The 12 best open source database software in 2023

The 12 best open source database software in 2023

March 24, 2023
March 24, 2023
The 12 best open source database software in 2023
In this article, we’ll explain how open source database software works and list the top 12 open source database software, along with their key features.‍

Business applications and programs use databases to store the data they collect. While organizations previously relied on database suites like SQL Server and Oracle, they now have open source databases for all their data needs. An open source database is flexible and affordable. Organizations can build the database they need without exceeding their budget.

They can store and organize both structured and unstructured data. For most companies, the most significant decision these days is which of the hundreds of open source database software options to choose from. In this article, we’ll explain how open source database software works and list the top 12 open source database software, along with their key features.

What is open source database software?

An open source database software is any database system with a codebase that’s free to use, redistribute and download. Developers can leverage open-source software’s existing features to create custom database applications that match their organization’s specific needs.

Typically, databases are classified as either relational or non-relational (NoSQL). A relational database uses key-value pairs to store structured data in columns and rows. A NoSQL database handles both structured and unstructured data by using different data storage architectures, like document storage and graph databases.

An open source data management system provides developers with the software layer they need to monitor and manage data on their own terms. They can use features from both database types to create an efficient database management system (DBMS) for the business.

Open source vs closed source database

Open source database software lets developers create, monitor and modify platform features to match their needs. Closed source database programs or commercial database software, like Fivetran, are proprietary platforms that can only be accessed and used by paying a subscription or one-time fee.  

Commercial data integration solutions like this benefit organizations since they are fully managed. The vendor has a team of developers to take care of maintenance, updates and any backend issues. Data teams also get access to additional features, dedicated customer support, easy integrations and strong security.

Organizations typically use a mix of both open source and proprietary software for data management. However, the best solution is a platform with advanced features that allow developers to build additional features. For example, a data tool like Fivetran enables this through custom connectors.

Open source database software use cases

Open source database software is commonly used for: 

  • Data storage: Since the code for an open source database is easily accessible, data engineers can use these platforms to store data securely. They can also build integrations with security solutions to increase privacy.
  • Data science: Open source database solutions can be altered to match a business’ specific analysis needs using popular code languages like Python and R.
  • Key-value storage: Key-value pair (KVP) storage is usually a resource-heavy task. But, it can be optimized by developers by tweaking an open source database. 
  • Other technology: In some cases, open source databases allow for easier integration with other applications, such as ones used for graphing and artificial intelligence (AI).

The top 12 open source database software

Data teams have many open source database solutions to choose from.

Here’s a look at the top 12 open source data software.

1. MySQL

MySQL is the most popular open source database. It is ACID compliant and supports large databases that power ecommerce, web, online transaction processing (OLTP) and SaaS applications.

MySQL has an integrated development, design and administration environment, named MySQL Workbench, that makes it easy to customize and scale. MySQL’s Enterprise edition boasts a record benchmark of up to 1.8 million queries/second, faster performance and information schemas, and invisible indexes for managing database changes.

2. PostgreSQL

PostgreSQL is another popular open source database system that offers reliability, advanced features and flexibility.

PostgreSQL works with most standard programming languages and is fully compatible with SQL. It also integrates with many third-party tools, including Fivetran. It has many unique offerings, like support for Array data types, asynchronous replication and native support for document and key-value storage. PostgreSQL is the best alternative for MySQL, but it only works for relational data models. It’s also not ideal for analytics or use cases that are read-heavy and do not work with strict schema.

3. MariaDB

MariaDB is considered a clone of MySQL. The database software was created by MySQL founder Michael Widenius after Oracle acquired MySQL in 2010. Despite being built on the same code base, the two databases have developed differently over the years. 


MariaDB boasts some unique features. It allows pluggable storage engines so data teams can go beyond basic transactional processing. For example, teams can use ColumnStore for high-volume data warehousing and distributed storage. ColumnStore can also be used for columnar analytics and hybrid smart transactions (HTAP). The database also uses the Galera Cluster engine to improve data replication and supports many JSON functions. If you want a free replacement for MySQL, MariaDB is an excellent option that’s constantly working on adding new innovative features.

4. MongoDB

MongoDB is a distributed database known for its flexibility and speed. It’s a document store that stores data in clusters instead of traditional tables.

The database solution offers a flexible schema so that analysts can run ad-hoc queries for real-time analytics. Analysts can also modify it to cater to many specialized use cases. Data teams can use MongoDB with Fivetran to get instant access to information. They can also take advantage of auto-scaling, full-text searches, serverless instances and seamless data distribution via the fully managed service Atlas.

5. SQLite

SQLite is a C-language library used as a relational database storage engine. It is a complete SQL database, with tables, indices, triggers and views, and can be stored in a single disk with the .sqlite extension. This file can be placed anywhere in your file system.

Despite the “Lite” in its name, SQLite can support JSON functions, up to 32,000 columns in a table and unlimited rows, multi-column indexes, virtual tables and much more. This feature-packed database engine is a good option for data teams with a relatively simple app, like a medium-sized content management system (CMS). However, it does lack the crucial features of a full database solution.

6. CockroachDB

CockroachDB was built to make large-scale traditional SQL databases more scalable and reliable. The cloud SQL database uses 3x data replication and a self-healing infrastructure to prevent downtime or data loss.

CockroachDB is a fully-managed database service with a Postgres-compatible SQL syntax so developers can easily build and manage databases. It also enables multi-cloud global deployment and uses a pricing model where teams only pay for the resources they need.

7. Redis

Redis is an in-memory data store used as a database due to its high read and write speeds.

The platform provides native data types, like strings, sets and hashes, that can support many use cases. Redis can be used for streaming, messaging, caching, event processing, queuing and more. Redis supports over 50 programming languages and hosts a module API that developers can use to build custom extensions that extend its capabilities.

8. CouchDB

CouchDB by Apache is a database replication tool that prevents data loss in case of network loss or any other pipeline failure.

The CouchDB Replication Protocol uses a cluster of nodes to sync data between the source and destination database. It compares the data on these two versions using Changes Feeds. Then it uses batch replication to ensure that the destination data matches the source. This technology is similar to the change data capture (CDC) by Fivetran but is less advanced. CouchDB offers offline syncing, easy clustering and high reliability. However, the tool is not worth it if you have better options for database replication, like Fivetran, since it uses a large amount of storage for redundant data copies. This significantly slows down write speeds.

9. Neo4j

Neo4j is a popular graph database. In this type of database, data is not segregated into rows, columns and tables. Instead, data is stored using nodes and relationships. Every node is related to one or more nodes to form a connected database.

A data model like this is impossible with SQL or document-store databases. A graph database bypasses the need for indexes and, by doing so, is a lot faster than most relational databases. Developers can easily see and run queries on connected data, which leads to improved query planning and faster retrieval of relevant data. Neo4j is also scalable and boasts enterprise-grade privacy and security measures.

10. FirebirdSQL

Firebird is a lesser-known relational database management system with a light footprint on your storage and a truckload of features.

Firebird provides the same features as most SQL databases and is fully compatible with MySQL. However, it can still help organizations looking to run large databases via a lightweight yet powerful platform. For example, Streamsoft uses software based on the Firebird database server to manage a 100GB-sized database with 150 end-user stations and 4000 daily recorded documents. It can also be used as a database for desktop apps that need to scale, like LibreOffice.

11. OrientDB 

OrientDB classifies itself as a multi-model open source NoSQL database management system. It works with several data models, including graphs, objects, key values and documents, but is primarily used as a graph database.

The creators of OrientDB created the solution with speed and performance in mind. As such, the operational database enables rapid read and write operations. It can store up to 120,000 records per second. Graph databases, like OrientDB and Neo4j, usually power applications related to social media, traffic management, banking and finance and other internal databases.

12. Cassandra

Cassandra is an open source NoSQL database that’s lightweight and distributed.

Distributed databases enable the rapid, ad-hoc organization and high-volume analysis of diverse data types. This is great for big data analytics that requires scaling to accommodate ever-growing data sets. Cassandra uses nodes to enable effortless scaling by developers using off-the-shelf tools. And it promises no downtime during the scaling process. You can also replicate data to multiple nodes to ensure reliability and fault tolerance. The distributed system is self-healing and data replication prevents data loss.

How to choose the right open source database software

Apart from the 12 options listed above, data teams have hundreds of other software and must consider proprietary software as well. When selecting the database solution for your team, keep these four factors in mind:

  • Understand your workload: Determine what you want to use your database for. Consider the data types, query types, performance expectations and business requirements for your organization. Now, look at the solution that can best support these parameters.
  • Know your use cases: Choose a database that can support multiple use cases without interruption or downtime. As business goals change frequently, so will data models and use cases. Choosing a rigid tool will force data migration to another database.
  • Check for security features: Data security is a primary concern for organizations. Your database vendor must keep data safe from hackers and avoid exposing sensitive data during transit or rest. Secure log-ins, encryption, role-based access control and compliance are also important.
  • Consider the cost: While many open source databases are free, most charge fees for enterprise plans and additional features. MongoDB Atlas is an example of this. Those using open-source tools also have to consider the labor and time costs of building their own database instead of buying a fully-managed platform like Amazon Redshift.


Open source database software is great for data engineers that want the freedom and flexibility to build their own DBMS. They are also cost-effective, so organizations can drive data analytics without breaking the bank.

Closed-source software is usually paid and uses licensed source code. However, these platforms provide additional capabilities and are fully managed. So, organizations can set up a database and start data replication in minutes.

Fivetran has data connectors that support both open-sourced and proprietary database applications. These connectors enable developers to build and launch no-code, automated data pipelines in minutes.

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