Without databases, most software applications would not be possible. Databases are the cornerstone of every type and size application: web-based ones for data storage through enterprise-level projects that require high volume amounts or speed in transferring large chunks across networks; an embedded system where you can find low-level interfaces with tight timing requirements unlike anything else compared to real-time systems. Of course, we can’t miss Artificial Intelligence, Deep Learning, Machine Learning, Data Science, HPC, Blockchain, and IoT, which totally relies on data and definitely need a database to store them and process them later.
Now, let’s read about some of the essential types of popular databases.
The Oracle: Oracle has offered a robust, enterprise-grade database to its customers for almost four decades. It is still the most used database system, according to DB-Engines, despite heavy competition from open-source SQL databases and NoSQL databases. It has C, C++, and Java as built-in assembly languages. This database’s most recent edition, 21c, contains a slew of new features. It is compact, fast, and has many extra features, such as JSON from SQL.
MySQL: Web development solutions are the most prevalent use of this database. MySQL is a structured query language that is built in C and C++. MySQL’s enterprise-grade functionality and free, flexible (GPL) community license, as well as an updated commercial license, immediately made it famous in the industry and community. The database’s key goals are stability, robustness, and maturity. There are several editions of the SQL database, each with its unique set of features.
PostgreSQL: PostgreSQL is the most advanced open-source relational database. It’s a C-based database management system used by companies that deal with large volumes of data. This database administration software is used in various gaming apps, database automation tools, and domain registrations.
Microsoft SQL Server: MS SQL is a multi-model database that supports Structured Data (SQL), Semi-Structured Data (JSON), and Spatial Data. It is supported by Windows and Linux operating systems. It was the most popular commercial mid-range database on Windows systems for the past three decades. Microsoft SQL Server has undergone considerable improvements and overhauls throughout the years while not being as inventive or advanced as others. It can be very beneficial when the development platform is strongly coupled with other Microsoft Products.
MongoDB: Using Object-Oriented programming languages to load and retrieve data in RDBMS requires additional application-level mapping. In 2009, MongoDB was released as the first Document Database to address those difficulties, particularly processing Document Data. It’s utilized for semi-structured data where consistency trumps availability.
IBM DB2: DB2 is a multi-model database that supports structured (SQL), semi-structured (JSON), and graph data. It’s also a Converged database with great OLAP functionality owing to IBM BLU Acceleration. DB2 LUW was also available for Windows, Linux, and Unix.
Redis: It’s a well-known open-source database. Redis can be used as a distributed key-value database that runs in memory. It can also be used as a message broker and distributed cache. It can handle massive amounts of data. It supports many data structures.
Cassandra: It’s a widely used database with an open core, distributed, vast column store, and Apache License 2.0. This is a scalable database management software frequently used in businesses to handle large amounts of data. Its decentralized database (Leaderless) with automatic replication is one of its primary advantages, allowing it to become fault-tolerant with no failures. Cassandra Query Language (CQL) is a user-friendly and SQL-like query language.
Elasticsearch: Released in 2010, Elasticsearch is an open-source, distributed, multi-tenant full-text search engine with a REST API. It also supports both structured and schema-less data (JSON), which is ideal for analyzing Logging and Monitoring data. I can handle significant amounts of data.
MariaDB: MariaDB is a Relational DBMS that works with the MySQL protocol and clients. The MySQL server can be easily changed with MariaDB without requiring any code changes. It is more community-driven as compared to MySQL. MariaDB’s “ColumnStore” Storage Engine combines columnar storage with a massively parallel distributed data architecture. Through its MaxScale and Spider Engine, it also provides horizontal partitioning. As a result, MariaDB can be used as an OLAP database.
Firebirdsql: Firebird is a free SQL relational database management system. It is supported by Windows, Mac OS X, Linux, and many Unix platforms. This fundamental database management system software solution has enhanced the multi-platform RDBMS.
OrientDB: OrientDB is an open-source NoSQL multi-model database. It’s a database management system that supports graph, document, key-value, and object-oriented database models, improving efficiency, security, and scalability.
DynamoDB: Amazon’s DynamoDB is a nonrelational database. It is a serverless, fully managed key-value NoSQL database built to run high-performance applications at any scale. Built-in security and in-memory caching, and consistent latency are all aspects of this database application.
SQLite: Created in 2000, SQLite is an open-source relational database management system with an integrated SQL database. It is a C-language library. It’s a fantastic database that doesn’t require configuration, server, or installation. SQLite is included in all mobile phones and most laptops, and a slew of other applications that people use daily.
Neo4j: Neo4j is a Java-based open-source NoSQL graph database. It employs the Cypher query language, which touts as the most efficient and expressive way to express relationship queries. Data is recorded as graphs rather than tables in this database management system software.
Consultant Intern: Currently in her third year of B.Tech from Indian Institute of Technology(IIT), Goa. She is an ML enthusiast and has a keen interest in Data Science. She is a very good learner and tries to be well versed with the latest developments in Artificial Intelligence.