Databases serve as the backbone for businesses of all sizes, from agile startups to global enterprises. The right choice of database software can significantly impact business performance, scalability, and operational efficiency.
The demand for databases is skyrocketing, driven by cloud-based solutions, AI-powered features, intelligent query optimization, and real-time data processing. The global database software market, valued at $145.69 billion in 2024, is projected to reach $186.72 billion by 2031, growing at a steady 3.15% CAGR.
With so many options available, how do you choose the right one? To simplify your search, we’ve researched and compiled a list of 45+ top database solutions, covering relational, NoSQL, cloud-based, time-series, graph-based, embedded, and in-memory databases—helping you find the perfect fit for your needs.
- Relational Database
- NoSQL Database
- Cloud Database
- Online Database
- Object-oriented Database
- Graph Database
- Distributed Database
- In-memory Database
- Time-series Database
- Embedded Database
- Show less
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Relational Database
A Relational Database Management System (RDBMS) stores data in structured tables with rows (records) and columns (fields). It uses SQL (Structured Query Language) to manage data and maintain relationships through primary keys (unique identifiers) and foreign keys (links between tables).
They support multiple users and can handle large amounts of data, ensuring data security, accuracy, integrity, and consistency. Relational databases are ideal for structured data and are widely used in banking, e-commerce, and ERP systems.
RDBMSs adhere to ACID properties (Atomicity, Consistency, Isolation, Durability) to ensure reliable transaction processing. Some popular RDBMS are MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server, and SQLite.
The relational database market is expected to grow from USD 79.61 billion in 2025 to USD 229.83 billion by 2034, registering a 12.50% CAGR during the forecast period (2025–2034). Additionally, the market was valued at USD 70.76 billion in 2024.
The table below provides an overview of popular relational databases, highlighting their key features and ideal use cases.
| Database | Description | Key Features | Use Cases |
|---|---|---|---|
| 1. MySQL | An open-source RDBMS known for its scalability, reliability, and performance, widely used in web applications and enterprise solutions. | ACID compliance, Replication & Clustering, JSON support, Cross-platform support, Fast performance, and Built-in security. | Web & mobile apps, CMS, E-commerce platforms, SaaS applications, and Enterprise applications like CRM and ERP, Financial & banking systems. |
| 2. PostgreSQL | A powerful, open-source, object-relational database system known for its advanced features, extensibility, and strong ACID compliance. | ACID compliance, advanced indexing, Full-text search, Concurrency control, Parallel query execution, Multi-version concurrency control (MVCC), Partitioning & sharding. | E-commerce, ERP systems, Geospatial & GIS apps, Financial services, FinTech, Data warehousing, Web & mobile apps, Multi-tenant SaaS apps. |
| 3. Oracle Database | A multi-model, enterprise-grade database offering high availability, security, and advanced real-time analytics. | Automatic Storage Management (ASM), Real application clusters, Masking capabilities, Built-in AI vector search, SQL firewall, Retrieval augmented generation (RAG). | Enterprise data management, Banking systems, Government & Public sector, Cloud & hybrid cloud deployments, Data warehousing, Financial services, Telecom. |
| 4. Microsoft SQL Server | A flexible, enterprise-grade RDBMS that offers high performance, security, and advanced integration capabilities. | Advanced security, BI tools, In-memory processing, Intelligent Query Performance (IQP), AI & ML integration, and Strong backup capabilities. | Business intelligence & analytics, Enterprise apps, IoT & Edge computing, Cloud & hybrid database solutions |
| 5. SQLite | A self-contained, lightweight, serverless, zero-configuration, transactional SQL database engine. | Serverless architecture, Single-file storage, Zero-configuration, and Cross-platform compatibility. | Mobile apps, IoT devices, and Lightweight desktop apps. |
NoSQL Database
A NoSQL database is a non-relational database designed to handle large amounts of data with flexible structures. It supports different data models, including key-value, document, column family, and graph.
NoSQL databases are ideal for managing unstructured and semi-structured data, making them ideal for big data, real-time analytics, and modern applications. They offer horizontal scaling, high availability, and schema flexibility, allowing developers to adapt quickly to changing needs.
Types of NoSQL Databases:
- Key-Value Stores: (e.g., DynamoDB) – Simple storage using key-value pairs, great for caching and real-time applications.
- Document Stores: (e.g., MongoDB) – Stores data in JSON-like documents, ideal for content management and web applications.
- Column-Family Stores: (e.g., Apache Cassandra) – Organizes data into columns optimized for big data and analytics.
- Graph Databases: (e.g., Neo4j) – Designed for handling complex relationships, useful in social networks and recommendation engines.
NoSQL data architectures are distributed by design and have no single point of failure. They also provide easy replication, making them more resistant to unplanned outages and disruptions.
The table below provides an overview of popular NoSQL databases, showcasing their standout features and best-fit use cases.
| Database | Description | Key Features | Use Cases |
|---|---|---|---|
| 6. MongoDB | A document-oriented multi-cloud database known for its flexible schema, scalability, and powerful query language, making it ideal for applications requiring dynamic data storage. | Full-text search, JSON-based documents, Document-oriented storage, Automated data tiering, Workload isolation, End-to-end encryption | Content management, IoT applications, E-commerce & retail, and Real-time applications, Gaming apps, Multi-cloud & distributed environments |
| 7. Cassandra | A highly scalable, distributed NoSQL database designed for high availability and fault tolerance, making it ideal for big data and real-time analytics. | Distributed architecture, Linear scalability, Fault tolerance, High write throughput, and Multi-datacenter replication. | Recommendation engines, Real-time big data applications, IoT & sensor data processing |
| 8. Couchbase | A distributed NoSQL database that combines document and key-value data models, offering high-speed caching, full-text search, and mobile synchronization. | Multi-model architecture, In-memory caching, Auto-scaling, N1QL query language, Role-based access control, Full-text search. | Customer experience platforms, Mobile apps, Real-time applications, Content management and Cloud-native apps |
| 9. Amazon DynamoDB | A serverless, NoSQL, fully managed database service providing scalability, and automatic multi-region replication for enterprise applications. | Serverless architecture, Flexible data models, Auto-scaling, Built-in security, Global replication, Single-digit millisecond performance. | Gaming & IoT apps, Media & Entertainment, Retail and E-commerce, Financial services |
Cloud Database
A cloud database is a scalable, managed database hosted in the cloud, eliminating the need for on-premise hardware. It offers high availability, security, and automated backups. It organizes and stores structured, unstructured, and semi-structured data.
Cloud databases can be offered as a managed database-as-a-service (DBaaS) or deployed on a cloud-based virtual machine (VM) and self-managed by an in-house IT team. You can launch or shut down a cloud database in minutes.
They also scale automatically, ensuring smooth performance even as your workload increases. With pay-as-you-go pricing, you only pay for what you use and can scale up or down based on demand.
Companies use cloud databases for real-time analytics, app development, and global access, reducing maintenance costs while ensuring reliability.
Types of Cloud Databases
Cloud databases fall into two main categories: relational and non-relational databases.
Relational Cloud Databases
Relational databases store data in structured tables with rows and columns, making it easy to organize and understand relationships between different pieces of information. They follow a fixed schema and use SQL to query and manage data. Examples: SQL Server, Oracle, MySQL, PostgreSQL, Spanner, and Cloud SQL.
Non-Relational Cloud Databases
Non-relational databases, or NoSQL databases, store unstructured data like emails, chat messages, documents, media files, and sensor data. Unlike relational databases, they don’t require a strict schema, allowing you to store and manage data more flexibly, regardless of format. Examples: MongoDB, Redis, Cassandra, HBase, and Bigtable.
The table below overviews popular cloud databases, showcasing their key features and ideal use cases.
| Database | Description | Key Features | Use Cases |
|---|---|---|---|
| 10. Amazon RDS | A managed relational database supports multiple engines like MySQL, PostgreSQL, and SQL Server. | Automated backups, scaling, Multi-availability zone deployment, Read replicas, Performance monitoring, and Security controls. | Enterprise databases, Analytics & business intelligence, Multi-tenant SaaS apps, HIPAA and PCI-DSS compliant apps, Web & mobile apps |
| 11. Google Cloud SQL | A fully managed relational database service for MySQL, PostgreSQL, and SQL Server, optimized for Google Cloud workloads. | Automated maintenance, IAM-based security, Gemini, Multi-Database Support, Integration with Google Cloud Services, Firebase Data Connect | Cloud-native apps, Enterprise data management, CMS & Content management, SaaS apps, Web & Mobile apps, Database modernization, Real-time data processing |
| 12. Microsoft Azure SQL Database | A fully managed cloud database service with AI-powered performance optimization and security features. | Intelligent query processing, AI monitoring & Search, Geo-redundancy, Microsoft Copilot, Hyperscale, and Advanced security. | Business intelligence, SaaS & Web apps, AI-driven apps, IoT & Edge computing, Cloud-native apps, E-commerce & Payment processing |
| 13. Snowflake | A cloud-based data platform that provides scalable storage, processing, and analytic solutions. | Cortex AI, Near-zero maintenance, Multi-cloud support, AI Data cloud, Advanced data protection, Role-based access control | Advertising, Media and Entertainment, Financial services, Manufacturing, Public sector, Retail, Telecom, Travel & hospitality. |
| 14. MongoDB Atlas | A fully managed NoSQL database service providing a flexible schema and horizontal scaling for modern apps. | Automatic scaling, Multi-cloud support, Automated data tiering, Continuous backups, Global data distribution, Full-text search, Real-time analytics, Workload optimization | Serverless application development, AI & ML apps, Healthcare, Gaming, API-driven payment solutions |
Online Database
An online database is a cloud-based system that allows users to store, manage, and access data over the Internet. They offer remote access, scalability, automated backups, and real-time collaboration.
They are commonly used in businesses, e-commerce, SaaS applications, and data-driven projects. Google Firebase, Amazon RDS, Microsoft Azure SQL Database, and Airtable are popular options.
The table below overviews popular online databases, highlighting their key features and ideal use cases.
| Database | Description | Key Features | Use Cases |
|---|---|---|---|
| 15. Knack | A no-code database platform that allows businesses to create custom web apps with structured data management. | Customizable databases, Workflow automation, API integration, Role-based permissions, and Cloud storage. | Client portals, Inventory & asset management, SaaS apps, Learning management, Event & registration management. |
| 16. Google Sheets | A cloud-based spreadsheet application that provides real-time collaboration, data analysis, and automation capabilities. | Real-time collaboration, Built-in formulas & functions, Import/export capabilities, Version history, Add-ons & API support. | Business & project management, Data analytics & reporting, Financial planning, and Academic research. |
| 17. Airtable | Airtable combines the simplicity of spreadsheets with the power of relational databases, offering flexible data organization and workflow automation. | Customizable views Admin controls, Two-way sync, Airtable AI, automation & API support, AI integrations, Security and compliance. | Project portfolio management, Marketing campaign management, Content creation, CRM & sales pipeline management, Inventory & asset management. |
| 18. Zoho Creator | A low-code application development platform that enables users to build custom apps with database-driven logic and workflows. | AI-powered insights, Multi-platform accessibility, AI-assisted migration tools, cross-functional analytics, and 600+ apps ready to integrate. | Field service & Inventory management, CRM, Customer portals, Property & IT asset management, ERP. |
| 19. Quickbase | A cloud-based database platform that helps organizations build scalable, customized applications for workflow automation. | Custom workflows, role-based access, Workflow Automation, real-time reporting, and Integrations with third-party tools. | Vendor & logistics management, Supply chain & inventory management, CRM & sales management, Finance & budget management |
Object-oriented Database
An Object-Oriented Database (OODB) stores and manages data as objects, following object-oriented programming principles. By storing data as objects, OODBs can retrieve complex data structures more efficiently, reducing the need for multiple table joins common in relational databases.
OODBs support key object-oriented features like encapsulation, inheritance, and polymorphism, eliminating the need for object-relational mapping (ORM) and reducing overhead. They offer better data modeling and performance for specific use cases but are less common due to their complexity and lack of standardization.
These databases are used in applications requiring complex data models, such as CAD systems, real-time simulations, multimedia applications, and AI-driven solutions. Some popular OODBs are db4o, ObjectDB, and Action NoSQL.
The table below overviews popular OODBs, highlighting their key features and ideal use cases.
| Database | Description | Key Features | Use Cases |
|---|---|---|---|
| 20. db4o | An open-source object database designed for embedded and mobile apps, supporting Java and .NET. | API Support ACID transactions, Zero-schema evolution, Small footprint, Fast performance. | Embedded database for mobile and IoT devices, Gaming and simulation data storage, Embedded databases in consumer electronics |
| 21. ObjectDB | A high-performance, object-oriented database for Java applications designed for JPA and JDO integration. | Schema-free storage, ACID Transactions Multi-user support, Full compliance with Java Persistence API (JPA) and Java Data Objects (JDO). | High-performance Java applications, Embedded systems, Data-intensive Java projects, Gaming & Simulation engines. |
| 22. Actian NoSQL | A scalable object database tailored for handling complex data structures in high-performance applications. | Built-in fault tolerance, Native Object Storage, handling extensive data volumes, Object-oriented data management | Network route optimization, Metadata management, Content management, simulation, and fraud detection. |
Graph Database
A graph database is a NoSQL database built to handle highly connected data. It is based on graph theory and organizes data as nodes (entities) and edges (relationships), making it more flexible and intuitive.
Graph databases use nodes to represent entities, edges to define relationships between them and properties to store additional details. Nodes function like rows in relational databases and can be categorized with labels. Edges establish structured connections with a defined start, end, type, and direction. Properties add contextual data, making graph databases highly efficient for modeling complex relationships.
Graph databases are ideal for applications that rely on complex relationships, such as social networks, fraud detection, recommendation engines, and knowledge graphs. They deliver faster queries by directly traversing connections rather than performing expensive table joins.
Some popular graph databases are Neo4j, ArangoDB, TigerGraph, and Amazon Neptune.
The table below overviews popular graph databases, highlighting their key features and ideal use cases.
| Database | Description | Key Features | Use Case |
|---|---|---|---|
| 23. Neo4j | Neo4j is a popular graph database known for its native graph storage and processing, making it efficient for highly connected data. | Native graph storage and processing, Cypher query language, ACID compliance, High scalability, 1000x faster than relational DBs | Network & IT operations, Recommendation systems, knowledge graphs, Identity & access management, Supply chain management, Retail, telecom. |
| 24. Amazon Neptune | A fully managed graph database service by AWS, designed for applications with highly connected datasets, offers high performance and scalability. | Automatic backups and replication, Integration with AWS ecosystem, Handles more than 100k queries per second | Enterprise knowledge graphs, identity management, fraud detection, IT security, Recommendation engines, ML Models |
| 25. JanusGraph | JanusGraph is a highly scalable, open-source graph database designed for big data applications, supporting a variety of backends. | Distributed and scalable architecture, Indexing via Elasticsearch and Solr, Gremlin query language, Strong integration with big data tools | Large-scale analytics, knowledge graphs, social network analysis, Network and IT monitoring, Healthcare & Bioinformatics. |
| 26. TigerGraph | TigerGraph is a high-performance graph database optimized for real-time deep link analytics with fast query processing. | High-speed parallel processing, GSQL query language, ACID compliance, Built-in machine learning support, AI-Powered Insights, Enterprise-grade security | Real-time fraud detection, Recommendation engines, financial analytics, Supply chain analysis, Cybersecurity & threat detection, Healthcare & genomics. |
| 27. ArangoDB | ArangoDB is a multi-model database that integrates graph, document, and key-value data models within a single core. | Multi-model support, Sharding and replication for scalability, Built-in full-text search and analytics, Flexible schema design | IoT apps, Supply chain optimization, Telecom, Network management, Cyber threat management, Aerospace & Defense |
Distributed Database
A distributed database is a collection of interconnected databases spread across multiple physical locations, managed by a centralized or decentralized system. It enhances scalability, availability, and fault tolerance by replicating or partitioning data across different nodes.
Distributed databases can follow horizontal or vertical scaling strategies and are commonly used in cloud computing, big data processing, and high-availability applications.
Types of Distributed Database
- Homogeneous Database: A homogeneous database has the same structure, operating system, and database management system across all sites. This consistency makes it easy to manage.
- Heterogeneous Database: In a heterogeneous distributed database, different machines store different data, run on various operating systems, use distinct data schemas, and need software to enable communication. Some machines may not even be aware of others in the system.
Depending on requirements, these databases support ACID or BASE consistency models, enabling faster query performance, load balancing, and disaster recovery. Some popular examples are Google Cloud Spanner, Amazon Aurora, Apache Cassandra, and CockroachDB.
The table below overviews popular distributed databases, highlighting their key features and ideal use cases.
| Database | Description | Key Features | Use Case |
|---|---|---|---|
| 28. Apache Cassandra | A highly scalable, distributed NoSQL database designed for handling large amounts of data with high availability. | Distributed architecture, Flexible data model, Linear scalability, Fault tolerance, Automatic data replication, Tunable consistency | Big data apps, IoT & sensor data storage, Social media analytics, CMS, Healthcare & Genomics, E-commerce & retail |
| 29. CockroachDB | A distributed SQL database designed for high availability and horizontal scalability that ensures strong consistency and resilience against failures. | ACID-compliant, Multi-region deployment, Automatic failover and recovery, PostgreSQL Compatibility, multi-active architecture | Banking & fintech, Retail & ecommerce, Media & streaming, Gaming, Healthcare, Manufacturing & logistics |
| 30 YugabyteDB | A PostgreSQL-compatible multi-master distributed SQL database optimized for high availability and horizontal scalability. | Distributed ACID transactions, PostgreSQL wire compatibility, Multi-cloud and hybrid deployment, Automatic sharding | Suitable for cloud-native apps, Large-scale transactional workloads, Streaming apps |
| 31. Amazon Aurora | A managed relational database service by AWS that delivers high performance and availability and fully compatible with MySQL and PostgreSQL | Auto-scaling, Serverless Configuration, Global Database, ML-based predictions, Multi-AZ deployment with up to 99.99% availability | SaaS apps, Big data & analytics, Gaming databases, IoT apps, Financial services & Payment processing, ML & AI Apps |
| 32. Google Cloud Spanner | A fully managed, scalable, globally distributed relational database service that offers strong consistency and high availability. | PostgreSQL interface, Vector search, Spanner graph, Automated maintenance ACID transactions, Multi-region replication, 99.999% Uptime SLA | Financial services, retail, and e-commerce, Gaming, IoT & Telemetry data, SaaS Apps & payments solutions |
In-memory Database
An in-memory database (IMDB) stores data in a computer’s main memory (RAM) instead of a hard drive, allowing extremely fast read and write operations. It’s perfect for low latency and high-speed applications, like real-time analytics, caching, and financial transactions.
Since IMDBs bypass disk storage, they’re much faster than traditional databases. However, they are typically volatile, meaning data is lost if the system crashes—unless features like snapshots or replication are used for persistence.
In-memory databases are perfect for applications that need microsecond response times or handle high traffic spikes, such as gaming leaderboards, session stores, and real-time analytics.
These databases are best for handling high-speed data access where some data loss is acceptable. For example, an e-commerce site should store product details in permanent storage, but shopping cart data can be kept in an in-memory database for faster access.
Some popular in-memory databases are Redis, Memcached, and Apache Ignite, which are commonly used in big data processing, gaming, and AI applications.
The table below overviews popular In-memory databases, showcasing their key features and ideal use cases.
| Database | Description | Key Features | Use Case |
|---|---|---|---|
| 33. Redis | Redis is an in-memory, high-performance data store that supports caching, real-time analytics, and message brokering. | In-memory key-value store, High availability with replication, Pub/sub messaging support, Lua scripting and clustering, Pub/Sub messaging | Real-time analytics, caching, message queuing, E-commerce, leaderboards, and session storage. |
| 34. Memcached | Memcached is a high-performance, distributed memory caching system designed to speed up dynamic web apps. | Simple key-value storage and in-memory caching for fast retrieval, Multi-threaded architecture, Scalable across multiple nodes | Web caching, session storage, Gaming apps, API response caching, Content Delivery Networks (CDNs) |
| 35. Oracle TimesTen | Oracle TimesTen is an in-memory relational database providing low-latency and high-performance transactions. | ACID-compliant in-memory database, Real-time data processing, SQL and PL/SQL support, Automatic monitoring | Financial Services & Trading Systems, Real-time analytics, Telecom billing, Travel and Logistics, Gaming, Manufacturing & Industrial automation |
| 36. VoltDB | VoltDB is a high-speed, in-memory NewSQL database optimized for transactional and analytical workloads. | ACID-compliant database, Horizontal scalability, Low Latency Processing, Built-in stream processing, Supports SQL and procedural logic | IoT apps, Fraud detection, Financial transactions, Streaming Data Processing, and Low-latency event processing. |
Time-series Database
A Time-Series Database (TSDB) is built specifically for storing and analyzing time-stamped data records where each entry is associated with a specific point in time. It’s designed to handle fast data writes and time-based queries, making it perfect for things like application performance monitoring, network data, sensor data, events, clicks, IoT data, financial analysis, and real-time analytics.
TSDBs help you handle large amounts of time-stamped data efficiently. You can quickly store data, compress it to save space, and run queries over specific time periods to track trends, spot anomalies, and even predict future patterns.
TSDBs store time data as timestamps with second or millisecond precision. They support different date formats, time zones, and calendar adjustments. You can also use them to get time-based aggregations and statistics, making it easier to analyze patterns over time.
With built-in compression algorithms, TSDBs ensure fast query responses—even for large datasets. Some of the most popular time-series databases include InfluxDB, TimescaleDB, Prometheus, OpenTSDB, and Graphite—each offering different strengths in performance and scalability.
The table below overviews popular TSDBs, showcasing their key features and ideal use cases.
| Database | Description | Key Features | Use Case |
|---|---|---|---|
| 37. InfluxDB | A high-performance open-source time-series database designed for real-time monitoring, analytics, and IoT applications. | High write and query performance, Downsampling, Real-time querying with SQL, Bulk data eviction, Low-cost object storage | DevOps monitoring, Predictive maintenance, Financial market analysis, IoT Data collection, Real-time analytics |
| 38. TimescaleDB | A PostgreSQL-based, open-source time-series database designed for high-performance and complex queries on time-series data. | Advanced compression, Flexible data modeling, 100 ready-to-go hyperfunctions, Automated data aggregation | IoT & sensor Data, Financial analytics, DevOps monitoring, Fleet & logistics tracking, Industrial IoT |
| 39. Prometheus | An open-source monitoring system designed for real-time metrics collection, alerting, and visualization. | Powerful multi-dimensional data model, Built-in Alerting, and Powerful Query Language (PromQL) | Cloud & Kubernetes monitoring, IoT & edge computing, Application performance monitoring (APM), Infrastructure & server monitoring |
| 40. Amazon Timestream | A fully managed time-series database by AWS, optimized for high-speed ingestion and querying of time-stamped data. | Serverless and auto-scaling, integrates with AWS services, optimized for fast time-series queries, automatic scaling, and built-in security. | Real-time analytics, IoT sensor data monitoring, Financial and business analytics, operational insights & forecasting. |
| 41. ClickHouse | A fast open-source columnar database designed for real-time analytics, offering high performance on large datasets. | Columnar storage, vectorized query execution, Compression & Optimization, real-time data ingestion | Business intelligence (BI), Financial & risk analysis, Ad Tech & marketing analytics, Monitoring & observability |
Embedded Database
An embedded database is a small, self-contained database that runs inside an application instead of a separate server. It provides fast, local data storage with minimal setup, making it ideal for mobile apps, IoT devices, desktop software, and real-time applications.
Popular embedded databases like SQLite, LevelDB, RocksDB, and LMDB are known for their high performance, low latency, and efficient memory use. They are widely used in browsers, messaging apps, and embedded systems where fast, seamless data access is crucial.
While they excel in speed and simplicity, they often lack features like multi-user access and advanced queries. Still, their small size and efficiency make them the top choice for applications that need reliable local data storage.
| Database | Description | Key Features | Use Case |
|---|---|---|---|
| 42. SQLite | A lightweight, self-contained SQL database engine widely used for embedded systems and mobile applications. | Zero-configuration, ACID compliance, SQL support, cross-platform compatibility, Atomic commit & rollback | Browsers & Web Applications, Game development, Mobile apps, Embedded systems |
| 43. Berkeley DB | A high-performance, embedded key-value store with support for multiple APIs and ACID transactions. | Embedded storage, High concurrency, Concurrent data access, Embedded systems, Multiple APIs | Mobile apps High-throughput messaging, Embedded systems, Data caching |
| 44. LevelDB | An open-source, high-performance key-value storage library by Google, designed for fast retrieval and storage of data. | Atomic batch operations, Log-Structured Merge-Tree Architecture, Cross-platform compatibility, Ordered key-value storage | Great for applications with heavy read/write loads, such as caching and indexing, Embedded storage engine, Metadata storage |
| 45. RocksDB | A high-speed, persistent key-value store designed for SSD storage and high-throughput workloads. | Optimized for SSDs, Multi-threaded Compaction & Background jobs, Log-structured merge (LSM) tree | Big data applications, IoT & Edge computing, Streaming & messaging systems |
| 46. H2 | A Java-based, lightweight, open-source relational database with in-memory capabilities and strong SQL compliance. | Supports JDBC, Fine-grained Tuning, in-memory mode, multi-version concurrency, Encrypted databases, Fulltext search | Perfect for Java applications needing an in-memory, Embedded database, Microservices & Small-scale applications |