Jump to:

• How Does Data Compression Work?
• Types of Data Compression
• Data Compression Techniques
• Data Compression Algorithms
• The Importance of Data Compression
• Advantages of Data Compression
• Disadvantages of Data Compression
• Data Compression Use Cases
• Bottom Line: Data Compression

How Does Data Compression Work?

At a high level, data compression works by encoding the original, target data in fewer bits, reducing the size of the data. When needed, the data can be uncompressed, or decoded and retrieved.

The process involves two algorithms—one for compression and one for reconstruction. The original data is reduced into an encoded representation of itself. When accessing or retrieving the information, the reconstruction algorithm decompresses the data representation into a newer format to make it as similar to the original as possible.

Data compression is valuable because raw data is not ready to use. Noise and redundancy can inflate the footprint data occupies needlessly, requiring more storage capacity to retain it and more bandwidth to transmit it over a network. Noisy data refers to data that is distorted, corrupted, or unreadable, while redundant data refers to information that is repeated multiple times within the same dataset.

Data compression is aimed at eliminating redundancy and noise, improving the overall quality of the available information as well as reducing its size.

Types of Data Compression

Generally speaking, there are four different types of data compression:

• Text compression—primarily uses codes or symbols for patterns and redundancies; reduces overall size, but information remains unaltered.
• Audio compression—eliminates noise to shrink file sizes, but reduces overall quality.
• Image compression—similar to text compression; replaces repeated color patterns using codes to reduce size.
• Video compression—a combination of audio and image compression, removes unwanted images and background noise to reduce overall size at the expense of quality.

Data Compression Techniques

Broadly speaking, there are two overall approaches to data compression. Each is better-suited to certain applications and types of data depending on the desired result.

Lossless Compression

Lossless data compression is non-destructive—it retains the original information and preserves the original file structure, maintaining absolute quality. The original version can be entirely restored. Common applications of lossless compression techniques are archiving and formatting.

It’s primarily used for executable files like documents, software applications, spreadsheets, texts, or other critical system files. Familiar lossless compression formats include ZIP, GIF, PDF, and PNG.

Lossy Compression

Lossy data compression reduces the original size of the data by compromising some detail—it permanently removes unnecessary bits. Although it only discards unimportant information, it still affects the data quality. Common applications of lossy compression are multimedia files such as audio, photos, graphics, and videos.

Good results are possible when executed effectively, but aggressive compression can affect the file quality considerably. As a result, it’s used when some degree of quality loss can be tolerated. The most familiar formats include JPEG, MPEG, MP3, MP4, and MOV.

Data Compression Algorithms

Data compression relies on a wide range of algorithms to work. Here are the most common.

Run Length Encoding (RLE)

This lossless method reduces the number of bits used in data representation. Overall size is reduced but no information is lost. For example, if the data set includes several repeated characters—such as “aaaabbbbcccddee,” the RLE algorithm encodes it as “4a4b3c2de.” The same information is available in fewer bytes, but the data sequence remains unaltered.

Huffman Coding

Another lossless algorithm, this is primarily used for data sets consisting of frequently occurring characters. It generates a unique code for each character based on frequency—when the string is represented using these codes, overall size is reduced but the data remains unaffected.

Lempel-Ziv Algorithm

A lossless algorithm widely used for GIF and TIFF formats, this creates a codebook for encountered sequences. Because codes take up less space, the overall size of the data is reduced.

LZSS (Lempel-Ziv-Storer-Szymanski) Algorithm

This lossless algorithm uses a textual substitution principle based on the dictionary coding technique. First it substitutes a string of symbols using a reference. Then it removes duplicate data and ensures that the new file size is smaller than the original. LZSS can be easily implemented and is widely used for GIF, TIFF, PDF, and text file compressions.

DEFLATE

A combination of the LZSS and Huffman coding algorithms, this lossless technique was initially developed for ZIP files but is now also used for gzip in HTTP compression and PNG formats. It works by finding repeated character sequences and encoding them based on frequency.

Then it uses Huffman coding to compress the data a second time using shorter codes, reducing size considerably. Popularly used for web content compression, it enhances the browsing experience by compressing HTTP responses and reducing load times and bandwidth.

Audio and Video Codecs

Encompassing a wide range of algorithms, these advanced techniques offer significant compression for media files. The popular MP3 format used for audio files utilizes perceptual coding, removing data that is less noticeable to listeners and reducing file sizes.

Similarly, high-efficiency video coding (HEVC), or H.264, compresses video files using entropy coding and motion compensation. This leads to higher compression ratios without compromising visual quality. This video codec is what makes high definition video streaming and conferencing possible.

The Importance of Data Compression

For the modern enterprise, data is central to business functions. It’s used for everything from predictive analytics and trend-spotting to understanding customer behavior, refining marketing strategies, and enhancing user experiences.

Accumulating data sets is not inherently valuable—for data to be advantageous, it must be stored systematically to ensure quick retrieval and accessibility. But blindly expanding storage capacities in response to growing data volumes is neither scalable nor economical.

Data compression is one arrow in an organization’s data management quiver. It helps ensure that storage is optimized and identifies repetitive patterns to streamline interpretation and analysis.

As storage technologies improve, the data compression market is expected to innovate more real-time compression algorithms with minimal loss in quality to meet customer applications. At the same time, as the Internet of Things (IoT) expands across sectors, the demand for data compression solutions that preserve data integrity and security will grow with it.

Advantages of Data Compression

Data compression offers an array of advantages that cater to the specific business needs. Here are the most common.

Disadvantages of Data Compression

While data compression offers numerous benefits, there are a few downsides—here are the most notable.

Data Compression Uses

Data compression is a useful component for both storage management and data management, making it valuable across most industries. Here are some of the most common applications for it.

Communication

Because data compression reduces file size, it increases the overall capacity of communication channels by using less bandwidth. It also enhances wireless data efficiency—current electronic storage systems extensively use data compression techniques for cost-saving and better space utilization.

Cloud Computing

Data compression maximizes the capacity of cloud storage solutions, ensuring accessibility without excessive storage overhead. It also speeds up file transfer, reduces costs, and optimizes network traffic, simplifying multi-user or multi-location cloud deployments.

File Archiving

With data compression, it is possible to archive large volumes of data and free up system space. Inactive files or data not in regular use are generally archived, and can be retrieved if needed.

HD Streaming

Streaming video users have come to expect seamless experiences with superior visual and auditory fidelity. Compressing multimedia data improves transmission rates, leading to faster streaming, reduced buffering intervals, and consistent high-quality output.

Mobile Data Usage

Mobile users demand fast connections and limited data usage—data compression facilitates smooth media streaming and enhances mobile gaming. Compressed files require less storage and reduce download times.

Healthcare

Diagnostic images from X-rays, MRIs, and other medical tests are often stored in compressed formats, optimizing storage while preserving the quality and integrity of critical patient information.

Bottom Line: Data Compression

As enterprise data use skyrockets—and as their dependence upon data to fuel decision-making across all departments grows in parallel—reliable data and storage management solutions become an essential need. Data compression is just one of the many tools in a data management toolbox. Its applications span domains with applications from enhancing cloud storage efficiencies to ensuring seamless high-definition streaming and safeguarding crucial medical records. With a wide range of techniques and algorithms designed to shift the balance between file size and file quality, data compression is an effective solution for all businesses. As our reliance on data continues to strengthen, strategically deploying data compression techniques will be integral to operational efficiency and resource optimization.

Learn more about how enterprises use data for everything from operational efficiency to customer engagement by reading our complete guide to data analytics.

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.

Here are our picks for the top 10 data center certifications for 2023.

Cisco Certified Network Associate (CCNA)

This associate-level certification demonstrates a grasp of IT fundamentals, including basic data center networking, troubleshooting, addressing schemes, switch configurations, VLANs, Nexus OS, common network services, network and server virtualization, load balancing, storage, and network access controls. The CCNA focuses on agility and versatility, certifying management and optimization skills in advanced networks, and is considered an industry standard certification.

Participants must earn a passing score on Cisco exam No. 200-301, which tests their knowledge and their ability to install, operate, and troubleshoot an enterprise branch network.

Prerequisites

No prerequisites; Cisco’s Data Center Networking and Technologies course recommended

Validity

Three years

Accreditation

Cisco

Location

Classroom and online

Cost

Course Fee: $4,500; Exam Fee: $600

Cisco Certified Network Professional (CCNP) 

This certification bestows the professional level of Cisco Career Certification upon those who successfully complete it. It specializes in the skills needed to implement effective solutions in enterprise-class data centers. Similar to the CCNA, the CCNP requires a passing score on an exam.

The Data Center exam tests the skills needed to run a data center effectively, including knowledge of the implementation of such core data center technologies as network, compute, storage network, automation, and security. A second exam lets participants specialize in a concentration of their choosing—candidates need to pass both exams to earn the certification.

Cisco Certified Network Professionals typically hold such roles as senior network designer, network administrator, senior data center engineer, and consulting systems engineer.

Prerequisites

No prerequisites; Recommended for people with three to five years of industry experience in security solutions

Validity

Three years

Accreditation

Cisco

Location

Classroom/e-learning/private

Cost

$300 per exam

VMware Certified Professional – Data Center Virtualization (VCP-DCV 2023)

VMware offers more than 16 data center certifications, including the VCP-DCV 2023, which bridges the gap between cloud management and classic data center networking. The VCP-DCV certification tests an individual’s knowledge of VMware’s vSphere solutions, including virtual machines, networking, and storage. Professionals seeking job roles including virtualization administrators, system engineers, and consultants should apply.

VMware also offers other advanced professional courses in virtualization design and deployment: VMware Certified Advanced Professional Data Center Virtualization Design (VCAP-DCV Design),  VMware Certified Advanced Professional Data Center Virtualization Deploy (VCAP-DCV Deploy) and VMware Certified Design Expert (VCDX-DCV).

Prerequisites

Experience with vSphere 7.x or vSphere 8.x is recommended; Applicants with no prior VCP certifications must enroll in at least one training course

Validity

No expiration; recertification recommended to upgrade skills

Accreditation

VMware

Location

Online

Cost

$250

Juniper Networks Junos Associate (JNCIA-Junos)

The JNCIA-Junos certification is a beginner/intermediate course designed for networking professionals that validates their understanding of the core functionality of the Juniper Networks Junos operating system. It establishes a baseline for multiple certification tracks, including Juniper’s Enterprise Routing and Switching Certification Track and Service Provider Routing and Switching Certification Track.

Candidates can avail themselves of the resources on the Juniper Networks website and then sign up for the 90-minute, 65 multiple-choice question exam. Pass/fail status is shown directly after the exam, which certifies knowledge in data center deployment, implementation of multi-chassis link aggregation group (LAG), internet protocol (IP) fabric, virtual chassis, virtual extensible LANs (VXLANs), and data center interconnections.

Prerequisites

Juniper Networks Certified Specialist Enterprise Routing and Switching certification; Advanced Data Center Switching course recommended

Validity

Three years

Accreditation

Juniper Networks

Location

Online

Cost

$2,500-$4,750 depending on course location

Schneider Electric Data Center Certified Associate (DCCA)

This associate certification from Schneider Electric validates foundational knowledge of physical infrastructure in data centers and requires candidates to demonstrate proficiency in such aspects as cooling, power management, and physical security, among others.

Schneider offers multiple courses to prepare for the Data Center Certified Associate exam. Candidates may apply for examination after completion of the course. This certification is meant for professionals looking to work with designs or upgrades for the physical layer data centers and covers foundational knowledge of data center design, builds, and operations.

Prerequisites

None

Validity

Does not expire

Accreditation

Schneider Electric

Location

Online

Cost

$250

VCE Certified Professional

Converged infrastructure systems vendor VCE’s Certified Professional Program offers experienced IT professionals operating in converged infrastructure environments the opportunity to validate their domain-specific focus with cross-domain expertise.

Candidates begin with the Converged Infrastructure Associate credential and then choose one of two certification tracks. The Deploy track is intended for deployment and implementation professionals, while the Manage track is intended for administration and management professionals. The VCE program trains candidates in system concepts, security, administration, resource management, troubleshooting, and data center maintenance.

Prerequisites

VCE Certified Converged Infrastructure Associate (VCE-CIA) certification

Validity

Two years

Accreditation

VCE Plus

Location

Offline

Cost

$200

BICSI Registered Communications Distribution Designer (RCDD)

BICSI is a professional association supporting the advancement of information and communication technology professionals, and the RCDD is its flagship program. It trains participants in the design and implementation of telecommunications distribution systems as a part of an infrastructure development track. Being recognized as a BICSI RCDD bestows industry recognition and can accelerate career paths.

Eligible candidates must have two years of industry experience. The exam tests their knowledge of design, integration, implementation, project management, and building physical infrastructure for data centers.

Prerequisites

Two years of industry experience

Validity

Does not expire

Accreditation

BICSI

Location

Offline

Cost

$495

EPI Certified Data Centre Expert (CDCE)

EPI is a Europe-based, globally focused provider of data center infrastructure services. Its CDCE course trains and certifies IT managers and data center professionals in building and relocating critical infrastructures and data centers. The exam consists of two parts: a closed-book exam, and an open question exam in which candidates must answer 25 questions in 90 minutes.

Topics include choosing optimum centers, describing components, designing life cycle stages, business resilience, site selection, technical level design, reading electrical Single Line Diagrams (SLD), evaluating product datasheets, correlating equipment specifications, floor loading capacity, maintenance requirements, developing Individual Equipment Test (IET), and building checklists for critical data center facility.

Prerequisites

CDCS Certificate

Validity

Three years

Accreditation

EPI

Location

Online/Offline

Cost

Varies with service provider

CNet Certified Data Centre Sustainability Professional (CDCSP)

CNet’s CDCSP certification focuses on creating a credible sustainability strategy and business implementation plan for data centers. The program covers the evaluation, analysis, planning, implementation, and monitoring of sustainability initiatives, with considerations for operational capability and business needs.

It addresses power distribution, cooling systems, IT hardware, and operational risks, and emphasizes design innovation and continuous planning cycles. It also covers compliance with national and international regulations along with the importance of demonstrating ROI and capitalizing on business, customer, social, and environmental benefits.

Candidates will learn best sustainability practices, CSR in data centers, data center performance KPIs, understanding business needs, operational risks, creating sustainable ethos, sustainability use-cases, monitoring of power sources, infrastructure, cooling capabilities, sustainability improvements, and maintenance strategies, corporate sustainability, and planning.

Graduates are encouraged to pursue further certifications and qualifications through The Global Digital Infrastructure Education Framework for career advancement in the network infrastructure and data center sectors.

Prerequisites

Two years of work experience in centers as an operations manager, designer, or sustainability engineer

Validity

Does not expire

Accreditation

CNet

Location

Online/Offline

Cost

$6,990

CNet Certified Data Center Design Professional (CDCDP)

CNet’s CDCDP certification is a 20-hour intensive training program designed to help candidates understand sustainability and energy from a professional perspective. It provides comprehensive training on data center design to meet business needs efficiently and sustainably. Participants learn best practices, compliance, and access to industry standards, with opportunities for further career advancement through The Global Digital Infrastructure Education Framework.

By finishing the five-day program, candidates gain expertise in developing projects, identifying national and international standards, availability models, structural requirements, cabinet designing, power systems, regulations, connection topologies, compliance requirements, cable management, seismic stability considerations, estimating power requirements, revising psychrometric charts, bypass and recirculation, earthing, bonding, strategizing IT requirements, virtualization, optimal testing, regulating local codes, and cable protection.

Prerequisites

Two years data center experience

Validity

Does not expire

Accreditation

CNet

Location

Online

Cost

$5,750

Bottom Line: Data Center Certifications

Experts estimate that data centers need to hire more than 300,000 new staff members by 2025 in order to keep pace with the growing demand for services. They’re also facing pressure to become more sustainable and to continually boost security to ensure the safety of client data. There’s never been more opportunity for professionals seeking to work in this expanding field, and professional certifications can expand their knowledge, demonstrate their skills to employers, and provide areas of focus and specialized expertise.

Read next: 7 Data Management Trends: The Future of Data Management

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

This article outlines five current database trends that explain the booming market for them and offer some idea about what to expect as they continue to evolve with changing technology.

1. Old Guard Losing Out to Cloud DBs

Not so long ago, Oracle, IBM, SAP, Teradata, and Software AG were the bigwigs of the database world. They all began life as on-premises systems and all have attempted to transition to the cloud, with varying degrees of success. However, cloud-based databases have largely taken over and cloud-native databases dominate the market. Microsoft is now the leader, with Amazon Web Services (AWS), Google Cloud Platform (GCP), and Alibaba Cloud close behind. Oracle, IBM, and SAP retain a large slice of the market after a painful transition to cloud-based systems, but cloud is king without question.

Learn more about cloud vs. on-premises architecture.

2. Artificial Intelligence in Databases

On average, database administrators (DBAs) spend 90 percent of their time on maintenance tasks, according to Oracle’s Cloud Business Group surveys. AI is being added to database management as a way to greatly lower the maintenance burden. When well-integrated with databases and their underlying infrastructure, AI helps DBAs spot storage and memory bottlenecks and other issues that inhibit database operations.

3. In-Memory Databases

Today’s mission-critical software solutions require minimal database latency for optimal performance. Unfortunately, traditional database management systems (DBMS) rely on sluggish disk read/write operations for storing data on media (e.g., hard disk drives). For this reason, in-memory databases—databases that store entire datasets in read only memory (RAM)—have become strong alternatives for these critical use cases. Records stored and retrieved directly to and from RAM make faster, more reliable performance possible. Additionally, popular solutions such as Redis—an in-memory data structure store—make it possible for databases to support more data structure types and custom access patterns, allowing for the simplification of software code without data structure conversion or serialization.

4. All-Flash Databases

Memory-based databases are great, but can be very expensive. All-flash arrays provide similar performance at a better price, while also providing a lot more capacity. As a result, more databases now run inside all-flash arrays than on in-memory systems. An example of this is JP Morgan Chase, which was seeing a 30 percent increase or more in data storage needs annually. Greg Johnson, executive director of Global Electronic Trading Services, transitioned from disk-based systems to all-flash arrays to provide the capacity and speed his databases need for transactional and other mission-critical systems. “The combination of all-flash and AI has helped us to approve over 200 million credit card transactions that would have otherwise been declined,” Johnson said.

5. Stronger Database Security Layers

With cyber attacks and data breaches continuing to dominate headlines in the technology world, more focus has been placed on securing the data layer of the software application. In turn, more vendors are augmenting their offerings with stronger built-in security features. Oracle now integrates always-on encryption and automated patching at the database level, for example, while Amazon RDS includes a built-in firewall for rules-based database access. Similarly, database users need far more safeguards related to privacy, data residency, sovereignty, and localization, and DBAs must pay attention to where data is stored and where it is going. Vendors are now introducing location-tracking features into their storage arrays and databases to make it possible to verify compliance.

Learn more about big data security.

Bottom Line: Database Trends

Most databases fall into one of two categories, relational database management systems (RDBMS) and unstructured/special application databases. RDBMS have been around since the 1970s and consist of related tables made up of rows and columns. They’re manipulated using structured query language (SQL), the de-facto standard language for performing create, read, update, and delete (CRUD) functions. This is the dominant database type for enterprise computing.

The advent of the cloud saw data processing capabilities scale horizontally like never before. This happened just in time to support the increase in data generated by the internet—both structured and unstructured. But as unstructured data became increasingly common, a need for a new database paradigm led to the creation of NoSQL, a broad category of databases that do not use SQL as their main language. Because NoSQL databases have no set requirements in terms of schemas or structure, they are ideal for software environments based on DevOps toolsets and continuous improvement/continuous delivery (CI/CD) pipelines.

Technologies come and go, and databases are no different. Early DBAs cut their teeth on Informix, SQL server, and Oracle database management systems, while the next generation favored the simplicity of open-source MySQL/LAMP stack and PostgreSQL. Current DevOps workflows benefit from the unstructured agility of NoSQL databases like MongoDB and DynamoDB.

Where databases go from here will depend upon a number of factors, including technology and market innovations, but the need for them will only continue to increase.

Read next: Top 6 Database Challenges and Solutions

Meanwhile, the ever-growing value of big data is driving data scientists to increase their access along with their ability to mine and analyze data for insights and information by adapting how data repositories are managed in relation to the type of data they house. While some legacy and on-premises solutions continue to be indispensable, a mass shift to the cloud is proving to be the answer to many of the problems organizations are facing in regards to data volume, compatibility, and accessibility.

Companies of various sizes and industries adapt to progress at different rates and may migrate data for different reasons. The five major trends in data migration in 2023 reflect the industry’s attitude as a whole toward solving specific problems.

1. A Shift Towards Data Lakehouses

Data lakehouses are open data management architectures that combine the flexibility, cost-efficiency, and scale of data lakes with the data management abilities of data warehouses. The result is a unified platform used for the storage, processing, and analysis of both structured and unstructured data. One reason this approach is gaining popularity is a sustained desire to break down data silos, improve quality, and accelerate data-driven decision-making within organizations.

Data lakehouses’ large capacity enables them to handle large volumes of data in real time, making them ideal for live consumer data, Internet of Things (IoT) networks, and physical sensors. Their ability to process data from multiple sources makes it easier for organizations to gain insights from multiple data streams.

Additionally, the centralization of data lakehouses allows for a unified, up-to-date view of data across an entire organization, facilitating inter-departmental collaboration on data-based projects and greatly reducing the costs and complexity of hosting multiple data storage and processing solutions.

2. A Focus on AI and Automation in Governance

Data migration helps organizations keep pace by ensuring their systems are able to accommodate the ever-increasing flow of new data. To simplify the already complex and time-consuming task of data governance, many companies are turning to artificial intelligence (AI)/machine learning (ML) algorithms and automation.

These technologies have revolutionized data migration by allowing organizations and data managers to automate some of the many manual processes it involves. It also enables them to reduce the risk of failures due to human error and execute the migration process more accurately and efficiently. With the help of smart algorithms, organizations can also better gain insights into their data than previously possible while identifying and eliminating data duplicates, which may reduce storage costs and improve performance.

Thanks to the recent boom in AI and ML-based technologies being developed and partially launched by a number of cloud computing giants, including Microsoft and Google, the role of such technologies in the more critical processes of data migration is likely to increase as the models become more and more sophisticated.

3. Expanding Storage Capacity

The world is expected to generate around 120 zettabytes of data in 2023, a nearly 24 percent increase from the prior year. This data is generated from a wide variety of sources, including IoT devices, log files, and marketing research. In this case, bigger is better—many organizations are looking to embrace big data by expanding storage capacities through novel methods of data storage.

One prominent option is cloud storage, which stands out as a scalable, reliable solution that’s also easily accessible over the internet. However, one of the challenges that arises with data migration to the cloud is maintaining security during transit. Organizations must carefully plan their migration strategies—including encryption, backup, and recovery plans—to protect financial and medical data and personal information while it is at risk.

Organizations can also benefit from an increase in agility and compounded value of structured and unstructured data by expanding their overall data storage capacity through flexible and scalable means.

4. Handling Unstructured Data

Most data sources produce semi-structured or unstructured data that cannot be easily organized and categorized. Company mergers and system updates are prominent sources of unstructured data—the initial categorization and structure of the data must be shed in order to fit into a different system. Unstructured data tends to be much larger in volume than structured data carrying the same amount of information and insights.

This poses a problem when migrating data. Not only is the massive volume costly to transfer and secure, both in-transit and at-rest, but it cannot be analyzed or stored in relational databases. However, that doesn’t make it void of value, as many organizations are seeking data science and migration solutions that would help structure incoming data.

Solving the unstructured data problem is a time-sensitive endeavor for many organizations. That’s because situational data quickly loses its value with time and gets replaced by more recent data, often in greater volume.

5. A Move From On-Premises Legacy Systems to Cloud Storage

Most data originates in the cloud, from such sources as digital logs, monitoring devices, customer transactions, and IoT devices and sensors. Many organizations are finding it more efficient to migrate entirely to the cloud rather than remaining split between legacy on-premises systems and cloud storage.

This approach would involve the integration of legacy data and systems with already-present data stored in the cloud, creating a more unified and comprehensive approach to data management and enabling remote access. A move to the cloud would also be accompanied by embracing multi-cloud architectures, allowing companies to optimize costs by working and switching between multiple cloud providers simultaneously.

Moving entirely to the cloud would also facilitate data storage segmentation, enabling data managers to differentiate data by type, purpose, and origin in addition to sensitivity and the level of security it may require. Organizations with data split between legacy and cloud systems may seek to unify the multiple sources in the cloud, enabling them to develop a richer, more holistic view of their data and how they might be able to use it.

Predictions for the Future of Data Migration

Data migration is expected to continue to grow in popularity alongside the exponential growth in the average volume of data produced annually by organizations. As businesses increasingly adopt cloud-based alternatives to everything from computing and processing to hosting software, cloud-based data solutions are likely to follow.

This will spark a wave of innovation, creating modern tools and technologies that aim to simplify the data migration process, ensuring the security and reliability of data in transit. Combined with the latest advancements in AI, ML, and automation, the migration process is likely to become faster, more efficient, and less prone to errors, making data migration as a concept more accessible to startups and emerging businesses who want to shift to the cloud and make the most out of their data.

For businesses considering a data warehouse solution, a number of competing providers offer a range of features and prices. This article will compare the top seven solutions and explain the features that differentiate them, making it easier to match them to specific needs.

Table Of Contents

• Top Data Warehouse Providers And Solutions
• Data Warehouse Providers And Solutions Comparison Table
• Amazon Redshift: Best For Deployment Optimization
• Google BigQuery: Best For Serverless Data Warehousing
• IBM Db2 Warehouse: Best For Analytic Workloads
• Azure Synapse Analytics: Best For Code-Free Offering
• Oracle Autonomous Data Warehouse: Best For Integration
• SAP Datasphere: Best For Data Warehouse Templates
• Snowflake: Best For Cloud Data Warehouses
• Key Features Of Data Warehouse Providers And Solutions
• How To Choose What Data Warehouse Provider Best For You
• Frequently Asked Questions (FAQ)
• Bottom Line: Data Warehouse Providers And Solutions

The top seven providers all offer feature-rich data warehousing plans at varying prices. A business’s specific needs will determine which is right for them. When selecting a provider, consider the use cases and costs for each as outlined below.

Data Warehouse Providers And Solutions Comparison Table

Amazon Redshift: Best For Deployment Options

With Amazon’s entry into the cloud data warehouse market, Redshift is an ideal solution for those organizations that have already invested in AWS tooling and deployment. Redshift deploys with Software as a Service (SaaS), cloud, and web-based solutions.

Pricing

Amazon Redshift has a pricing page where users can sign up for a trial period, request a quote, or calculate costs based on needs. Pricing starts at $0.25 an hour and can be configured using various models based on usage.

Features

• Spectrum Feature: This feature allows organizations to directly connect with data stores in the AWS S3 cloud data storage service, reducing startup time and cost.
• Strong Performance: The performance benefits companies from AWS infrastructure and large parallel processing data warehouse architecture for distributed queries and data analysis.
• Integration With AWS Glue: AWS Glue makes it easy to write or autogenerate Extract, Transform, and Load (ETL) scripts in addition to testing and running them.

See all Redshift features at https://aws.amazon.com/redshift/features.

Pros

• Parallel processing capabilities
• Contains network isolation security
• Good documentation

Cons

• Expensive
• Poorly designed user interface
• Unable to restrict duplicate records

For more on AWS: AWS Data Portfolio Review

Google BigQuery: Best For Serverless Technology

Google BigQuery is a reasonable choice for users looking to use standard SQL queries to analyze large data sets in the cloud. It is a serverless enterprise data warehouse that uses cloud, scale, Machine Learning (ML)/Artificial Intelligence (AI), and Business Intelligence (BI).

Pricing

Google BigQuery’s pricing page contains specific information about pay-as-you-go plans and longer-term (one to three year) commitments. The provider offers multiple versions of the platform, including Enterprise Edition and Enterprise Plus Edition. The Standard Edition is a pay-as-you-go plan starting at $0.04 per slot hour and the Enterprise Edition has different plans to help a company find its cloud data warehouse.

Features

• Serverless Technology: Using serverless technology, Google handles the functions of a fully managed cloud service, data warehouse setup, and resource provisioning.
• Logical Data Warehousing Capabilities: BigQuery lets users connect with other data sources, including databases and spreadsheets to analyze data.
• Integration With BigQuery ML: With BigQuery ML machine learning, workloads can be trained on data in a data warehouse.

See all BigQuery features at https://cloud.google.com/bigquery.

Pros

• Works with Google Cloud
• Full SQL query support
• Efficient management of data

Cons

• No user support
• Difficult for beginners in data warehouses
• Difficult user interface

For more information on Google: Google Data Portfolio Review

IBM Db2 Warehouse: Best For Analytic Workloads

IBM Db2 Warehouse is a strong option for organizations handling analytics workloads that can benefit from the platform’s integrated in-memory database engine and Apache Spark analytics engine.

Pricing

IBM offers a free trial for IBM Db2 Warehouse and provides a pricing page where users can ask for a quote and estimate the cost. For the flex one plan, the pricing is $1.23 per instance-hour, $0.99 per VPC-hour, and $850 per a service endpoint dedicated connectivity.

For more information, go to IBM’s pricing page.

Features

• Helpful Integration: IBM Db2 Warehouse integrates an in-memory, columnar database engine, which can be a big benefit for organizations looking for a data warehouse that includes a high-performance database.
• Netezza Technology: Db2 Warehouse benefits from IBM’s Netezza technology with advanced data lookup capabilities.
• Cloud Deployment And On-Premises: Deployment can be done in either IBM cloud or in AWS, and there is also an on-premises version of Db2 Warehouse, which can be useful for organizations that have hybrid cloud deployment needs.

See all Db2 Warehouse features at https://www.ibm.com/products/db2/warehouse.

Pros

• Includes in-memory columnar database
• Cloud deployment options
• Configuration flexibility

Cons

• Expensive
• Limited references online
• Limited buffer pool commands

For more on IBM: IBM: Hybrid Cloud Portfolio Review

Azure Synapse Analytics: Best For Code-Free Offerings

Azure Synapse Analytics, previously known as Azure SQL Data Warehouse, is well suited for organizations of any size looking for an easy on-ramp into cloud-based data warehouse technology, thanks to its integration with Microsoft SQL Server.

Pricing

Azure Synapse Analytics’s pricing page allows customers to request a quote or explore pricing options. For tier one, Azure offers 5,000 units for $4,700; tier two offers 10,000 units for $9,200. For other tier options, refer to the pricing page.

Features

• Dynamic Data Masking (DDM): Azure Synapse Analytics provides a granular level of security control, enabling sensitive data to be hidden on the fly as queries are made.
• Azure Integration: Existing Microsoft users will likely find the most benefit from Azure SQL Data Warehouse, with multiple integrations across the Microsoft Azure public cloud and more importantly, SQL Server for a database.
• Parallel Processing: In contrast to simply running SQL Server on-premises, Microsoft has built on a massively parallel processing architecture that can enable users to run over a hundred concurrent queries.

See more Azure Synapse Analytics features at https://learn.microsoft.com/en-us/azure/synapse-analytics/whats-new.

Pros

• Easy integration
• Some code-free offerings
• Strong data distribution

Cons

• Difficult logging metrics
• Limited diagramming tools
• Limited documentation

For more on Microsoft Azure: Microsoft Azure: Cloud Portfolio Review

Oracle Autonomous Data Warehouse: Best For Integration

For existing users of the Oracle database, the Oracle Autonomous Data Warehouse might be the easiest choice, offering a connected onramp into the cloud including the benefits of data marts, data warehouses, data lakes, and data lakehouses.

Pricing

Oracle’s Autonomous Data Warehouse’s main page offers pricing information as well as a cost estimator for users. The bottom price for Oracle Autonomous Data Warehouse shared and dedicated infrastructures is $0.25 per unit.

Features

• Works With Cloud And Hardware: A key differentiator for Oracle is that it runs the Autonomous Data Warehouse in an optimized cloud service with Oracle’s Exadata hardware systems, which has been purpose-built for the Oracle database.
• Easy Collaboration: The service integrates a web-based notebook and reporting services to share data analysis and enable easy collaboration.
• Strong Integration: While Oracle’s namesake database is supported, users can also migrate data from other databases and clouds, including Amazon Redshift, as well as on-premises object data stores.

See more features at https://www.oracle.com/autonomous-database/autonomous-data-warehouse/.

Pros

• Migration support for other database services
• Purpose-built hardware
• Fast query performance

Cons

• No on-premises solutions
• Limited data connection
• Complicated setup

For more on Oracle: Oracle Data Portfolio Review

SAP Datasphere: Best For Templates

Thanks to the pre-built templates it offers, SAP’s Datasphere might be a good fit for organizations looking for more of a turnkey approach to getting the full benefit of a data warehouse. SAP Datasphere allows data professionals to deliver scalable access to business data.

Pricing

SAP Datasphere’s pricing page lists a free tier and range of flexible pricing options based on needs. The price for capacity datasphere units is $1.06 per unit.

Features

• SAP’s HANA (High-performance Analytic Appliance): The cloud services and database are at the core of Data Warehouse Cloud, supplemented by best practices for data governance and integrated with a SQL query engine.
• Pre-Built Business Templates: Templates can help solve common data warehouse and analytics use cases for specific industries and lines of business.
• Integration with SAP Applications: SAP Datasphere integration means easier access to on-premises as well as cloud data sets.

See more features including a product demo at https://www.sap.com/products/technology-platform/datasphere.html.

Pros

• Inventory controls
• Extract data from multiple sources
• Strategic solutions

Cons

• Difficult for beginners
• Difficult integration
• Limited visual analytics

For more on SAP: SAP Data Portfolio Review

Snowflake: Best For Data Warehouse In The Cloud

Snowflake is a great option for organizations in any industry that want a choice of different public cloud providers for data warehouse capabilities. Snowflake aims to bring development to data, help companies govern data for users, and work globally and cross-cloud.

Pricing

Snowflake’s pricing page links to a quote page and offers a 30-day free trial with $400 of free usage.

Features

• Database Engine: Snowflake’s columnar database engine capability can handle both structured and semi-structured data, such as JSON and XML.
• Cloud Provider Of Choice: Snowflake architecture allows for compute and storage to scale separately, with data storage provided on the user’s cloud provider of choice.
• Virtual Data Warehouse: The system creates what Snowflake refers to as a virtual data warehouse, where different workloads share the same data but can run independently.

See more features at https://www.snowflake.com/en/.

Pros

• SQL-based queries for analytics
• Support for JSON and XML
• Integration with AWS, Azure, and GCP

Cons

• Limited data visualization
• Unable to create dynamic SQL
• Difficult documentation

For more information on Snowflake: Snowflake and the Enterprise Data Platform

Key Features of Data Warehouse Providers and Solutions

Cloud data warehouses typically include a database or pointers to a collection of databases where the production data is collected. Many modern cloud data warehouses also include some form of integrated query engine that enables users to search and analyze the data and assist with data mining.

Other key features to look for in a cloud data warehouse setup:

• Integration or API Libraries
• Data Quality and Compliance Tools
• ETL Tools
• Data Access Tools/Database Searchability
• SQL and NoSQL Data Capabilities

For more features and benefits: Top 10 Benefits of Data Warehousing: Is It Right for You?

How To Choose Which Data Warehouse Provider is Best for You

When looking to choose a cloud data warehouse service, there are several criteria to consider.

Existing Cloud Deployments. Each of the major public cloud providers has its data warehouse that provides integration with existing resources, which could make deployment and usage easier for cloud data warehouse users.

Ability to Migrate Data. Consider the different types of data the organization has and where it is stored. The ability to migrate data effectively into a new data warehouse is critically important.

Storage Options. While data warehouse solutions can be used to store data, having the ability to access commodity cloud storage services can provide lower-cost options.

Bottom Line: Data Warehousing Providers and Solutions

When considering providers and solutions of data warehousing, it’s important to weigh features and cost against your company’s primary goals, including deployment and analytic needs and cloud services.

While each provider and solution offers a variety of features, identifying a company’s own use case can help better evaluate them against a company’s needs.

For more information: 15 Best Data Warehouse Software & Tools

The need for enterprise-grade data storage has propelled the global public cloud market skyward. It is expected to almost double from $445 billion to $988 billion between 2022 and 2027. The richness and diversity of the market can make it daunting for organizations looking to upscale and upgrade their services.

Here’s a brief guide to some of the leading providers of public cloud solutions and how to choose the right provider for specific business needs.

Best Public Cloud Providers:

Amazon Web Services (AWS)

Amazon subsidiary Amazon Web Service (AWS) emerged in 2006, revolutionizing how organizations access cloud computing technology and remote resources. It offers a vast array of resources, allowing it to design and execute new solutions at a rapid pace to keep up with the global market’s evolution.

AWS’s services range from Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) to the simplified and easy-to-access and use, Software as a Service (SaaS) cloud models. Key offerings include:

Amazon EC2

Amazon Elastic Compute Cloud (EC2) is a web service that delivers secure and scalable computing capacity based in the cloud designed to facilitate web-centric computing for developers. This allows them to obtain and configure capacity with minimal friction with the infrastructure.

The services are available in a wide selection of instance types, from public to private and hybrid, that can be optimized to fit different use cases.

Amazon S3

Amazon Simple Storage Service (S3) is an object-based storage service known for its industry-leading scalability, security, performance and reliable data availability. Organizations of various sizes and industries can use it to store and retrieve any amount of data at any time, providing easy-to-use management features in order to organize data and configure it finely-tuned access control.

Amazon RDS

Amazon Relational Database Service (RDS) simplifies the setup and operations of relational databases in the cloud. AWS is responsible for automating all the redundant and time-consuming administrative tasks, such as hardware provisioning, database setup and data backup and recovery. This is best used to free up developers’ time, allowing them to focus on more pressing tasks like application development and design.

Use Cases and Industries

As a multinational corporation, AWS is able to cater to a wide variety of industries at different stages of development, from startups to established enterprises, as well as the public sector.

Use cases include:

• Application hosting
• Data processing
• Data warehousing
• Backup and restoration

This makes AWS’s service particularly useful for data-intensive industries such as healthcare, telecommunications, financial services, retail, and manufacturing.

Microsoft Azure

Microsoft launched Azure in 2010 as a comprehensive suite of cloud-based services designed to help businesses and organizations navigate the challenges that come with digital adoption. Azure was built on Microsoft’s decades-long specialty—software design—allowing its public cloud solutions to integrate seamlessly with other Microsoft products.

Azure also includes a multitude of services that range from computing and database management to storage and machine learning, including the following:

Azure Blob Storage

Azure Blob Storage is an object-based and scalable storage platform used for data lakes, warehouses and analytics as well as backup and recovery. It’s optimized for massive amounts of unstructured data, like text or binary values.

Azure Cosmos DB

Azure Cosmos DB is a database management service that’s multi-modeled, globally distributed and highly scalable, ensuring low latency that supports various APIs to facilitate access. It supports data models including SQL, MongoDB, Tables, Gremlin and Cassandra.

Azure Virtual Machines

Azure’s Virtual Machines are on-demand, scalable resources that provide users the flexibility of virtualization without the need to invest in or maintain the infrastructure that runs it. They also run on several Microsoft software platforms, supporting numerous Linux distributions for a more versatile experience.

Use Cases and Industries

When combined with Microsoft’s software and enterprise-focused approach to the public cloud, Microsoft Azure’s comprehensive services make it the ideal solution for numerous use cases, such as:

• Big data and analytics
• Application hosting
• Disaster and backup recovery
• IoT applications

Azure’s services are used by businesses and organizations in a number of industries such as e-commerce, healthcare, insurance and financial institutions.

Google Cloud Platform (GCP)

First launched in 2011 as a cloud-based subsidiary of Google, Google Cloud Platform (GCP) is a suite of cloud computing services that uses the same infrastructure as Google’s software products. Its industry-leading creations from TensorFlow and Kubernetes are some of the greatest examples of Google’s sophisticated solutions, and include the following:

Google Cloud Engine

Also known as Google Kubernetes Engine (GKE), Cloud Engine is a fully managed, user-ready environment used to deploy containerized applications and web services. Based on the open-source Kubernetes system, it’s developed by Google for managing workloads, enabling developers to flexibly and efficiently develop apps and deploy applications.

Google Cloud Storage

Google Cloud Storage is a fully managed and scalable object-oriented storage service. It includes many services ranging from serving website content to storing data for archival purposes and disaster recovery.

Google Compute Engine

Google Compute Engine is a cloud-based virtual machine solution that’s scalable and flexible. It allows users to tailor their computing environment, meeting specific requirements, and offering flexible pricing and cost savings.

Use Cases and Industries

GCP is used by organizations and businesses in IT, healthcare and retail, as well as the financial industry. Use cases include:

• Data analytics and machine learning
• Application development
• Storage and database management

IBM Cloud

IBM launched IBM Cloud in 2011 as a collection of cloud-based computing services. It leverages IBM’s vast experience, offering a robust approach to enterprise-grade public cloud platforms with an emphasis on open-source technologies and supporting a diverse set of computing models, including the following:

IBM Cloud Functions

IBM Cloud Functions is IBM’s Function as a Service (FaaS) solution built on Apache OpenWhisk. It enables developers to execute code in response to events as well as direct HTTP calls without having to manage their own hardware infrastructure.

IBM Cloud Virtual Servers

These flexible and scalable cloud computing solutions support both public and dedicated virtual servers. They’re the right balance of computing power to cost, allowing companies to deploy the servers globally and reach their customers.

IBM Cloud Databases

IBM Cloud Databases is a family of managed, public databases that support a wide variety of data models that include relational, key-value, document, and time-series applications.

Use Cases and Industries

IBM Cloud services a wide range of industries with its diverse offerings, such as IT and technology companies, healthcare organizations, financial institutions and retail providers, as well as the public sector. Use cases include:

• Public and hybrid cloud implementation
• Blockchain development
• Data analytics and management
• AI and machine learning

Oracle Cloud Infrastructure

The Oracle Cloud Infrastructure is a part of Oracle’s comprehensive cloud offering, first launched in 2012. The public cloud solution leverages Oracle’s long history in enterprise computing and data processing, enabling the company to provide robust, scalable and secure services, including the following:

Oracle Cloud Storage

Oracle Cloud Storage is a high-performance, scalable and reliable object storage service. It’s capable of storing an unlimited amount of data of any content type, including analytic data and rich content like images and video.

Oracle Cloud Compute

Oracle Cloud Compute encompasses a variety of cloud computing options set to meet the needs of small-scale applications to enterprise-grade workloads. It’s available as both bare metal and virtual machine instances, giving users a flexible, scalable environment for running applications.

Oracle Cloud Functions

Oracle’s Function as a Service (FaaS) offering lets developers write and deploy code without worrying about underlying infrastructure. It’s based on the open-source Fn Project and allows developers to build, run, and scale applications in a fully managed serverless environment.

Use Cases and Industries

With its versatile offerings, Oracle Cloud Infrastructure is able to serve a wide range of industries such as application development, insurance, healthcare and e-commerce in both the private and public sectors. Use cases include:

• High-performance computing (HPC)
• Enterprise resource planning (ERP)
• Data backup and recovery
• Data analytics

Alibaba Cloud

Launched in 2009, Alibaba Cloud is the cloud computing faction of the Alibaba Group. As the leading cloud provider in China and among the top global providers, Alibaba Cloud capitalizes on Alibaba’s massive scale and experience with e-commerce and data processing. Services include the following:

ApsaraDB

ApsaraDB is a suite of managed database services that cover a wide range of database types including relational, NoSQL and in-memory databases. These services handle database administration tasks, allowing developers to focus on their applications rather than database management.

Alibaba Object Storage Service

Alibaba Object Storage Service (OSS) is an easy-to-use service that enables users to store, backup and archive large amounts of data in the cloud. It is highly scalable, secure, and designed to store exabytes of data, making it ideal for big data scenarios.

Alibaba Elastic Compute Service

Alibaba Elastic Compute Service (ECS) provides fast memory and flexible cloud servers, allowing users to build reliable and efficient applications with ease. ECS instances come in a variety of types, each optimized for certain workloads, making them versatile for different application scenarios.

Use Cases and Industries

In essence, Alibaba Cloud’s extensive services, coupled with its strong presence in Asia, make it a compelling choice in the public cloud market. It also serves a multitude of data-heavy industries such as technology companies, media and entertainment, financial services and education. Use cases include:

• E-commerce platforms
• Big data analytics and processing
• AI and machine learning models

Emerging Public Cloud Providers

The booming market and demand for public cloud have opened the doors for numerous technology companies to start offering their own cloud computing and storage solutions. The focus of emerging cloud providers tends to be on providing straightforward, scalable, and affordable cloud services to small and midsize businesses, and key players in addition to the ones covered in this article include DigitalOcean, Linode and Vultr. All offer developer-friendly features at affordable rates alongside high-quality customer service and support.

Factors to Consider When Choosing a Public Cloud Provider

When choosing a provider of public cloud solutions, there are several factors to consider.

Scalability and performance

The cloud service provider must be able to handle workloads and be able to accommodate growth and changes as business grows.

Security

Providers must be compliant with local and federal data security and privacy regulations. Additionally, they should be able to protect data against attacks, leaks and breaches.

Pricing flexibility

Cloud services are most known for their flexible, pay-as-you-go pricing models. Multiple tiers at varying costs allow businesses to access only the resources they need.

Integration and customer service

A public cloud solution should be compatible with existing and legacy systems, ensuring seamless integration, and should include reliable customer support and service to ensure access to solutions and assistance.

Bottom Line: Public Cloud Providers

The public cloud market offers a diverse range of options, each with its own strengths and trade-offs. AWS, Microsoft Azure, GCP, IBM Cloud, Oracle Cloud Infrastructure and Alibaba Cloud are major players, each serving a multitude of industries with a broad array of services. Simultaneously, emerging providers offer compelling alternatives, especially for certain use cases or customer profiles.

When choosing a provider, considerations over scalability, performance, security, cost, integration and support are key. By understanding these factors, businesses can make informed decisions and choose the public cloud provider that best meets their specific needs.

Particularly with innovations like the cloud, edge computing, Internet of Things (IoT) devices, and streaming, big data has become more prevalent for organizations that want to better understand their customers and operational potential. 

Big Data Trends: Table of Contents

• Real Time Analytics
  
• Stronger Reliance on Cloud Storage
  
• Ethical Customer Data Collection
• AI/ML Powered Automation
• Big Data in Different Industries
• Challenges in Big Data
• Bottom Line: Big Data Trends

Real Time Analytics

Real time big data analytics – data that streams moment by moment – is becoming more popular within businesses to help with large and diverse big data sets. This includes structured, semi-structured, and unstructured data from different sizes of data sets.

With real time big data analytics, a company can have faster decision-making, modeling, and predicting of future outcomes and business intelligence (BI). There are many benefits when it comes to real time analytics in businesses:

• Faster decision-making: Companies can access a large amount of data and analyze a variety of sources of data to receive insights and take needed action – fast.
• Cost reduction: Data processing and storage tools can help companies save costs in storing and analyzing data. 
• Operational efficiency: Quickly finding patterns and insights that help a company identify repeated data patterns more efficiently is a competitive advantage. 
• Improved data-driven market: Analyzing real time data from many devices and platforms empowers a company to be data-driven. Customer needs and potential risks can be discovered so they can create new products and services.

Big data analytics can help any company grow and change the way they do business for customers and employees.

For more on structured and unstructured data: Structured vs. Unstructured Data: Key Differences Explained

Stronger Reliance On Cloud Storage

Big data comes into organizations from many different directions, and with the growth of tech, such as streaming data, observational data, or data unrelated to transactions, big data storage capacity is an issue.

In most businesses, traditional on-premises data storage no longer suffices for the terabytes and petabytes of data flowing into the organization. Cloud and hybrid cloud solutions are increasingly being chosen for their simplified storage infrastructure and scalability.

Popular big data cloud storage tools:

• Amazon Web Services S3
• Microsoft Azure Data Lake
• Google Cloud Storage
• Oracle Cloud
• IBM Cloud
• Alibaba Cloud

With an increased reliance on cloud storage, companies have also started to implement other cloud-based solutions, such as cloud-hosted data warehouses and data lakes. 

For more on data warehousing: 15 Best Data Warehouse Software & Tools

Ethical Customer Data Collection 

Much of the increase in big data over the years has come in the form of consumer data or data that is constantly connected to consumers while they use tech such as streaming devices, IoT devices, and social media. 

Data regulations like GDPR require organizations to handle this personal data with care and compliance, but compliance becomes incredibly complicated when companies don’t know where their data is coming from or what sensitive data is stored in their systems. 

That’s why more companies are relying on software and best practices that emphasize ethical customer data collection.

It’s also important to note that many larger organizations that have historically collected and sold personal data are changing their approach, making consumer data less accessible and more expensive to purchase. 

Many smaller companies are now opting into first-party data sourcing, or collecting their own data, not only to ensure compliance with data laws and maintain data quality but also for cost savings.

AI/ML-Powered Automation

One of the most significant big data trends is using big data analytics to power AI/ML automation, both for consumer-facing needs and internal operations. 

Without the depth and breadth of big data, these automated tools would not have the training data necessary to replace human actions at an enterprise.

AI and ML solutions are exciting on their own, but the automation and workflow shortcuts that they enable are business game-changers. 

With the continued growth of big data input for AI/ML solutions, expect to see more predictive and real-time analytics possibilities in everything from workflow automation to customer service chatbots.

Big Data In Different Industries 

Different industries are picking up on big data and seeing many changes in how big data can help their businesses grow and change. From banking to healthcare, big data can help companies grow, change their technology, and provide for their data.

Banking

Banks must use big data for business and customer accounts to identify any cybersecurity risk that may happen. Big data also can help banks have location intelligence to manage and set goals for branch locations.

As big data develops, big data may become a basis for banks to use money more efficiently.

Agriculture

Agriculture is a large industry, and big data is vital within the industry. However, using the growing big data tools such as big data analytics can predict the weather and when it is best to plant or other agricultural situations for farmers.

Because agriculture is one of the most crucial industries, it’s important that big data support it, and it’s vital to help farmers in their processes. 

Real Estate And Property Management 

Understanding current property markets is necessary for anyone looking, selling, or renting a place to live. With big data, real estate firms can have better property analysis, better trends, and an understanding of customers and markets.

Property management companies are also utilizing their big data collected from their buildings to increase performance, find areas of concern, and help with maintenance processes.

Healthcare

Big data is one of the most important technologies within healthcare. Data needs to be collected from all patients to ensure they are receiving the care they need. This includes data on which medicine a patient should take, their vitals are and how they could change, and what a patient should consume. 

Going forward, data collection through devices will be able to help doctors understand their patients at an even deeper level, which can also help doctors save money and deliver better care.

Challenges in Big Data

With every helpful tool, there will be challenges for companies. While big data grows and changes, there are still challenges to solve.

Here are four challenges and how they can be solved:

Misunderstanding In Big Data

Companies and employees need to know how big data works. This includes storage, processing, key issues, and how a company plans to use the big data tools. Without clarity, properly using big data may not be possible.

Solutions: Big data training and workshops can help companies let their employees learn the ins and outs of how the company is using big data and how it benefits the company.

Data Growth

Storing data properly can be difficult, given how constantly data storehouses grow. This can include unstructured data that cannot be found in all databases. As data grows, it is important to know how to handle the data so the challenge can be fixed as soon as possible.

Solutions: Modern techniques, such as compression, tiering, and deduplication can help a company with large data sets. Using these techniques may help a company with growth and remove duplicate data and unwanted data.

Integrating Company Data

Data integration is necessary for analysis, reporting, and BI. These sources may contain social media pages, ERP applications, customer logs, financial reports, e-mails, presentations, and reports created by employees. This can be difficult to integrate, but it is possible.

Solutions: Integration is based on what tools are used for integration. Companies need to research and find the correct tools.

Lack Of Big Data Professionals

Data tools are growing and changing and often need a professional to handle them, including professionals with titles like data scientists, data analysts, and data engineers. However, some of these workers cannot keep up with the changes happening in the market.

Solutions: Investing money into a worker faced with difficulties in tech changes can fix this problem. Despite the expense, this can solve many problems with companies using big data.

Most challenges with big data can be solved with a company’s care and effort. The trends are growing to be more helpful for companies in need, and challenges will decrease as the technology grows. 

For more big data tools: Top 23 Big Data Companies: Which Are The Best?

Bottom Line: Growing Big Data Trends

Big data is changing continuously to help companies across all industries. Even with the challenges, big data trends will help companies as it grows.

Real time analytics, cloud storage, customer data collection, AI/ML automation, and big data across industries can dramatically help companies improve their big data tools.

What is MySQL?

MySQL is a relational database management system (RDBMS) that is free, open-source, and uses various proprietary licenses, including GNU General Public License (GPL). As an RDBMS, MySQL uses SQL to manage data inside a database. It organizes correlated data into one or more data tables, and this correlation helps structure the data.

It allows programmers to use SQL to create, modify, and extract data from the relational database. By normalizing data in the rows and columns of the tables, MySQL turns into a scalable yet flexible data storage system with a user-friendly interface that can manage lots of data.

MySQL also controls user access to the database as an added security measure, managing users and providing network access based on administrator rules. And it facilitates the testing of database integrity and the creation of backups.

While MySQL is normally accessed using SQL, it is often used with other programs as a component of various technology stacks, including LAMP (Linux, Apache, MySQL, and Perl/PHP/Python). As a result, several web applications that require relational database capabilities run on databases that use MySQL, including Drupal, Joomla, phpBB, and WordPress. Some popular websites even include Facebook, Flickr, Twitter, and YouTube.

What Makes MySQL So Popular?

MySQL is one of many RDBMSs available in the market. Still, it is among the most popular ones — second only to Oracle Database when compared using critical parameters like search engine results, LinkedIn profiles, and frequency of mentions on online forums. In addition, the reliance of major tech giants on MySQL further solidifies its popularity.

Although the database management industry is dominated by technology behemoths like Microsoft, Oracle, and IBM, free and open-source database management systems (DBMSs) such as Apache Cassandra, PostgreSQL, and MySQL remain highly competitive.

Here are four primary reasons for the incredible popularity of MySQL.

Easy to Use

MySQL is an easy-to-use and flexible RDBMS. Within 30 minutes of starting MySQL’s simple installation process, you’re able to modify source code to meet your needs. And as a free, open-source system, you don’t need to spend money for this level of freedom, including upgrading to an advanced version.

Secure

While choosing the right RDBMS software, the security of your data must be your priority. Fortunately, MySQL always prioritizes data security with its access privilege system and user account management. MySQL also offers host-based verification and password encryption.

High Performance

A server cluster backs MySQL. Therefore, MySQL offers smooth assistance with optimum speed, whether you store massive amounts of big data or perform intensive business intelligence (BI) activities.

Industry Standard

MySQL has been in the field for many years, turning it into an industry standard. It also means there are abundant resources for skilled developers. In addition, rapid developments in MySQL are possible anytime, and users can get freelance software experts for a smaller fee.

Top 8 Advantages of MySQL

1. Open Source

MySQL is one of the most popular choices for organizations or businesses regarding software as a service. Its community edition is freely available for anyone to use and modify, offering superior speed, scale, and reliability. This can be extremely beneficial, especially when businesses want to avoid paying licensing fees.

Since the source code is available for anyone to view and modify, developers can make changes to their software to suit their specific needs. This flexibility can benefit businesses with unique requirements or if there is a need to integrate the software with other tools or systems.

2. Data Security

MySQL is the most secure database management system in the world. The recent version of MySQL offers data security and transactional processing support that can significantly benefit any business, especially e-commerce businesses that carry out frequent monetary transactions.

3. Scalability on Demand

Scalability on demand is the hallmark feature of MySQL. It manages deeply embedded applications using a shallow footprint, even in databases that store terabytes of data. Moreover, MySQL offers customized solutions to e-commerce enterprises with specific database requirements.

4. Higher Efficiency

MySQL has several unique features, including a distinct storage engine software. It allows system administrators to configure the MySQL database server for flawless performance. It doesn’t matter if it is an e-commerce web application receiving a million daily queries or a high-speed transactional processing system.

MySQL is created to meet the increasing demands of almost every application and to ensure full-text indexes, optimum speed, and distinct caches for improved performance.

5. 24×7 Server Uptime

MySQL guarantees 24/7 uptime. It also offers a wide array of high-availability database solutions, including master/slave replication configurations and specialized server clusters.

6. Complete Transactional Support

MySQL is the number-one transactional database engine in the world. Its features include full atomic, consistent, isolated, durable, and multi-version transaction support as well as unrestricted row-level locking. Owing to these unique features, MySQL is a one-stop solution for comprehensive data integrity that ensures instant deadlock identification through server-enforced referential integrity.

7. Comprehensive Workflow Control

MySQL has easy usability with an average download and installation time of fewer than 30 minutes. In addition, it doesn’t matter whether your platform is Microsoft, Macintosh, Linux, or UNIX; MySQL is a comprehensive solution with self-management features. These features automate everything from configuration and space expansion to data design and database administration.

8. Lower Total Cost of Ownership (TCO)

When enterprises migrate from current database applications to MySQL, they save a great deal on total cost of ownership. They can also save money on new projects. The reliability and ease of management of MySQL can save the money and time spent on troubleshooting that is otherwise spent in fixing downtimes and performance issues.

3 Tips on Enhancing MySQL Performance

Today, almost every open-source web application uses MySQL. It’s compatible with every hosting provider and is extremely easy to use. But, if your web application or e-commerce website is performing poorly, here are three performance hacks you should try.

1. Performance Fine-Tuning

You can improve your web application’s performance by fine-tuning your high-availability proxy or HAProxy instances. In addition, use updated load-balancing software to optimize your database and speed up your server. Database load-balancing software is designed to bring agility and scalability to expand capabilities if needed. It can meet unplanned performance demands in the future as well.

2. Security Audits

Denial-of-service (DoS) attacks and spamming can wreak havoc on your database server. But, solid load-balancing software helps to easily prevent performance issues and increase uptime. It also ensures automatic failover and timely security updates.

3. Queries Optimization

Database optimization tools or techniques can only help fix the server load if websites and applications are coded well. But, SQL server load-balancing software can help in greater lengths. It is a one-stop solution for uptime maintenance, data consistency, performance enhancement, and reducing service costs.

Moreover, it ensures continuous availability for an enhanced customer experience. In short, MySQL does everything from running health checks to lowering the query wait time and evenly distributing the load across multiple servers.

Who Shouldn’t Use MySQL?

There are several sound reasons for not using MySQL; although, many are based on a misconception. So before implementing MySQL, go through these reasons and check whether they can apply to your enterprise. More than that, rejection of any database technology should be based on solid reasons rather than on the opinion of a database administrator (DBA).

Use of GPL

It is the biggest reason for not using MySQL. A GPL license is an advantage for many, but software with a GPL license may not suit specific environments. In these situations, a commercial license must be preferred if the Berkeley Source Distribution (BSD) license of PostgreSQL is still widely “open.”

In instances where MySQL is not free, GPL may not fit those situations well. For example, if you want to distribute the license for the database along with your project, the project must either be licensed under a similar compliant license, or you must get a commercial license for a fee.

A Proprietary Database Is Already Being Used

If an IT environment already has licensed Oracle and Sybase and several specific licenses for MS-SQL Server, then the MS-SQL instances are primarily the result of department staff’s ignorance of their paid licenses for other databases.

Adding any other database, including MySQL, is not wise in this situation, as DBAs already have to deal with many environments. Maintenance of a common database lessens the management burden. Further, if the company already paid for a proprietary database software license, a free, open-source database management system like MySQL is optional.

High-Volume Applications Need to Be Processed Quickly

MySQL needs to be optimized for high-volume applications and may not be suitable for fast data processing or streaming. For instance, users working on Internet of Things (IoT) systems require databases that can handle high-volume writes and reads as well as low latency.

For such cases, databases designed for IoT applications or real-time data processing work best. Although, it is important to note that MySQL can still be used for real-time data processing. However, it will require significant tweaks and optimizations to achieve the desired performance.

Complex Data Structures Require Advanced Querying

As a relational database management system, MySQL may not be suitable for applications with complex data structures that require advanced querying capabilities. For complex and high write-intensive workloads, NoSQL databases like MongoDB are more suited. Moreover, MySQL may not be suitable for applications with specialized needs, such as graph database capabilities or time series data.

There Is a Lack of Accessible Certification and Support

Certification is a favorite thing for some IT enterprises. Although MySQL has a certification training program, its availability is more challenging than Oracle or MS-SQL Server. In broader terms, even if IT professionals with MySQL skills are easy to find, certification or training programs are more difficult to reach out to with the availability of only a few third-party training sources. Larger IT businesses desire commercial database systems with enterprise experience, while some professionals with MySQL experience may have lesser depth.

Another related issue is the availability of qualified third-party support. The availability of support from the vendor mitigates the issue but only to some degree. The solution to the problem lies in solid third-party, on-site support.

Transparency

Microsoft, Oracle, and Sybase are publicly traded companies. On the other hand, MySQL is a private enterprise running on open-source technology. That means its financials or other business-related documents are optional by law to make it into the public record.

As a result, a listed company is relatively transparent, and this transparency provides certainty, stability, and security to some IT professionals or entrepreneurs. In other words, dealing with an immense reputed corporate entity helps some people sleep peacefully at night.

There Is a View That MySQL Doesn’t Scale Well

There is a widespread perception among many IT professionals that MySQL needs to scale better. However, it is a matter of debate, and most arguments discuss the difference between scaling up (vertical) and scaling out (horizontal) processes. Scalability is always one of the top reasons for using MySQL; although, it discusses scaling out more than scaling up.

It has been estimated (without much evidence) that most trained DBAs prefer a proprietary RDBMS such as Oracle to an open-source data management system. In a larger IT environment under the management of a full-time DBA, MySQL creates lesser interest.

In this situation, the criticism of MySQL’s scalability becomes irrelevant. When you have talents and monetary resources at your disposal, it is always better to equip them with the tools they are comfortable with. This approach always pays off in the long run.

Top 3 MySQL Alternatives

Of course, MySQL is a widely used database management system. On top of that, it can be easily installed and integrated with various applications. However, there are also other excellent alternative database management systems available in the market. Some of the well-known options include:

1. PostgreSQL

PostgreSQL, also known as Postgres, is an open-source yet powerful data management system that rivals any other paid RDBMS. It is compatible with Windows, Linux, Mac, and BSD.

Pros

• PostgreSQL has a holistic approach toward data integrity and robustness, and it is reflected by its comprehensive ACID compliance.
• The performance of PostgreSQL increases with each release. It is also proven by many benchmark tests.
• A strong open-source community backs PostgreSQL with tutorials, guides, and support.
• Updates, features, and fixes are released on time.
• PostgreSQL supports the JavaScript Object Notation (JSON) data type, an open data interchange format readable to humans and machines.
• PostgreSQL supports popular programming languages such as Perl and Python, allowing programmers to quickly transform a database server into a reliable service with complex business logic.

Cons

• PostgreSQL is not suitable for small applications, as they can’t handle its full power and complex operations.

2. MariaDB

MariaDB is a rapidly growing, MySQL-compatible, open-source database. It has free and paid versions along with a variety of plug-ins to provide more functionalities. Moreover, it works under GPL.

Pros

• MariaDB has strong open-source community support in development, documentation, troubleshooting, and tutorials.
• It has cutting-edge features such as a geographic information system (GIS).
• It has dynamic column support that allows a few NoSQL functionalities.

Cons

• An expensive horizontal scaling process.
• A lesser performance potential with bulky databases.
• Poor load and cluster management.
• Lesser advanced features.

3. SQLite

SQLite is an RDMS that is not a client-server database. However, SQLite and PostgreSQL have similar syntax.

Pros

• It has bindings for various programming languages, including BASIC, C, C++, Java, JavaScript, Perl, PHP, Python, Ruby, and Visual Basic.
• It is a lightweight software.
• SQLite is self-contained and requires little or no support from external libraries or operating systems.
• It is portable across multiple applications with cross-platform support.
• SQLite is reliable with little complications.
• It is ideal for testing and initial development stages.
• No configuration is needed.

Cons

• No multi-user support.
• Missing SQL features, such as FOR EACH STATEMENT and RIGHT OUTER JOIN.

Bottom Line: Advantages of MySQL

MySQL is a versatile, mature, open-source, and extensible database management system. Moreover, if we weigh the advantages of MySQL discussed above, five of its key features and benefits stand out from the rest.

MySQL is a reliable, easy-to-use, and secure RDBMS that is enterprise-ready with support from GPL and can scale with businesses after some fine-tuning and optimization.

Reasons are plenty for MySQL’s popularity. It’s an accessible database management system with improved capability to deal with modern problems. If you need something beyond the core functionality of MySQL, MariaDB may be a better option.