipelines are able to transform data operations, allowing companies to remain competitive in their industries.

Learn more about what data pipelines have in common with extract, transform and load (ETL), and when to choose one process over the other.

Jump to:

• What is ETL?
• What is a Data Pipeline?
• ETL vs. Data Pipeline: Key Differences
• Transformation Process: ETL vs. Data Pipelines
• Examples of ETL vs. Data Pipeline
• Tools and Technologies: Popular ETL and Data Pipeline Platforms
• Bottom Line: ETL vs. Data Pipeline

What is ETL?

Extract, Transform, and Load, or ETL, refers to a three-step process used in data management and warehousing during which data gets consolidated from a variety of sources and transformed into a single, unified format that aligns with business rules. Together, these steps prepare the data for refinement and further analysis for business intelligence (BI) purposes while ensuring that data quality remains uncompromised.

• Extract. Data is sourced from various databases and platforms from around the organization’s ecosystem. The format may differ depending on its source, from structured data found in SQL databases to unstructured data like weblogs and social media feeds.
• Transform. In this refinement and modification stage, data is converted and restructured into the desired format. This includes a wide range of processes to clean and deduplicate the data and address any missing or null values it contains.
• Load. Data is inserted into a target database or data warehouse for long-term storage and accessibility where it can be queried and analyzed as needed.

When to Use ETL

ETL is indispensable in data management. For example, it ensures consistency and accuracy when attempting to handle large volumes of data being rapidly updated and moved around. ETL tools can address data anomalies early in the process, creating a more standardized format of the data and streamlining analysis.

It’s not just a technical process—it’s also a strategic one. ETL is often used in large-scale data migration projects where organizations need to transform data from legacy systems to suit their management and storage solutions. ETL can ensure that the data is clean, well-integrated, and actionable.

What is a Data Pipeline?

A data pipeline is a comprehensive system of processing elements and steps leading to a centralized destination that primes data from multiple sources and in a wide range of formats for storage and analysis. Typically, data pipelines help with error handling and data integration by offering a holistic approach to data management and analytics.

Data pipelines can be divided into multiple types based on their primary processing mode and data handling approach—either real-time or batch processing, and streaming or non-streaming. They’re incredibly versatile, as the different operations in the pipeline can be exchanged, tweaked, or reordered to suit the needs of different departments in the organization.

Typically they are made up on three main components:

• Data source. This is where the data is generated, creating the contents for the pipeline—it can vary from structured to unstructured data and, in some instances, dynamic, real-time data streams coming from Internet of Things (IoT) devices and social media feeds.
• Processing. This is the heart of the pipeline where operations are applied to ingested data. These operations encompass tasks like data cleaning to ensure purity, validation for accuracy, aggregation for coherence, or transformation to make it ready for analysis.
• Data destination. This is the final stop where data is moved to be stored. It can be anything from a high-performance database to a consolidated data warehouse or data lake, all of which can be optimized for big data analytics.

ETL vs. Data Pipeline: Key Differences

ETL and data pipelines do essentially the same type of work, but they come at it from different angles—here’s a closer look at where and how they diverge.

Purpose and Function

While both are used for data processing, their purposes and functions differ. ETL is most commonly used for batch processing thanks to its ability to handle large volumes of data and focus on data cleaning and preparation for analysis. Data pipelines, on the other hand, are more versatile and capable of handling both batch and real-time data processing streams.

Flexibility and Scalability

Data pipelines have an edge over ETL tools when it comes to flexibility and scalability. ETL processes are typically linear and follow a fixed sequence, while data pipelines can be simple and straightforward or be made more complex with multiple branching and merging paths. This makes them more adaptable to changing data environments.

Complexity and Implementation

ETL processes are generally much easier to implement than data pipelines, which range greatly in complexity due to their flexibility and versatility. However, it’s important to note that the complexity of each solution depends on the desired outcome of the processed data.

Transformation Process: ETL vs. Data Pipelines

The transformation process is pivotal in data processing and results in different outcomes in both ETL and data pipelines because of their unique approaches that cater to different needs.

ETL Transformation

The ETL process predominantly relies on a centralized data transformation process. As soon as the data is extracted from its source, it undergoes transformation before it gets loaded to its final destination. The transformation itself encompasses a variety of operations, depending on the data source and type.

For example, it might include data cleaning to fix anomalies and errors, data enrichment to fill in missing gaps, and aggregation to summarize specific data points. The primary objective behind the transformations in ETL solutions is to make raw data suitable for analysis, ensuring it’s clean, consistent, and seamlessly integrated.

Data Pipeline Transformation

Data pipelines adopt a more decentralized approach to data transformations, which allows data to undergo multiple transformations at different points throughout the pipeline. This decentralization allows pipelines to be more flexible and scalable, which is particularly beneficial with fluctuating rates of live data.

Depending on the intricate demands of the data processing task, the nature, sequence, and complexity of the transformations can be tailored accordingly. This ensures that data is processed in a way that is most congruent with the end-use requirements, whether that’s for analytics, machine learning, or other data-driven applications.

Examples of ETL vs. Data Pipeline

While both ETL and data pipelines can be used in a wide variety of circumstances, for certain applications one solution is preferred over the other.

ETL Examples

ETL tools are widely used in data migrations, particularly when an organization switches from a legacy platform to a more advanced one. The company likely has years of data stored across numerous databases and systems, and the main objective is to consolidate this data into a location more suitable for analysis and decision-making.

This is where ETL tools become indispensable, as they can pull data directly from the legacy systems and storage environments—including data that contains inconsistencies, redundancies, or errors. In the transformation process, the data is cleaned, standardized, and enriched to align with current data requirements. The ETL tool would then migrate the data into the new environment for use and analysis.

Data Pipeline Example

Data pipelines are often used by companies that need to process large amounts of live data rather than bulk process stored data—for example, in a streaming platform like Spotify or Netflix. Business models that rely on real-time user engagement need access to tools that handle continuous data streams.

Unlike an ETL, a data pipeline enables companies to continuously process and analyze large streams of data as it’s being produced, resulting in real-time analysis. If a user frequently listens to a particular genre of music or watches one type of show, the platform would be able to immediately recommend new content.

Tools and Technologies: Popular ETL and Data Pipeline Platforms

Neither ETL or data pipeline solutions are new—both have been around long enough for there to be a wide range of options available on the market. They range in specialty, complexity, and cost.

ETL Tools

Traditional ETL tools were used for data management before cloud computing and big data came along. They’ve evolved to keep pace with technology, making them indispensable for the modern business. Here’s a look at some of the most widely used:

• Informatica PowerCenter. A renowned ETL tool used by enterprises in data integration, Informatica PowerCenter is compatible with a variety of platforms and offers numerous rich features that ensure complex integration and data reliability across diverse ecosystems.
• IBM InfoSphere DataStage. Part of the IBM Information Platforms Solutions suite, this ETL tool allows users to seamlessly integrate data from myriad sources, making it particularly useful for organizations with vast data landscapes. It’s highly adaptable and compatible with many IBM solutions.
• Oracle Data Integrator (ODI). A holistic ETL platform that employs a graphical environment to build, manage, and maintain data flows, ODI’s versatile nature means it caters to both conventional and modern data environments.

Data Pipeline Tools

At a time defined by real-time insights and data flows, the traditional approach of batch processing often falls short—this is where data pipeline solutions come into play. Here are some of the most widely used:

• Apache Kafka. This free and open-source platform for building tailored, real-time data pipelines and applications is best at processing fast-moving, vast volumes of data in near real-time.
• Google Cloud Dataflow. This fully managed Apache Beam pipeline management service is a solid choice for acquiring low-latency insights, ensuring businesses are able to gain access without the typical lag associated with batch processing.
• Amazon Kinesis. Part of Amazon Web Services (AWS), this collection of data management services is adept at real-time data streaming and is able to process massive amounts of data from numerous sources simultaneously. This makes it indispensable for businesses operating globally.

Bottom Line: ETL vs. Data Pipeline

ETL and data pipelines are two different methods for data processing with distinct functionalities designed to meet different use cases. ETL tools take a more traditional approach that tends to be better suited for batch processing large volumes of data. Data pipelines are flexible and can handle real-time data processing and streams.

Choosing between them should depend on multiple factors, such as your specific needs and the nature of your data. When dealing with large volumes that need cleaning and standardization, ETL is the best option. However, real-time data processing from multiple sources can easily overwhelm ETL solutions, making data pipelines the ideal alternative.

As the data landscape continues to evolve, expect to see new developments in both ETL and data pipelines, making them even more efficient and effective at handling the ever-increasing volumes of data businesses rely upon for decision-making and competitive advantage.

To learn more about enterprise approaches to data management, read The Future of Data Management next.

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. 

Data Lakehouses vs. Data Lakes vs. Data Warehouses

A data lakehouse is a new data architecture that combines the best features of data lakes and data warehouses into a single, centralized platform to store and handle data. Designed to address the weaknesses of the two, this comprehensive data platform can perform advanced analytics and generate valuable real-time insights by supporting the entire lifecycle of data processing for continuous streams of real-time and historical data.

Data lakes are vast repositories of raw data in its native format. Primarily designed for the storage of unstructured data—data generated by Internet of Things (IoT) devices, social media posts, and log files, for example—they are well-suited to storing store large volumes of data at a relatively low cost, but lack the capacity to process and analyze that data. Data stored in lakes tends to be disorganized, and because they require the use of external tools and techniques to support processing, they’re not well-suited for business intelligence (BI) applications and can lead to data stagnancy issues—sometimes referred to as “data swamps”—over time.

Data warehouses, on the other hand, are designed for the storage, processing, and analysis of large volumes of data—primarily structured data like information from customer relationship management systems (CRMs) and financial records. They excel at handling structured data, but are generally not as useful for unstructured data formats. They’re also inefficient and expensive for organizations with constantly expanding data volumes.

Data lakehouses bridge the gap by combining the storage capabilities of a data lake with the processing and analytics capabilities of a data warehouse. A data lakehouse can store, process, and analyze both structured and unstructured data in a single platform.

Learn more about data architecture vs. data modeling.

Key Features of a Data Lakehouse

Data lakehouses can facilitate high-speed data queries and other data processing efforts, consolidating data from multiple sources and in multiple formats in a single, flexible solution. Here are some of the key features that set them apart from other storage solutions:

• Unified data architecture. Data lakehouses provide a unified and centralized platform for the storage, processing, and analysis of both structured and unstructured data.
• Scalability and flexibility. Due to data lakehouses’ ability to handle vast volumes of data, they’re also capable of exceptional scalability, enabling businesses to increase their data capacity based on demand.
• Advanced analytics support. Data lakehouses can facilitate advanced analytics, including machine learning and artificial intelligence, on stored data.

Benefits of a Data Lakehouse for Business Operations

Why choose a data lakehouse over a data lake or data warehouse? They can be used across a wide range of industries to help enterprises meet their data processing and business intelligence needs. In the healthcare sector, for example, data lakehouses are used to store and keep track of patient data, enabling healthcare providers to deliver personalized care. In the finance industry, data lakehouses are used to manage and analyze transaction data, helping financial institutions detect fraudulent activities.

Here are few of the key benefits of data lakehouses for enterprise use.

Simplified Data Management

In traditional data warehouses, data needs to be transformed and loaded before analysis, while data lakes are raw and lack schema enforcement. Data lakehouses, on the other hand, enable businesses to ingest and store both types of data in the same location, simplifying the process of needing to manage multiple storage technologies. This enables businesses to focus on data-driven decisions more effectively.

Improved Data Accessibility and Collaboration

Data lakehouses facilitate data accessibility and collaboration across the various departments of an organization thanks to centralizing the repository of the enterprise data. This lets employees access a much wider range of data sets without the need for complex data request procedures or access permissions. This also enables teams to work together more efficiently by letting analysts, data scientists, and business users collaborate on data exploration, analysis, and visualization during the decision-making process.

Scalability and Cost Efficiency

When combined with cloud-based storage and cloud computing, data lakehouses allow businesses to easily scale their data infrastructure based on demand. As the volume of data grows, the architecture can expand to handle the influx of data with minimum disruptions or last-minute hardware investments. Most data lakehouse providers offer pay-as-you-go models for cost efficiency, as businesses only pay for the resources they use. This eliminates the need for expensive, upfront infrastructure costs, making it suitable for businesses of all sizes.

Real-time Analytics and Processing

Using data lakehouses, organizations can perform real-time data analytics and processing, generating immediate insights and responses to changing market conditions and customer purchasing behaviors and trends. This capability is particularly important for industries that rely on up-to-date information, such as retail, finance, and telecommunications. By harnessing real-time data, they can better optimize operations, personalize customer experiences, and gain a competitive edge in the dynamic market landscape.

Data Lakehouse Architecture

Building a data lakehouse structure from scratch can be a complicated task. For many enterprises, paying for the service from a vendor will be a better option. Databricks is one of the better known data lakehouse providers; others include Amazon Web Services (AWS), iomete, Oracle, and Google. There are also hybrid solutions that allow more control over the data lakehouse structure while working alongside a cloud provider for easier implementation.

At a high level, five levels comprise data lakehouses:

• Ingestion. This layer uses a variety of protocols to connect to disparate external sources, pull in the data, and route it to the storage layer.
• Storage. This layer keeps all the data (both structured and unstructured) in affordable object storage, where it can be accessed directly by client tools.
• Metadata. This layer deploys a unified catalog to provide information about all the data in the storage layer, making it possible to implement data management.
• Application Programming Interface (API). This layer serves as a host layer for the APIs that are used to analyze and process the data.
• Consumption. This layer is where client applications perform BI, visualization, and other tasks on the data.

While each layer is essential to the architecture, the metadata layer is the one that makes data lakehouses more useful than either data lakes or data warehouses. It allows users to apply data warehouse schemas and auditing directly to the data, facilitating governance and improving data integrity.

Bottom Line: The Future of Data Lakehouses

Data lakehouses are a relatively new architecture, but because they provide a single point of access to an organization’s entire data stores, their future looks promising. As businesses continue to generate vast amounts of data, the need for a unified data platform like a data lakehouse will only increase.

Enterprises already using data lakes will find the shift to a data lakehouse can provide better data processing capabilities while creating cost efficiencies over a data warehouse. Opting for a single platform can also cut down on costs and redundancy issues caused by using multiple data storage solutions. A data lakehouse can also support better BI and analytics and improve data integrity and security.

Advancements in technologies like machine learning and artificial intelligence will only increase the capabilities of data lakehouses, and as they become more intelligent and better able to automate data processing and analysis, they’ll become more useful enterprises hungry for more insights to give them a competitive advantage.

Read next: Data Management: Types and Challenges

Jump to:

• Understanding Data Architecture
• Understanding Data Modeling
• The Importance of Data Storage and Organization
• Data Architecture vs Data Modeling: Key Differences
• The Importance of Data Modeling in Data Architecture
• Choosing Between Data Architectures and Data Modeling
• Bottom Line: Data Modeling vs Data Architecture

Understanding Data Architecture

Data architecture is a complex discipline that pertains to the creation, design, deployment, and management of an organization’s data. It consists of models, policies, standards, and rules that manage how data is collected and stored. You can think of data architecture as the blueprint for managing data across the enterprise and ensuring there are no inconsistencies or redundancies in collected and stored data.

Data architecture encompasses a wide range of components, including data models, data integration, data storage, and data governance. The specifics of the architecture can vary widely depending on the needs of the organization, but the overarching goal is always the same: to facilitate the effective use of data.

A data architect is the person or group of people responsible for setting up data infrastructure and ensuring the data is accurate, accessible, consistent, and secure at all times. Data architects often collaborate directly with other professionals, such as analysts, engineers, and business leaders, to fully comprehend data requirements and translate them into scalable and secure solutions.

Their expertise lies in selecting the appropriate data storage technology for the job that also integrates well with desired data governance practices. The aim is to optimize data flow among all parts of the enterprise without compromising on quality, security, or compliance. Data architects are the intermediaries between technical data teams and business stakeholders; their goal is to make sure all data-related decisions align perfectly with the organization’s goals.

Understanding Data Modeling

Data modeling is the process of creating a visual representation of the data stored and governed by data architecture components and policies. It offers a conceptual representation of data objects, illustrating the relationships between different data points and the rules governing them. It helps in the visual representation of data and enforcement of business rules, compliance laws, and government policies.

Data modeling is usually completed with the help of specialized data modeling software. This helps ensure consistency in naming conventions, default values, and semantics, allowing for a more readable and accurate final result. The information represented in the data model can then be used to define relational tables and establish primary and foreign keys to describe relationships between information in different tables.

A data modeler’s skill set typically includes a deep understanding of data structures and how these structures relate to business operations. They are responsible for adequately representing data in a business context, they need to be able to understand relationships among different data elements, they must have a grasp on the rules governing these relationships, and they have to communicate effectively with business stakeholders on these points.

The Importance of Data Storage and Organization

Data storage, organization, and governance are foundation elements of both data architecture and data modeling. If you lack a logical and effective system for storing and categorizing data, your organization’s digital assets could quickly become an illegible liability.

While data storage refers to where and how data is kept for both the short and long term, which could include on-site servers or cloud-based storage, data organization refers to how the data is structured and accessed within the storage system. The choice of both a data storage system and an organization method should depend on the specific needs of your organization and the nature of its data.

Data Architecture vs Data Modeling: Key Differences

While data architecture and data modeling are terms that are often used interchangeably, they represent different aspects of data management. It’s crucial to understand the key differences between both concepts in order to make the most out of your available and future data. Some of the key differences include:

Scope and Focus

One of the main distinctions between data modeling and data architecture lies in their scope and focus. Data architecture is considered to be a much broader discipline that encompasses numerous design and data management elements of an organization’s data infrastructure. Its main focus is how the data is collected, stored, and governed. Essentially, it addresses “what” the data ecosystem of an enterprise should look and behave like, and its scope includes the entire data infrastructure of an organization.

Data modeling, on the other hand, is a type of activity conducted within the bounds of the data architecture. It focuses on creating detailed representations of datasets and modeling their relationships, constraints, and attributes within a scope determined by the data analyst or engineer in charge. It generally operates on a lower level of abstraction than data architecture, answering questions pertaining to “how” data should be organized and structured in relation to the organization’s goals and objectives.

Roles and Responsibilities

Within the same data management team, the roles and responsibilities for data management vs. data architecture are vastly different. While data architects are responsible for designing the overall data infrastructure, including developing the necessary data strategies and compliance structures, data modelers and analysts are responsible for the accuracy of data when it’s represented and presented to stakeholders and other data management team members.

Both work hand-in-hand to ensure that data-related decisions support the organization’s objectives and can be translated from raw material into practical implementations. They focus more on the details of the modeled data and aim for results that are well-structured, efficient, and scalable.

Included Activities

Data architecture tends to involve a much wider range of activities compared to data modeling. Data architects collaborate directly with stakeholders to understand various business requirements and data sources, as well as how to best organize them within data infrastructure. They play a key role in selecting appropriate data storage technologies, implementing integration methods, and establishing data governance policies.

Data modeling activities center around creating accurate representations of data using a variety of techniques and notations. Example activities include using unified modeling language (UML) diagrams and entity-relationship diagrams (ERDs) in representing structure, relations, and individual entities.

Levels of Abstraction

Another key difference between data architecture and modeling is the level of abstraction at which they operate. Data modeling tends to be more detailed and specific in its objectives. Operating at a lower level of abstraction enables data modelers to deal with the fine-grained designs of data points and tables. They create conceptual, logical, and sometimes-physical data models to translate the stakeholders’ strategic vision into easy-to-comprehend and practical models.

Data architecture, in contrast, is more abstract and conceptual than data modeling. It primarily concerns itself with data strategy, policies, and frameworks to govern the data management process. This allows it to work in a broader context when addressing enterprise-level data organization and monitoring needs.

The Importance of Data Modeling in Data Architecture

Data modeling plays a critical role in making the most out of data architecture, serving as a foundational element for ensuring the effectiveness, efficiency, and sustainability of the infrastructure.

Precise Data Design

Data modeling enables data architects to design precise data structures, helping them keep all data entities and their attributes well-defined by eliminating ambiguity in data representation. Data modelers can also establish and examine relationships between data entities, providing valuable insights into data-dependent operations.

Data Integrity and Consistency

By enforcing strict data integrity constraints within the data model, data architects are able to maintain consistency and accuracy across the entirety of the data ecosystem. This results in more reliable and trustworthy data, which is vital for making informed business decisions and investments.

Data System Integration

In more complex data ecosystems, data modeling facilitates seamless integration between different data systems. With their understanding of the structures and relationships between data points and tables, data architects can design for integration, enabling smooth data flow between the systems and supporting data accessibility and consistency among users.

Choosing Between Data Architecture and Data Modeling

Between data architecture and data modeling, the choice is rarely an either/or situation. Both are essential components of effective data management on an enterprise scale. The key is to understand how both concepts fit with and complement each other in regard to your data ecosystem.

Between focusing on data architecture or data modeling, the final decision should depend on your role within the organization and the specific needs of your data management strategy. If you’re responsible for the overall strategy and framework for data usage across the organization, then your focus will likely be on data architecture.

Data architects are pivotal to aligning data management with business goals as they define data storage technologies, integration methods, and security measures. This work is done by collaborating with stakeholders to understand business requirements.

As for organizations with data management strategies that require more specific focus on individual databases and their underlying structures, data modeling should be their main objective. Data modelers work closely with data architects, developers, and database administrators to create detailed representations of data entities, attributes, and relationships. They design efficient and well-structured databases or data structures in order to maintain data integrity, consistency, and accuracy.

Bottom Line: How to Effectively Combine Data Architecture and Data Modeling 

Data architecture and data modeling are two sides of the same coin. They both play crucial roles in effective data management and usage. While data architecture provides the overarching framework and strategy for data usage, data modeling provides the necessary detail and precision to implement this strategy effectively.

To effectively use and manage data on a large scale, an organization frequently must use data architecture and data modeling strategies in conjunction. Together, they can ensure that data is stored, organized, and used effectively.

A strong data architecture provides the framework and strategy for data usage across the organization. It sets the direction for and provides a big-picture view of the organization’s data landscape.

Within this framework, data modeling provides the detail and precision needed to manage the data architecture effectively. It defines how data is stored, organized, and used within specific databases, ensuring that the data architecture is implemented in a consistent and predictable way both on a broad scale and at a granular level.

Read next: Data Analytics vs. Data Science

What is Structured Data? Types & Examples

Structured data, or quantitative data, is highly organized and readable by machine learning algorithms, making it easier to search, manipulate, and analyze. Structured data can include names, addresses, dates—fields that are recognizable and searchable by computers.

Compare this to unstructured data, which includes everything from social media posts to music files, emails, and images. It’s estimated that unstructured data makes up between 80 and 90 percent of all data generated globally.

Despite making up a much smaller percentage of existing data, structured data is considerably more valuable, as it’s much easier to handle and extract insights from.

The two types of data are not in opposition. In fact, structured data complements unstructured data, and enables you to find insights in your unstructured datasets.

For example, structured data records can hold unstructured data within them. Consider a form that offers questions with a list of answers available in a dropdown menu but also allows users to add free-form comments. The answers generated from the pick list are structured data, but the comments field yields unstructured data.

To some degree, most data is a hybrid of unstructured and structured data. Semi-structured data is a loosely defined subset of structured data, and includes the capability to add tags, keywords, and metadata to data types that were once considered unstructured data—for example, adding descriptive elements to images, emails, and word processing files. Markup languages such as XML are often used to manage semi-structured data.

How Does Structured Data Work?

You’ll typically find structured data in tables, rows, and columns, with each field containing a specific type of data corresponding to its category and value—think of a spreadsheet with specific headings for each column. This format makes it possible for search engine algorithms to read and understand data posted by individual sites.

Additionally, structured data enables both tools and individuals to quickly scan, organize, and analyze vast amounts of data for information.

Structured data can be generated in a number of ways. It can come from enterprise software, such as customer relationship management (CRM) systems, accounting programs, and other applications used in critical business operations. It can also be generated from online sources, including social media platforms and web-based surveys. Another source, while fairly limited in scope and application, is data coming from manual human input.

As for extraction from pre-existing material, structured data can be extracted from unstructured data by using a variety of business intelligence (BI) tools that rely on artificial intelligence (AI) and natural language processing (NLP) to transform massive amounts of unstructured data into structured data. You may come across structured data in various formats, depending on what makes it most accessible and easy to compare and contrast with other datasets.

The key to understanding structured data lies in its name: structure. It follows a specific format and organization, making it easier for machines to read and process data. This structure is usually predefined and consistent, meaning it uses the same format across all instances of the data.

Why Is Structured Data Important?

Structured data plays a pivotal role in numerous sectors, from business and finance to healthcare and education. It facilitates data analysis, enabling organizations to extract meaningful insights from their data. These insights can then be used to drive decision-making processes, optimize operations, and predict future trends.

Structured data can help organizations improve customer service by allowing them to easily access customer information. This can help them to quickly identify customer needs and preferences, allowing for more personalized experiences and building customer loyalty. It enables organizations to better understand their customers and target marketing efforts appropriately. Structured data also makes it easier for organizations to track performance metrics and identify areas where employees are eligible for improvement.

When it comes to search engine optimization (SEO), structured data can help you significantly boost a website’s visibility and reach. It allows search engines to better understand the content of pages, improving their chances of ranking higher in search results. Google, for instance, “uses structured data to understand the content on the page and show that content in a richer appearance in search results, which is called a rich result.” The aim is to encourage users to click the most suitable links, increasing click-through rates for websites with valuable content as a result.

Moreover, structured data is crucial for data interoperability. It ensures information is consistently formatted and easily exchangeable between different systems or applications. This is particularly important in today’s interconnected digital ecosystem, where the ability to seamlessly share and integrate data from multiple sources can streamline operations and foster deeper collaboration.

Where Does Structured Data Come From?

The two primary examples of where structured data is generated are databases and search algorithms.

The term structured data is often associated with relational database management systems, which organize data into one or more tables—also known as relations—of columns and rows. The structured query language (SQL) is used in the vast majority of relational databases.

In addition to relational databases, spreadsheets are common sources of structured data.

Whether it’s a complex SQL database or an Excel spreadsheet, because structured data depends on users to create a data model, you must plan for how you will capture, store, and access data. For example, will you be storing numeric, monetary, and/or alphabetic data?

To create a structured data standard for web-based applications, email messages, and forms of internet content, Google, Microsoft, Yahoo, and Yandex created Schema.org, an open community that includes encodings such as RDFa (an HTML5 extension used in both the head and body sections of the HTML page), Microdata (an open HTML specification used to include structured data in HTML content), and JSON-LD (JavaScript Object Notation for Linked Data).

What Are Common Sources of Structured Data?

Unlike unstructured data, which will grow organically—often uncontrollably—and come from a wide range of sources, structured data is often created in controlled spaces and through planned methods. Usually, those can be divided into two categories: hardware and software.

Some of the most common sources include:

Databases

Databases are a common source of structured data. They store data in tables, rows, and columns, making it easy to search and analyze data.

Spreadsheets

Spreadsheets are another common source of structured data. They allow you to organize data in a grid of cells, with each cell containing a specific piece of information. This is often the data format that software and hardware products are programmed to produce, creating a wealth of structured data from scratch.

Sensors

Sensors produce data, such as temperature readings or GPS coordinates, which are types of structured data. Sensors collect data in a structured manner, making it easy to analyze and interpret the data. These sensors may cover global networks, such as ones for weather forecasting, but there’s also smaller and individual sensors, like the ones used in logistics and transportation to track items.

5 Structured Data Examples

Not all data can be organized into a neat and easy-to-comprehend system; unstructured data can often be hard to quantify. Meanwhile, structured data is often numerical data or clear-cut strings of information, such as:

1. Dates and Times

Dates and times follow a specific format, making it easy for machines to read and analyze them. For instance, a date can be structured as YYYY-MM-DD, while a time can be structured as HH:MM:SS. Both can be transformed into different iterations of the same format so they become accessible to data scientists from different cultural and linguistic backgrounds.

2. Customer Names and Contact Information

When you sign up for a service or purchase a product online, your name, email address, phone number, and other contact information are collected and stored in a structured manner. This allows businesses to easily manage and analyze customer data, thereby enhancing their customer relationship management (CRM) efforts.

3. Financial Transactions

Financial transactions, such as credit card transactions, bank deposits, and wire transfers, are all examples of structured data. Each transaction comes with specific information in the form of a serial number, a transaction date, the amount, and the parties involved. This information is structured and stored in databases, enabling banks and financial institutions to track and analyze financial activities.

4. Stock Information

Stock information, such as share prices, trading volumes, and market capitalization, is another example of structured data. This information is systematically organized and updated in real time. It enables investors and traders to make informed decisions based on the latest versions of data collected from the market.

5. Geolocation

Geolocation data, such as GPS coordinates and IP addresses, is often used in various applications, from navigation systems to location-based marketing campaigns. This data helps businesses understand where their customers are located, thereby helping them tailor their services or products to specific geographical areas.

Advantages of Structured Data

Despite making up a small percentage of all data generated globally, structured data is highly sought-after due to its value and importance for business decisions. Some of its advantages include:

Simplifies Search and Analysis

One of the main advantages of structured data is that it’s easy to search and analyze. Its organized nature allows data analysis tools to quickly scan and interpret the data, thereby speeding up the data analysis process.

Enhances SEO

Structured data can enhance SEO efforts and enable search engines to better understand the content of webpages, potentially leading to higher search rankings and improved visibility.

Facilitates Data Integration

Structured data facilitates data interoperability, ensuring information is consistently formatted and easily exchangeable between different systems or applications.

Disadvantages of Structured Data

It’s important to also be aware of the various disadvantages and limitations of structured data in order to work around them and prepare for any shortcomings you may encounter.

Limited Flexibility

One of the main disadvantages of structured data is its limited flexibility. Since it follows a specific format and structure, it can be challenging to accommodate data that doesn’t fit into these predefined categories, therefore, limiting the data’s growth potential.

Time-Consuming to Set Up

Setting up a structured data system can be time-consuming and requires a significant amount of planning and coordination. You need to define the structure of the data beforehand, which can be a complex task, especially for large datasets.

Risk of Data Silos

There’s a risk of creating data silos with structured data, especially in large organizations where different departments may use different systems to store and manage data. This can make it difficult to share and integrate data across the organization.

3 Structured Data Characteristics

It’s often a fine line between structured and unstructured data, depending on its source, organization method, and the software and expertise you have on hand to handle it. However, there are a number of characteristics that are unique to structured data, such as the following:

Organized and Categorized

Structured data is organized. It follows a specific format and structure, making it easy for machines to read and process the data.

Consistent

Structured data is consistent. It uses the same format across all instances of the data, ensuring data is consistently formatted and easily exchangeable.

Easily Searchable

Structured data is searchable. Its organized nature allows data analysis tools to quickly scan and interpret the data, thereby speeding up the data analysis process.

Bottom Line: Structured Data

Structured data is a crucial component of any company’s big data landscape. It’s organized, searchable, and easy to analyze, making it an incredibly valuable asset for businesses, organizations, and individuals alike. Understanding structured data is just the first step toward being able to use it to the fullest. The real value lies in how you adopt this data to drive decision-making, optimize operations, and enhance customer and client experiences.

Read next: Structured vs. Unstructured Data: Key Differences Explained

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?

This ultimate guide explains everything you need to know about cloud computing, including how it works, the difference between public and private clouds, and the benefits and drawbacks of different cloud services.

Jump to:
What Is Cloud Computing?
Cloud Computing Services
Public vs. Private vs. Hybrid Cloud
Cloud Computing Benefits
Cloud Computing Drawbacks
Cloud Security
Bottom Line: Cloud Computing

What Is Cloud Computing?

There are many definitions of cloud computing, but the most widely accepted one was published in 2011 by the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) and subsequently summarized by Gartner as “a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies.”

NIST’s longer definition identifies five “essential characteristics” shared by all cloud computing environments:

• On-demand self-service: Consumers can unilaterally provision computing capabilities (such as server time and network storage) as needed.
• Broad network access: Capabilities are available over the network and accessed through standard mechanisms.
• Resource pooling: Resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand to allow for location independence and high resource availability.
• Rapid elasticity: Capabilities can be elastically provisioned and released to scale rapidly with demand. To the consumers, provisioning capabilities appear unlimited and highly flexible.
• Measured service: Cloud systems automatically control and optimize resource use by metering appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). To codify technical aspects, cloud vendors must provide every customer with a Service Level Agreement.

Cloud also makes use of a number of key technologies that boost the efficiency of software development, including containers, a method of operating system virtualization that allows consistent app deployment across computing environments.

Cloud Computing Services

Cloud computing comprises a lot of different types of cloud services, but the NIST definition identifies three cloud service models: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). While these three models continue to dominate cloud computing, various vendors have also introduced other types of cloud services that they market with the “as-a-service” label. These include database as a service (DBaaS), disaster recovery as a service (DRaaS), function as a service (FaaS), storage as a service (SaaS), mobile backend as a service (MBaaS), security as a service (SECaaS), networking as a service (NaaS), and a host of others.

All of these cloud services can be gathered under the umbrella label “everything as a service,” or XaaS, but most of these other types of cloud computing services fall under one of the three original categories.

Software as a Service (SaaS)

In the SaaS model, users access applications via the Web. Application data resides in the software vendor’s cloud infrastructure, and users access it from any internet-connected device. Instead of paying a flat fee, as with the traditional software model, users purchase a subscription on a monthly or yearly basis.

The SaaS market alone is expected to grow from $273.55 billion in 2023 to $908.21 billion by 2030, representing a compound annual growth rate (CAGR) of 18.7 percent. The world’s largest SaaS vendors include Salesforce, Microsoft, Google, ADP, SAP, Oracle, IBM, Cisco and Adobe.

Infrastructure as a Service (IaaS)

IaaS vendors provide access to computing, storage, networks, and other infrastructure resources. Using an IaaS is very similar to using a server, storage appliance, networking device, or other hardware, except that it is managed as a cloud rather than as a traditional data center.

The IaaS cloud market, which was estimated at $118.43 billion in 2022, will be worth $450.52 billion by 2028, maintaining a CAGR of 24.3 percent over the analysis period. Amazon Web Services is considered the leading public IaaS vendor, with over 200 cloud services available across different industries. Others include Microsoft Azure, Google Cloud, IBM SoftLayer, and VMware vCloud Air. Organizations like HPE, Dell Technologies, Cisco, Lenovo, NetApp, and others also sell infrastructure that allows enterprises to set up private IaaS services.

Platform as a Service (PaaS)

PaaS occupies the middle ground between IaaS and SaaS. PaaS solutions don’t offer applications for end-users the way SaaS vendors do, but they offer more than just the infrastructure provided by IaaS solutions. Typically, PaaS solutions bundle together the tools that developers will need to write, deploy, and run applications. They are meant to be easier to use than IaaS offerings, but the line between what counts as IaaS and what counts as PaaS is sometimes blurry. Most PaaS offerings are designed for developers, and they are sometimes called “cloud development platforms.”

The global PaaS market is worth $61.42 billion, an increase of 9.8 percent over 2022. The list of leading public PaaS vendors is very similar to the list of IaaS vendors, and includes Amazon Web Services, Microsoft Azure, IBM Bluemix, Google App Engine, Salesforce App Cloud, Red Hat OpenShift, Cloud Foundry, and Heroku.

Public vs. Private vs. Hybrid Cloud

Cloud computing services can also be categorized based on their deployment models. In general, cloud deployment options include public cloud, private cloud, and hybrid cloud. Each has its own strengths and weaknesses.

Public Cloud

As the name suggests, a public cloud is available to businesses at large for a wide variety of remote computing needs. These cloud services are managed by third-party vendors and hosted in the cloud vendors’ data centers.

Public cloud saves organizations from having to buy, deploy, manage, and maintain their own hardware. Instead, vendors are responsible in exchange for a recurring fee.

On the other hand, public cloud users give up the ability to control the infrastructure, which can raise security and regulatory compliance concerns. Some public cloud providers, like AWS Outposts rack, now offer physical, on-premises server racks for jobs that need to be done in-house for security and compliance reasons. Additionally, many vendors offer cloud cost calculators to help users better predict and understand charges.

Private Cloud

A private cloud is a cloud computing environment used only by a single organization, which can take two different forms—organizations build their own private clouds in their own data centers, or use a hosted private cloud service. They’re also the most commonly used and best option for businesses that require a multi-layered infrastructure for IT and data protection.

Like a public cloud, a hosted private cloud is operated by a third party, but each customer gets dedicated infrastructure set aside for its needs rather than sharing servers and resources. A private cloud allows organizations to enjoy the scalability and agility of cloud computing without some of the security and compliance concerns of a public cloud. However, a private cloud is generally more expensive and more difficult to maintain.

Hybrid Cloud

A hybrid cloud is a combination of public private clouds managed as a single environment. They can be particularly beneficial for enterprises that have some data and applications that are too sensitive to entrust to a public cloud but need it to be accessible to other applications that do run on public cloud services.

Hybrid clouds are also helpful for “cloudbursting,” which involves using the public cloud during spikes in demand that overwhelm an organization’s private cloud. Managing a hybrid cloud can be very complex and requires special tools.

It’s important to note that a hybrid cloud is managed as a single environment. Already the average enterprise is using more than one cloud, and most market researchers expect multi-cloud and hybrid cloud environments to dominate the enterprise for the foreseeable future.

Cloud Computing Benefits

As already mentioned, each type of cloud computing has advantages and disadvantages, but all types of cloud computing generally offer the following benefits:

• Agility and Flexibility: Cloud environments enable end users to self-service and quickly provision the resources they need for new projects. Organizations can move workloads around to different servers and expand or contract the resources dedicated to a particular job as necessary.
• Scalability: The same virtualization and pooling features that make it easy to move workloads around also make it easy for organizations to scale up or down as usage of particular applications increases or decreases. It is somewhat easier to scale in a public cloud than a private cloud, but both offer scalability benefits in comparison to a traditional data center.
• Availability: It’s easier to recover data if a particular piece of infrastructure experiences an outage. In most cases, organizations can simply failover to another server or storage device within the cloud, and users don’t notice that a problem has occurred.
• Location Independence: Users access all types of cloud environments via the internet, which means that they can get to their applications and data from any web-connected device, nearly anywhere on the planet. For enterprises seeking to enable greater workforce mobility, this can be a powerful draw.
• Financial Benefits: Cloud computing services tend to be less expensive than traditional data centers. However, that isn’t true in every case, and the financial benefit varies depending on the type of cloud service used. For all types of cloud, however, organizations have a greater ability to chargeback computing usage to the particular business unit that is utilizing the resources, which can be a big aid for budgeting.

Cloud Computing Drawbacks

Of course, cloud computing also has some drawbacks. First of all, demand for knowledgeable IT workers remains high, and many organizations say it is difficult to find staff with the experience and skills they need to be successful with cloud computing. Experts say this problem will likely diminish over time as cloud computing becomes even more commonplace.

In addition, as organizations move toward multi-cloud and hybrid cloud environments, one of their biggest challenges is integrating and managing the services they use. Some organizations also experience problems related to cloud governance and control when end users begin using cloud services without the knowledge or approval of IT.

But the most commonly cited drawbacks of cloud computing center around cloud security and compliance. A hybrid infrastructure model that integrates public cloud with on-premises resources—and sometimes with a private cloud—can offer many of the advantages of both cloud and on-premises models while mitigating security and compliance risks by maintaining full control over data centers and virtual machines.

Cloud Security

Most of the security concerns around cloud computing relate primarily to public cloud services. Because public clouds are shared environments, many organizations have concerns that others using the same service can access their data. And without control over the physical infrastructure hosting their data and applications in the public cloud, enterprises need to make sure vendors take adequate measures to prevent attacks and meet compliance requirements.

However, some security experts argue that public cloud services are more secure than traditional data centers. Most cloud vendors have large security teams and employ the latest technologies to prevent and mitigate attacks. Smaller enterprises simply don’t have as many resources to devote to securing their networks.

But organizations should not just assume that cloud vendors have appropriate safeguards in place—vendors and users share responsibility for cloud security and both need to play an active role in keeping data secure.

Bottom Line: Cloud Computing

The popularity of cloud computing has grown steadily with no signs of slowing down since the phrase “cloud computing” was first used in the mid-1990s. It’s nearly ubiquitous among enterprises, with 87 percent operating a multi-cloud strategy and 72 percent a hybrid cloud strategy. Experts predict the market will continue to grow as organizations migrate more applications and data to the cloud. There are multiple models and a wide range of services available, giving organizations a lot of flexibility when it comes to cloud computing. From public to private to hybrid cloud, businesses can find or build the right configuration to meet their own particular budget, requirements, and needs.

Read next: Cloud Services Providers Comparison.