21 Best NoSQL Databases Reviewed for 2025

IBM Cloud is a fully managed, distributed database that runs on a serverless schematic, meaning you don’t have to manage server configuration and scaling. This lets you build and run databases without worrying about the backend.

Why I Picked IBM Cloudant: I picked IBM Cloudant because it’s incredibly secure. You only pay for the resources you use, and you can scale up or down as your data needs change. For security, I was impressed by IBM Key Protect, which gave me total control and visibility over encrypted keys.

IBM Cloudant Standout Features and Integrations:

Features include its multi-region replication, high availability, and automatic failover for globally distributed applications. It also offers integrated full-text search powered by Apache Lucene, making it easier to query and analyze large datasets efficiently. Additionally, Cloudant provides offline-first synchronization, allowing mobile and web applications to function seamlessly even with intermittent connectivity.

Integrations include IBM App Connect, IBM Cloud Log Analysis,  IBM Watson, Apache Spark, IBM Cloud Functions, Apache CouchDB, Tableau, and IBM Cognos Analytics.

RavenDB is a multi-document database that supports ACID (Atomic, Consistency, Isolation, and Durability) transactions. This helps prevent data from accidentally falling into an inconsistent state. The platform is used by nationally recognized companies such as Toyota, Verizon, and Medicaid.

Why I Picked RavenDB: Aside from the visually appealing interface, I like that RavenDB supports ACID transactions. This ensures transactions can only change data in predictable ways and that those changes are always saved, even during a system crash.

RavenDB Standout Features and Integrations:

Features include built-in full-text search, allowing fast and efficient querying of unstructured data without needing an external search engine. It also offers automatic indexing, reducing the need for manual query optimization and improving performance over time. Additionally, RavenDB supports multi-master replication, ensuring high availability and real-time synchronization across distributed environments.

Integrations include FastReport, Elasticsearch, Grafana, Power BI, Node.js, .NET, Python, and Kubernetes.

Apache Cassandra is an open-source, wide-column database supporting hybrid cloud and on-premises replicating and audit logging. Because the platform is freely available and used by thousands of companies, I think it is an excellent solution for businesses that want to manage extensive active data sets at low cost.

Why I Picked Apache Cassandra: Cassandra stands out as a powerful wide-column database with lots of scalability. You can add more servers horizontally to the cluster as your data needs increase. Also, Cassandra uses a column-family data model, so it’s accessible to traditional relational database users.

Cassandra Standout Features and Integrations:

Features include a masterless architecture, ensuring no single point of failure and enabling continuous availability even during node failures. It also offers lightweight transactions (LWT) using Paxos, providing strong consistency when needed without sacrificing performance. Additionally, Cassandra handles write-heavy workloads, making it an ideal choice for applications requiring high-speed data ingestion at scale.

Integrations include New Relic, IRI Voracity, DbVisualizer, Sematext Cloud, Flex83, Retool, and DbSchema.

MongoDB is an open-source, document-based database. It can store structured data in the popular JavaScript Object Notation (JSON) format or in a proprietary Binary JSON (BSON) format. BSON can store more data types than JSON and encode the type and length of a piece of information, making it easier for a machine to parse (convert formatted text into a specific data structure).

Why I Picked MongoDB: I chose MongoDB because it excels at what document databases do best: providing flexible data models that can evolve as your application needs to grow. Since MongoDB scales horizontally, you can quickly add new servers to accommodate more significant amounts of data clusters, and it works well for applications that require high performance.

MongoDB Standout Features and Integrations:

Features include a flexible document-based data model, allowing developers to store and query data in a way that mirrors real-world objects. It also offers native support for sharding, enabling seamless horizontal scaling across distributed environments. Additionally, MongoDB includes an aggregation framework that simplifies complex data transformations and analytics without requiring external processing tools.

Integrations include Netlify, SAML SSO Providers, HashiCorp Terraform, and HashiCorp Fault. APIs are also available.

Couchbase Capella is a cloud database platform, combining the speed and flexibility of a NoSQL database with the benefits of an SQL database. It also has an app development solution called Capella App Services, which you can use to design and deploy IoT, mobile, and edge applications.

Why I Picked Couchbase Capella: I chose Couchbase Capella because it is accessible to those familiar with SQL databases. It uses the SQL++ query language for operations and analytics. I like how this streamlines the move from traditional relational databases to non-relational databases, with familiar features like ACID transactions and hierarchical schemas.

Couchbase Capella Standout Features and Integrations:

Features include its built-in memory-first architecture, ensuring low-latency performance for high-speed applications. It also offers advanced mobile synchronization with Couchbase Lite and Sync Gateway, making it an excellent choice for offline-first applications. Additionally, Capella supports multi-model capabilities, allowing users to work with key-value, document, and graph data within a single database system.

Integrations include Apache Kafka, Kubernetes, Confluent Cloud, Workato, and Microsoft Azure.

Neo4j is a graph-based NoSQL database used to develop applications, create and deploy Artificial Intelligence (AI) and Machine Learning (ML) pipelines, and perform detailed analytics. It is particularly suited to data scientists, application developers, and similar enterprises. With the intuitive graph-based user interface, establishing patterns and relationships with large amounts of data is easy.

Why I Picked Neo4j: Neo4j grabbed my interest because of its clear user interface and advanced graphing capabilities. I found it was helpful for uncovering hidden insights in complex data relationships, where trends and patterns may not be obvious right away. I was also impressed by how easily the platform scales horizontally. This is thanks to Autonomous Clustering, which automatically copies your datasets to the most optimal servers based on your rules and guidelines.

Neo4j Standout Features and Integrations:

Features include its native graph database architecture, which enables highly efficient relationship queries that outperform traditional relational databases for connected data. It also features Cypher, a powerful graph query language designed for intuitive and complex relationship-based searches. Additionally, Neo4j offers advanced visualization tools that help users explore data connections and uncover insights without needing extensive SQL expertise.

Integrations include Apache Spark, Kafka Connect, and the Neo4j Data Warehouse connector.

Redis is an open-source, in-memory, key-value database. It delivers sub-millisecond response times, which makes it ideal for all kinds of real-time applications, such as updating the leaderboard in an online multiplayer video game or providing real-time shipping information to a customer awaiting a delivery.

Why I Picked Redis: I was impressed by Redis’ fast performance. It stores data in memory instead of on a physical disk or Solid-State Drive (SSD), so when you request data, it is unnecessary to pass through the disk, making it more efficient than many competitors.

Redis Standout Features and Integrations

Features include its in-memory data storage, which enables ultra-low latency and high-speed data retrieval, making it ideal for real-time applications. It also supports data persistence through snapshotting and append-only file (AOF) modes, allowing users to balance speed with durability. Additionally, Redis offers built-in support for pub/sub messaging, making it a strong choice for event-driven architectures and real-time notifications.

Integrations include RediSearch, RedisJSON, RedisGraph, RedisBloom, redis-cell, RedisTimeSeries, and RedisAI.

Oracle Coherence is an in-memory, key-value database that delivers strong scalability and performance for enterprise applications. Using low-latency data storage to read, write, and query latencies allows businesses — running multiple applications in different languages — to maintain data consistency in real-time quickly.

Why I Picked Oracle Coherence: What I love about Oracle Coherence is its support for asynchronous event streaming. This means you can incorporate event models into your event-driven architecture, allowing for efficient communication between microservices like servers and clients.

Oracle Coherence Standout Features and Integrations:

Features include real-time data grid capabilities, enabling ultra-fast in-memory data processing for high-performance applications. It also offers dynamic scaling, allowing clusters to expand and contract automatically based on workload demands. Additionally, Coherence supports federated caching, which ensures data consistency across multiple geographic regions by synchronizing caches in real-time.

Integrations include Spring, Oracle WebLogic Server, and Oracle NoSQL database.

HBase is a column-oriented, NoSQL database on the Hadoop Distributed File System (HDFS). The HBase platform is particularly good at managing extensive data, as the system is almost entirely fault-tolerant.

Why I Picked HBase: HBase stood out because it is extremely good with large datasets. It can scale across thousands of servers and accommodate data up to terabytes. I also appreciated that HBases uses HDFS to detect faults across all servers and automatically recover quickly. It excelled at minimizing server downtime for large systems.

HBase Standout Features and Integrations:

Features include deep integration with Hadoop Distributed File System (HDFS), enabling efficient storage and processing of massive datasets across distributed clusters. It features automatic sharding, evenly distributing data and allowing for seamless horizontal scaling without manual intervention. Additionally, HBase supports real-time read and write access with strong consistency, making it ideal for applications that require low-latency performance on large-scale data.

Integrations include Hive, Apache Hadoop, Apache Spark, Apache Kafka, Apache Hive, Zookeeper, Flink, and Grafana.

OrientDB is an open-source, multi-model database. It supports all types of NoSQL databases, including graph, key-value, object, and document models. So your business can build and manage multiple NoSQL databases on one system without investing in various products.

Why I Picked OrientDB: I chose OrientDB because its engine is built from the ground up to support the full functionality of each NoSQL database type natively. I found this to be a better approach than merely replicating the interfaces of other models, which achieves limited speed and scalability.

OrientDB Standout Features and Integrations:

Features include its multi-model architecture, which natively supports graph, document, object, and key/value data models within a single database engine. It also offers ACID compliance, ensuring strong consistency and reliability across transactions, which is uncommon among many NoSQL databases. Additionally, OrientDB features a distributed architecture with sharding and replication, enabling seamless horizontal scaling and high availability.

Integrations include Teleporter, Orient DBL, Spark Connector, and Neo4j Importer.

Elasticsearch is a distributed database that has a uniquely fast search function. Based on the Apache Lucene library, you can use Elasticsearch across various industries to improve the customer experience, streamline the DevOps lifecycle, and gain insights into production environments.

Why I Picked Elasticsearch: I chose Elasticsearch because it’s a powerhouse for searching data. Aside from storing and analyzing large amounts of data in real time, Elasticsearch can provide answers in milliseconds. It achieves this speed by converting unstructured data into specially configured documents optimized for language-based searches. This allows users to find the information they need quickly and easily.

Elasticsearch Standout Features and Integrations:

Features include its distributed search architecture, which enables high-speed querying and indexing across massive datasets. It also offers built-in support for full-text search with advanced features like fuzzy matching, autocomplete, and relevancy scoring to refine results. Additionally, Elasticsearch provides real-time analytics and visualization through its integration with Kibana, making it a suitable tool for monitoring, log analysis, and business intelligence.

Integrations include 1Password, Amazon CloudWatch, AWS ECS, Graphite, HA-Proxy, and LastPass.

Amazon DynamoDB is a NoSQL database that supports both document and key-value data models. It is a cloud-based service that lets you store items, tables, and attributes. You can use the service on pay-as-you-go or provisioned pricing model, whichever suits your workload.

Why I Picked Amazon DynamoDB: Aside from being a fully managed NoSQL database with high availability and durability, what I like the most about Amazon DynamoDB is its Amazon Web Services (AWS) integration. This helps you do more with your data, for example, by letting you import and export from your S3 buckets. I also appreciated the ease of use, thanks to excellent documentation and a user interface that is clean and intuitive.

Amazon DynamoDB Standout Features and Integrations:

Features include on-demand and provisioned capacity modes, allowing users to optimize costs and performance based on workload requirements. It also supports global tables, enabling multi-region replication with automatic synchronization for globally distributed applications. Additionally, DynamoDB Streams provides real-time event capture, making it easy to trigger AWS Lambda functions and integrate with event-driven architectures.

Integrations include AWS Lambda, Amazon S3, Amazon Kinesis, AWS Glue, Apache Spark, Kubernetes, Grafana, and Elasticsearch.