15 Open Source Database Software Recommendations for 2025

The demand for reliable database software has increased significantly, with many organizations across industries depending heavily on data to make informed business decisions. By choosing the right database solution, you can ensure optimal performance, scalability, and security while managing large volumes of your organizational data.

However, with plenty of options available in the market, identifying the best fit for your specific needs can be difficult. This article provides a curated list of 15 open-source database software recommendations to help streamline your data management efforts.

What Is an Open-Source Database Software?

Open-source database software is a freely accessible data storage and retrieval system whose source code is publicly available for anyone to use, modify, and distribute. These databases are often developed by a community of developers and can be tailored to your requirements. They are cost-effective alternatives that support web development and data management applications.

Benefits of Using an Open-Source Database System

Unlike proprietary solutions, open-source database systems provide your organization’s data teams with better control over data pipelines. Some advantages of using these tools include:

No Vendor Lock-in

Open-source databases prevent you from being tied to a specific vendor's technology, policies, or pricing. This flexibility allows your organization to switch platforms or providers without any penalties or operational disruptions.

Transparency

Since the source code is available to everyone, your data teams can inspect it for security vulnerabilities, performance issues, or compliance needs. This level of transparency helps build trust and clarifies that there are no hidden processes or data-handling practices.

Community Support

Open-source software is usually backed by large, active communities of developers who offer peer support through bug fixes, code enhancements, and detailed documentation. This collaborative environment ensures regular updates and faster responses to your issues.

Cost-Effective

Compared to commercial solutions, using open-source databases can significantly lower upfront costs. This makes open-source tools a great choice for startups and organizations with limited budgets, enabling them to redirect their resources wherever necessary.

15 Best Open-Source Database Software for 2025

Many open-source database systems have features that can cater to your organization’s diverse and ever-evolving data needs. Below are 15 of the best open-source databases for you to consider in 2025.

#1. PostgreSQL

PostgreSQL is an advanced open-source object-relational database management system (ORDBMS) that supports both SQL and NoSQL data models. Its strong compliance with ACID properties ensures robust transactional capabilities. Postgres also employs multi-version concurrency control (MVCC) and allows multiple users to read and update data simultaneously.

Key Features of PostgreSQL

• Key-Value Storage: PostgreSQL implements key-value storage through its hstore and JSONB data types, enabling you to store schema-less data efficiently.‍
• Asynchronous Replication: With this feature, you can replicate data to standby servers without delaying the primary server’s operations.‍
• Advanced Indexing: The platform supports indexing techniques B-tree, GiST, hash, SP-GiST, and GIN for efficient Postgres full-text searches and data retrieval.  

Best Suited For

Organizations with applications that implement complex transactions, high levels of concurrency, and data warehousing.

Notable Users

Afilias, Cognitivity, Affymetrix, and Tsutaya are some of the popular organizations using PostgreSQL.

#2. MySQL

MySQL is a widely used client-server-based RDBMS with a multi-threaded SQL server. According to the report by OpenLogic, over 40% of organizations surveyed prefer MySQL as their go-to open-source database software. It is also a core component of the LAMP stack (a bundle of open-source technologies) comprising Linux, Apache, MySQL, and PHP/Perl/Python.

Key Features of MySQL

• Data Replication: MySQL supports data replication across multiple servers, enhancing performance, data availability, and fault tolerance.
• ‍Storage Engines: Based on your specific use case, MySQL offers several storage engines like InnoDB, MyISAM, Blackhole, Archive, and Network Database (NDB).‍
• Data Partitioning: With MySQL’s partitioning feature, you can divide large databases into smaller, more manageable chunks. This optimizes query execution and reduces disk I/O operations.

Best Suited For

E-commerce platforms, SaaS applications, Content Management Systems (CMS), and Online Transaction Processing (OLTP).

Notable Users

Sony, Airbnb, and the Bombay Stock Exchange (BSE) are some MySQL users.

#3. Apache Druid

Apache Druid is an open-source, real-time analytics database optimized for fast, interactive querying on large datasets. It allows you to execute sub-second OLAP queries on high-cardinality and high-dimensional streaming or batch data without caching queries in advance. Apache Druid also offers data protection services, such as automated recovery, continuous backups, and multi-node replication.

Key Features of Apache Druid

• Columnar Storage Format: Apache Druid allows you to load only the relevant columns required for a particular query, significantly reducing the time needed for scans and aggregations.‍
• Schema Auto-Discovery: The platform automatically detects and infers the schema of incoming data and updates the column names and data types accordingly.

Best Suited For

Event-oriented data and applications requiring real-time ingest, fast query performance, and high uptime.

Notable Users

Some popular organizations using Apache Druid are Alibaba, Confluent, Reddit, Flipkart, Oppo, and PayPal.

#4. TimescaleDB

TimescaleDB is an open-source time-series database built on PostgreSQL. It is designed to manage high-volume time-stamped data efficiently. With TimescaleDB, you can implement continuous aggregates that update incrementally and create useful summaries while minimizing resource consumption.

Key Features of TimescaleDB

• Hypertables: These are virtual tables that automatically partition and index time-series data for faster querying and storage efficiency.‍
• Gap Filling: TimescaleDB uses interpolation and last observation carried forward (LOCF) to simplify joining time series with varying or irregular sampling intervals.
• ‍Compression: You can compress your data chunk by 90% and reduce storage without compromising the data’s integrity. This is beneficial when you want to retain data for a longer period or improve query performance.

Best Suited For

Analyzing time-series data, like IoT sensor data, financial transactions, and network logs.

Notable Users

The European Space Agency (ESA), Octave, and Sakura Internet are the leading customers of TimescaleDB.

#5. MariaDB

MariaDB is a high-performance, open-source RDBMS known for its speed, scalability, and security. It is a community-developed fork of MySQL that allows you to perform both Online Analytical Processing (OLAP) and OLTP workloads. The platform maintains one-to-one compatibility with MySQL, streamlining data migration between them. However, they diverge significantly in their latest versions.

Key Features of MariaDB

• Pluggable Storage Engines: MariaDB offers different storage engines for different workloads, like Aria for read-heavy workflows, ColumnStore for OLAP processes, and Spider for federated table access.
• ‍Temporal Tables: You can track and query data changes over time and facilitate easy point-in-time analysis and audits.‍
• Data Masking: This feature enables you to hide sensitive data from unauthorized access and meets compliance requirements.

Best Suited For

Web applications, cloud-based applications, enterprise-grade systems, and data warehousing.

Notable Users

Samsung, DBS Bank, RedHat, and InfoArmor are some organizations using MariaDB.

#6. SQLite

SQLite is a lightweight, self-contained RDBMS widely used as a database for embedded systems. It is ACID-compliant and ensures data integrity and consistency even during system failures or power outages. SQLite is highly compatible with Android, iOS, Linux, and Windows, making it a versatile data solution.

Key Features of SQLite

• Full-Text Search: FTS5 is an SQLite extension that provides your applications with full-text search capabilities, such as tokenization and search results ranking.‍
• Usability: SQLite has a serverless architecture and requires zero-configuration efforts. You can start using it by simply including the library in your project.

Best Suited For

Mobile applications, IoT devices, web browsers, and systems that require local storage solutions.

Notable Users

Adobe, Apple, Facebook, Bosch, Google, and Microsoft are some popular customers of SQLite.

#7. YugabyteDB

YugabyteDB is an open-source, high-performance distributed SQL database designed for cloud-native applications. It combines the scalability of NoSQL systems and the transactional capabilities of relational databases. YugabyteDB allows you to manage globally distributed data workloads while offering fault tolerance and low-latency access across regions.

Key Features of YugabyteDB

• PostgreSQL Compatibility: YugabyteDB is fully compatible with PostgreSQL. It retains the familiarity of Postgres while allowing you to leverage its added features like observability, built-in connection management, and end-to-end security.‍
• Scalability: The platform automatically performs data sharding and enables you to scale your connections, data, reads, and writes operations without interrupting your applications.

Best Suited For

Edge and streaming applications, as well as business-critical applications requiring global distribution.

Notable Users

Some renowned users of YugabyteDB include Rakuten, Paramount, Kroger, and Trustt.

#8. Neo4j

Neo4j is a graph database that enables you to manage and query large networks of connected data. It uses a property graph model in which data is represented as nodes, relationships, and properties, making it highly efficient for real-time analytics. You can also scale out large graphs across multiple databases while maintaining optimal performance.

Key Features of Neo4j

• ACID Compliance: Neo4j ensures full ACID compliance, maintaining data integrity and reliability, even in highly transactional environments.‍
• Cypher Query Language: Neo4j uses Cypher, a declarative query language that employs ASCII-art syntax and helps you interact with and query the graph database with ease.‍
• Automated Rolling Updates: Neo4j offers continuous patches, updates, and upgrades without any downtime.

Best Suited For

Applications requiring complex relationship analysis, such as knowledge graphs, social networks, and recommendation engines.

Notable Users

NASA, IBM, eBay, Telenor, and Airbus are some organizations leveraging Neo4j.

#9. Apache Cassandra

Apache Cassandra is a scalable distributed NoSQL database that allows you to manage massive amounts of data across multiple servers. It operates on peer-to-peer architecture, where every node can perform read and write operations. This reduces the risk of a single point of failure and ensures fault tolerance and reliability across the system.

Key Features of Apache Cassandra

• Linear Scalability: With Apache Cassandra, you can scale horizontally by adding more nodes to the cluster. This enables you to handle increased data traffic without downtime.‍
• Tunable Consistency: Using this feature, you can prioritize either data availability or consistency, depending on your application's needs.‍
• Time-Series Data: Apache Cassandra allows you to efficiently model and manage time-series data from applications performing logging, monitoring, and IoT data processing.

Best Suited For

Applications requiring high availability, continuous uptime, and high throughput.

Notable Users

Some of Apache Cassandra's well-known customers are BlackBerry, Bloomberg Engineering, Dream11, and Discord.

#10. OrientDB

OrientDB is a multi-model NoSQL database that supports various data models, such as graphs, documents, key-value pairs, and objects. It offers faster read and write operations and can store up to 120k records per second. Since OrientDB is written entirely in Java, you can run it on any platform without configuration and installation.

Key Features of OrientDB

• APIs and Drivers: The platform supports three types of drivers—native binary remote, HTTP REST/JSON, and Java wrapper. You can leverage them using Python, Java, PHP, Ruby, and more.
• ‍High Concurrency: OrientDB implements Optimistic Concurrency Control (OCC), which allows multiple transactions to read and write data simultaneously. In case of any conflicts, the database automatically retries failed transactions and ensures data consistency.

Best Suited For

Fraud detection, social media analysis, and traffic management.

Notable Users

Squarespace, Cineworld, Riskified, and Contentful are some of the leading users of OrientDB.

#11. CouchDB

CouchDB is a NoSQL database that uses a document-oriented model and allows you to store data in JSON format. Its clustered architecture allows you to run a single logical database server on several servers or virtual machines (VMs). Additionally, you can integrate CouchDB with external tools such as HTTP proxy servers and load balancers.

Key Features of CouchDB

• CouchDB Replication Protocol: This protocol lets you synchronize JSON documents between two peers using the CouchDB HTTP REST API. It supports both push and pull replication, enabling efficient, bidirectional syncs.‍
• MapReduce Views: You can create views over your data using MapReduce functions and use them to aggregate, filter, and transform your data in real-time.‍
• Advanced Security: CouchDB provides robust security features, including hashing passwords, user authentication, and cookie authentication.

Best Suited For

Distributed applications with unreliable network connections and mobile and offline-first apps.

Notable Users

Visa, Fabhotels, Epsilon3, and Pipedrive are some popular organizations using CouchDB.

#12. Firebird

Firebird is a lightweight open-source RDBMS known for its adaptability, supporting both embedded data models and enterprise-scale deployments. You can leverage its SQL conformance and implement various features like stored procedures, triggers, joins, and subqueries on your data.

Key Features of Firebird

• Multi-Generation Architecture: Through this architecture, Firebird ensures that multiple versions of records can co-exist within the database to support concurrency and hybrid OLTP and OLAP applications.
• ‍Logging and Monitoring: Firebird provides Trace API and monitoring tables to help you perform real-time monitoring, SQL debugging, and auditing.

Best Suited For

Enterprise applications, small embedded systems, and multi-user environments requiring low resource consumption and strong concurrency support.

Notable Users

Some leading organizations that use Firebird include British Rail, Broadview Software Ltd, and KIMData.

#13. BigchainDB

BigchainDB is an open-source data solution that offers the scalability of a distributed database along with the security and transparency of blockchain technology. You can easily interact with BigchainDB by using the BigchainDB HTTP API or a wrapper for that API, like the BigchainDB Python Driver.

Key Features of BigchainDB

• Byzantine Fault Tolerant (BFT): This feature ensures that the network operates correctly and reaches consensus in the next block, even if one-third of the nodes behave maliciously.‍
• Digital Asset Management: Usually, blockchains have a dedicated currency. However, BigchainDB doesn’t have a native currency and allows you to create, track, and manage any asset, token, or currency.
• ‍Tamper Resistance: Once your data is recorded on the BigchainDB network, it cannot be altered or deleted, making your data tamper-proof while ensuring integrity and authenticity.

Best Suited For

Decentralized computing platforms and applications requiring secure data transfers and intellectual property protection.

Notable Users

Resonate, Recruit Technologies, Innogy, and BenBen are some well-known organizations leveraging BigchainDB.

#14. ClickHouse

ClickHouse is a columnar database system for implementing sub-second online analytical processing queries. Its column-oriented architecture, efficient query engine, and parallel processing capabilities enable you to process petabytes of data in near real-time. This reduces the time needed to extract valuable insights from data.

Key Features of ClickHouse

• Data Compression: ClickHouse employs several compression algorithms, such as LZ4, Delta, or ZSTD, to reduce storage and improve I/O performance.‍
• SQL Support: The platform supports a powerful SQL dialect and allows you to implement complex data analysis and manipulation tasks using joins, subqueries, and window functions.‍
• Indexing: Instead of conventional B-tree indexes, ClickHouse uses sparse and skip indexes to speed up query execution on large datasets.

Best Suited For

Data warehousing and applications using OLAP processes or extensive data analysis.

Notable Users

Spotify, HubSpot, LangChain, Vimeo, Twilio, and Lyft are some of ClickHouse's renowned customers.

#15. MongoDB

MongoDB is a source-available document-oriented NoSQL database. It allows you to store data in JSON-like formats and manipulate it using the MongoDB query language. You can also interact with the database using languages like C, C++, Go, Java, PHP, Python, Scala, Ruby, and more. Additionally, MongoDB provides data sharding to help you achieve horizontal scalability.

Key Features of MongoDB

• Aggregations: The platform provides a robust query system for data aggregation and transformation. It allows you to execute complex operations like filtering and grouping using the Aggregation Framework.‍
• Embedded Documents: This feature enables the nesting of related data within a single document and eliminates the need for complex and expensive table joins.
• ‍Advanced Security: MongoDB offers role-based access control, TLS/SSL encryption, and SCRAM authentication to protect your data.

Best Suited For

Unstructured data containing text, geospatial, or time-series information, and for applications with rapid iterative development.

Notable Users

Toyota, Indeed, Cisco, and Nokia are some of the popular customers of MongoDB.

Which Open-Source Database System Should You Choose?

Choosing the right open-source database software depends on your specific needs and use case. For high-performance analytics and time-series data, you can choose between ClickHouse and TimescaleDB. If you need a flexible NoSQL solution for handling unstructured data, MongoDB or CouchDB are great options.

For graph databases, you can work with Neo4j or OrientDB. If scalability and high availability are your requirements, then Apache Cassandra or YugabyteDB are ideal choices. You should also consider factors like data structure, platform compatibility, and other advanced features each tool offers before making the decision.

Airbyte: Versatile Data Solution with Extensive Database Support

As an organization, your data infrastructure will likely have multiple data sources. However, to gain comprehensive insights into your organizational data and the underlying operations, you need a reliable way to unify these disparate sources. This is where Airbyte, an AI-enabled data integration platform, can help you.

Airbyte simplifies data management by providing over 400 pre-built connectors to help you build scalable data pipelines and connect various data solutions. It supports a broad range of databases, including relational databases (MySQL, Postgres), cloud-based warehouses (Redshift, BigQuery), lakes (GCS, S3), and vector databases (Chroma, Milvus, Pinecone).

Some key features of Airbyte are listed below:

• Custom Connectors: Airbyte provides you with the flexibility to build connectors from scratch using Connector Builder, low-code Connector Development Kit (CDK), Python CDK, or Java CDK. The AI assistant in Connector Builder automatically reads the API documentation and prefills several fields during configuration to speed up the process.
• ‍GenAI Workflows: You can simplify your GenAI workflows by directly loading your semi-structured and unstructured data into vector databases. To further transform this data, you can integrate Airbyte with popular LLM frameworks (LangChan, LlamaIndex) and implement RAG transformations like automatic chunking, indexing, and embedding.

Airbyte has announced the general availability of its Self-Managed Enterprise Edition, offering support for multi-tenant data mesh or data fabric architectures. To know more about how Airbyte can fit into your data workflows, you can connect with experts or be a part of its active community of 20K users.

Wrapping It Up

Whether you are dealing with structured, unstructured, or time-series data, there are several open-source database solutions to choose from. This article familiarizes you with 15 such options to help you make an informed decision and enhance your data workflows.

By selecting the right open-source database system, you can streamline your organization’s data strategy. It can also save you from overspending and connect you with a supportive community of data experts who can help you when needed. You can leverage open-source tools to optimize your overall data practices and get the most out of your data assets.