Enterprise software spendinghas doubled in the decade from 2009 to 2019, eventually amounting to 458 billion U.S. dollars, according to Statista. This surge in enterprise software spending reflects a growing demand for robust database management systems to support the increasingly complex operations of modern businesses.
What is a database?
Even though they come as practical tools, it is not always easy to explain what databases are conceptually. Databases are organized collections of structured information designed for efficient storage, retrieval, and management of data.
They may allow structuring information in tables, rows, or columns, allowing electronic filing systems to store, organize, and provide access to various types of data.
Databases are mostly used for data storage, but they are also relevant for data security, retrieval, and even relationships.
They are used not only for business applications but also for websites or mobile apps, to store users' information.
To further know about databases, let’s delve into the most popular ones and the key features of each one.
The Top 10 Most Popular Databases
1. MySQL
It is open-source, which means that it is free to use. MySQL supports SQL for data management, the standard language for interacting with relational databases. This makes it cost-effective, especially for small to medium-sized businesses that might not have the budget for expensive commercial databases.
As of January 2020, around 56 percent of software developers worldwide work with MySQL databases. This database engine is also known for its user-friendly interface along with its high performance, especially when handling heavy workloads.
MySQL can scale from small applications to large, enterprise-level systems and it runs various platforms, including Windows, Linux, macOS, and more, making it versatile and easy to deploy across different environments.
Additionally, it integrates well with other technologies, such as Python or Java.
MySQL can ensure high availability and fault tolerance. This means that even if one server fails, the system can continue running without interruption. Aligned with this, another benefit is that MySQL offers tools for easy backup and restore operations, allowing you to protect your data and recover it in case of unexpected events.
2. PostgreSQL
PostgreSQL is a powerful, open-source relational database engine known for its reliability, robustness, and extensibility. According to statistics provided by Statista, in 2020, 36.1% of developers worked with the open-source PostgreSQL databases. This makes it one of the most sought-after database skills among software developers worldwide.
PostgreSQL allows users to define custom functions, procedures, and data types, extending its functionality to meet specific needs.
Moreover, it follows the ACID (Atomicity, Consistency, Isolation, Durability) principles, ensuring data integrity and reliability.
It is also ideal for applications that work with semi-structured or flexible data as it offers native support for JSON (JavaScript Object Notation) and JSONB (Binary JSON).
Another advantage of PostgreSQL is that it includes advanced full-text search capabilities, allowing efficient and accurate searching within large volumes of text data.
What makes PostgreSQL thrive is that, even though it is an open-source package, it is reliable and has a high performance and extensive feature set.
Through replication methods, PostgreSQL ensures high availability and fault tolerance, allowing for continuous operation even in the event of hardware failures.
3. Microsoft SQL Server
Microsoft SQL Server is a robust relational database management system (RDBMS) developed by Microsoft.
It integrates with other Microsoft products and services, such as Azure cloud services, Visual Studio, Excel, and Power BI which makes it easy to build end-to-end solutions within the Microsoft ecosystem.
Its server can scale from small databases to large, enterprise-level solutions and it is designed for high performance, with features like column store indexes.
Microsoft SQL Server provides robust security features that ensure data is protected against unauthorized access and breaches.
Powerful BI tools such as Analysis Services, Reporting Services, and Integration Services are also included. These enable organizations to create rich data visualizations, reports, and data integration workflows.
This server offers a lot of disaster recovery solutions. It allows for automatic failover and readable secondary replicas for reporting and backup purposes.
In addition, it integrates with Python and R for machine learning tasks. This enables data scientists and analysts to perform advanced analytics directly within the database engine.
Such as PostgreSQL provides a user-friendly interface for database administration, development, and management tasks and it is compatible with existing SQL codebases and easier for developers familiar with SQL.
4. Oracle
Oracle Database is a widely-used relational database management system (RDBMS) known for its performance, scalability, and comprehensive feature set. It is designed for high performance, supporting efficient query processing, indexing, and caching mechanisms. It also offers features like in-memory columnar storage and partitioning for improved performance.
Oracle Database scales from small single-server deployments to large, distributed enterprise-level systems, such as MySQL. It can handle massive amounts of data and user connections, making it suitable for growth.
Its reliability comes from features like automatic storage management, automatic backup and recovery, and robust fault tolerance, as well as from its advanced security features, including data encryption, fine-grained access controls, auditing, and role-based security.
Oracle offers a wide range of data management features, such as partitioning, compression, and advanced indexing options. These features optimize storage and improve query performance.
This database also supports various programming languages (including PL/SQL), and offers built-in analytics capabilities with features like Oracle Analytics Cloud and Oracle Machine Learning.
As it has been implied, Oracle has a strong presence in cloud computing with Oracle Cloud Infrastructure (OCI). Oracle Database is fully integrated with OCI, providing seamless migration options and flexible deployment choices.
5. MongoDB
MongoDB is a popular NoSQL database known for its flexibility, scalability, and developer-friendly features. Its document-based data model allows for flexible and dynamic schemas.
MongoDB offers high performance for read and write operations. It utilizes in-memory computing, indexing, and efficient storage mechanisms to optimize query performance.
As a NoSQL database, MongoDB offers advantages like allowing developers to change data structures without downtime. It also supports nested data, arrays, and subdocuments, making it ideal for modern applications.
In addition, MongoDB's query language is intuitive and resembles JavaScript, making it easy for developers to work with. Its rich set of drivers and libraries supports various programming languages.
MongoDB's Aggregation Framework allows for advanced data processing and analysis within the database. It supports operations like filtering, grouping, sorting, and data transformations.
Another advantage is that MongoDB GridFS allows for storing and retrieving large files, such as images and videos, in a scalable manner. It breaks down large files into smaller pieces for efficient storage and retrieval.
With MongoDB Atlas Search (formerly known as Atlas Full-Text Search), users can perform complex text searches, including linguistic analysis and stemming.
As MySQL, MongoDB supports multiple platforms, including Windows, macOS, Linux, and cloud environments like AWS, Azure, and Google Cloud Platform.
It also has a strong and active community of developers and users. Additionally, it provides comprehensive documentation, tutorials, and online resources, as well as professional support and consulting services.
6. SQLite
SQLite is a lightweight, serverless, self-contained, and easy-to-use relational database management system. Despite its minimalistic footprint, it offers several benefits, particularly for applications with specific requirements.
For instance, it is designed to be embedded within applications. SQLite operates without a separate server process, allowing applications to interact directly with the database file.
In contrast with other databases, there is no need for server configuration or administration. Developers simply include the SQLite library in their application, and it can start managing data right away.
This database is lightweight in terms of memory and disk space usage. It's optimized for efficiency, making it fast for read and write operations, especially in scenarios with moderate data volumes.
SQLite is also highly portable and compatible with various platforms, including Windows, macOS, Linux, and mobile operating systems like Android and iOS, such as MongoDB.
In addition, such as PostgreSQL, SQLite supports ACID (Atomicity, Consistency, Isolation, Durability) properties, providing data integrity and reliability even in multi-user environments.
SQLite has minimal dependencies, making it easy to integrate into applications without worrying about external libraries or dependencies.
7. MariaDB
MariaDB is a popular open-source relational database management system (RDBMS) known for its performance, scalability, and compatibility with MySQL. As the latter, MariaDB is an open-source project, meaning it's free to use and has a strong community of developers contributing to its development and support.
MariaDB is designed as a drop-in replacement for MySQL. This means that applications and code written for MySQL can usually run on MariaDB without modification. It supports MySQL protocols and APIs, making migration from MySQL straightforward.
It is also optimized for high performance, offering efficient storage engines like InnoDB and MyRocks. It also provides features like query optimization, indexes, and caching mechanisms for improved query speed.
MariaDB supports scalability with features such as multi-threading, parallel replication, and clustering. It can handle large-scale databases and high-traffic applications.
Another advantage is that it offers robust security features including encryption and user authentication. This ensures data integrity and protection against unauthorized access.
MariaDB offers native support for JSON data types, allowing for efficient storage and querying of JSON documents as well as PostgreSQL.
This database has a growing community of developers and users. This implies that there are plenty of resources, forums, and documentation available for support and learning.
As many of the mentioned above, it is compatible with various operating systems including Windows, Linux, and macOS, making it versatile for different deployment environments.
Moreover, MariaDB is cost-effective for businesses that want a powerful database system without the licensing costs associated with proprietary databases.
8. Redis
Redis is an open-source, in-memory data structure store known for its speed, simplicity, and versatility. It is optimized for performance, offering extremely fast read and write operations. It stores data in memory, making it ideal for use cases that require low-latency access to data.
Related to the above, Redis keeps all data in RAM, which allows for lightning-fast data access. This is particularly advantageous for applications requiring rapid data retrieval, caching, and session storage.
Another quality is that Redis supports various data structures such as strings, hashes, lists, sets, and sorted sets. This versatility enables developers to model complex data patterns easily.
Moreover, it has a straightforward and intuitive API, making it easy for developers to learn and use. Its commands are simple and well-documented.
Redis provides options for data persistence, allowing data to be saved to disk periodically or on-demand. This ensures that data is not lost on server restarts.
In addition, Redis Sentinel and Redis Cluster provide high-availability solutions. Sentinel monitors Redis instances and performs automatic failover in case of node failures. Redis Cluster provides automatic partitioning and replication for fault tolerance.
The availability of client libraries for a wide range of programming languages makes it accessible and easy to integrate into different types of applications. As with many other databases, Redis has a large and active community of users and developers.
Due to its fast read and write capabilities, Redis is used for real-time analytics and tracking of metrics in applications.
9. Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine known for its scalability, speed, and versatility. It provides highly efficient full-text search capabilities, allowing users to search and analyze large volumes of structured and unstructured data quickly.
Elasticsearch can scale from a single node to thousands of nodes, allowing organizations to handle growing data volumes and user loads. In addition, it processes data in real-time, enabling organizations to perform real-time analytics, monitoring, and alerting on streaming data sources.
Elasticsearch offers near real-time search capabilities, ensuring that newly indexed documents are searchable within milliseconds. It also stores data in a document-oriented format (typically JSON), making it flexible and easy to work with for a wide range of use cases.
It also includes support for aggregations, enabling users to perform analytics, statistical calculations, and data summarization on indexed data.
Elasticsearch includes a variety of built-in analyzers for tokenization, stemming, and language detection. Users can also define custom analyzers to suit specific language or text processing requirements.
Its built-in mechanisms for high availability and fault tolerance include index replication, shard allocation awareness, and automatic node recovery.
Elasticsearch integrates with other components of the Elastic Stack, including Logstash for data ingestion, Kibana for data visualization, and Beats for lightweight data shippers. This integration provides end-to-end data processing and analytics capabilities.
Elasticsearch’s security features include role-based access control (RBAC), encryption, authentication, and audit logging, ensuring data confidentiality and integrity.
As we have seen with other databases above, Elasticsearch also benefits from a large and active community of users and developers. There are extensive documentation, forums, tutorials, and online resources available for support and learning.
10. IBM Db2
IBM Db2, like many others mentioned above, is a robust, enterprise-grade relational database management system (RDBMS) known for its performance, reliability, and advanced features.
Db2 is optimized for high performance, offering efficient query processing, indexing, and caching mechanisms. It supports multithreading, parallelism, and optimization techniques for improved query speed.
In addition, it includes data compression features to reduce storage requirements and improve query performance. This is particularly useful for large databases with high volumes of data.
Like Elasticsearch, it supports scalability and can handle large-scale databases and high-traffic applications. It offers features like partitioning, which allows data to be distributed across multiple servers for improved performance and capacity.
Its high availability and disaster recovery are key. This includes database mirroring, log shipping, and clustering.
Robust security features also come as an advantage. This includes encryption, role-based access control (RBAC), auditing, and fine-grained access controls. It complies with various industry security standards, such as HIPAA and GDPR.
Db2 supports database partitioning, which allows large tables to be divided into smaller, more manageable partitions. This improves query performance and simplifies data management.
Regarding indexing options, it includes clustered and non-clustered indexes, to optimize query performance. Indexes can be customized based on specific query patterns and workload characteristics.
Db2 offers a range of development tools, including IBM Data Studio and IBM Data Server Manager, for database administration, development, and performance tuning. In addition, it integrates with other IBM products and services, such as IBM Cloud Pak for Data, IBM Watson, and IBM Cognos Analytics. This allows for end-to-end data processing, analytics, and visualization within the IBM ecosystem.
A highlight of Db2 is that it integrates with IBM Watson Machine Learning, enabling users to build and deploy machine learning models directly within the database. This allows for predictive analytics and real-time insights into the data.
Moreover, it is compatible, as many other databases, with various operating systems, including Windows, Linux, and Unix, as well as cloud environments such as IBM Cloud and Amazon Web Services (AWS).
IBM also provides extensive support for Db2, including documentation, forums, training, and professional services. Users have access to a wealth of resources for troubleshooting, learning, and optimizing Db2 deployments.
Another special highlight is that Db2 has a long history of industry adoption, particularly in sectors such as finance, healthcare, and government, where data integrity, reliability, and security are critical.
The Impact of Database Trends on the Industry
Database trends have a significant impact on the industry, shaping how organizations store, manage, analyze, and derive value from their data.
The impact of database trends on industries influences how businesses operate, make decisions, and deliver value to customers.
First and foremost, database trends shape industries in terms of performance and efficiency, for instance, through quality control.
Improving scalability and AI integration is also key in database companies' competition, as it may make a difference for a certain industry. Scalability is key when managing growing data volumes, while automated decision-making is essential given the amount of information bigger industries have to handle.
Regarding security matters, having strengthened data encryption is also relevant as it is what gives a certain company the reliability that is a clue when handling clients’ information.
Choosing the Right Database for Your Needs
The choice of which database is the best one for you depends on your business needs:
If you are prioritizing memory, Redis may be the right one
If you are in need of flexibility, MongoDB could be the right choice
Oracle is better if performance is your aim
If you are looking for interaction between databases, MySQL could suit you better
No matter what database you choose, make sure to list your business needs, scale, and aims first so that choosing a popular though unsuitable database ends up becoming a problem.
Future Developments in Database Technology
Even though there is no clear path towards the future, there are many developments that could be key in the database world.
Augmented analytics, for instance, integrates AI and machine learning into the analytics process, automating data preparation, insight generation, and natural language processing for easier interpretation.
Blockchain databases are also relevant as their technology offers immutable, transparent, and secure data storage, particularly suitable for industries requiring tamper-proof records.
Just as Blockchain databases, Privacy-Preserving Databases also tend to be transparent within their processes as they focus on preserving data privacy by implementing techniques such as homomorphic encryption, differential privacy, and secure multiparty computation.
From augmented analytics to blockchain-based security, these developments promise to transform how organizations manage, analyze, and derive insights from their data. Embracing these advancements will empower businesses to innovate, stay competitive, and unlock new possibilities for companies to develop in the best way.
