10 Best Real-Time Databases for 2025

In a world full of ever-increasing data, there has always been a huge requirement for proper storage and access to data. As the number of dynamic applications and websites on the Internet increased, databases became highly crucial. Today, one needs a database to run an application or website. With advancing technology and growing users, the need for a database is crucial for any developer. But with more and more databases being developed, many people need to know what database to use for building effective applications and websites. In this article, we will discover the 10 best real-time databases that are widely used.

👁 Best Real Time Databases

What is a Real-Time Database?

A real-time database is a database that stores data in JSON files which is synchronized in real-time to every client that is connected to the database. In simple terms, the data keeps changing frequently and gets updated regularly. A real-time database and an application go hand-in-hand like bread and butter which allows websites and applications easier to manage.

Traditional database requires clients to actively query for updated data whereas a real-time database automatically provides changes in data to the connected client.

Why Real-Time Database?

Real-time databases have the necessary scale and speed to handle sophisticated queries over huge data. It reduces the lag time which is used a lot in video conferencing, gaming, and geolocation services. Sure, we could use traditional databases but Real-time databases are more efficient and easier to use as the data is well-segregated and updated. A few other advantages of using a real-time database are:

1. It is well-suited for applications requiring event-driven architectures.
2. It provides low latency and high concurrency connections
3. It ships with web and mobile SDKs and eliminates the need for servers
4. It gives limited scalability

10 Best Real-Time Databases for 2025

The number of databases available out there is overwhelming, and the need for better databases grows with time. You might get a little confused about what database you are in and should use, it’s only natural. Here’s a list of best real-time databases that you might be interested in, they are suggested by many users and considered to be the best.

1. MongoDB

MongoDB is known for its top-notch features and it is recommended by many users. In MongoDB, the data is stored in the form of collections and not in a tabular format as it is stored in Relational databases. It can handle large volumes of data and has flexible data storage. The data can naturally be stored in JSON format.

Key Features

1. Replication: This is an important feature as it can help in backing up data in another server by creating multiple copies of it that can be accessed if one server fails.
2. Indexing: The data is indexed which makes locating data from a database easier.
3. High Performance: MongoDB exhibits high performance compared to other databases due to its incredible features like replication, indexing, etc.
4. Document-oriented: Data is stored in a document in key-value pairs which gives a unique ID to each object in the database.

Where to Use: MongoDB is great for flexible and scalable applications, especially when dealing with large volumes of unstructured or semi-structured data. It’s ideal for content management systems, real-time analytics, and applications that require quick iterations.

Best Use Cases:

• Large-scale applications needing high performance and horizontal scalability.
• Projects with rapidly evolving schemas.
• Social networks, mobile applications, and real-time data storage (like logs).

2. Apache Cassandra

It is an open-source real-time database that provides a complete zero point of failure. Apache Cassandra uses Cassandra Query Language (CQL) as an alternative to SQL to access, store, update, delete, and manage the data stored in Collections.

Key Features

1. Replication - The data copies are taken and stored in multiple data centers and it can deploy a large number of nodes across multiple data centers.
2. Apache Cassandra is highly Scalable.
3. Query Language: Cassandra Query Language (CQL) is used instead of SQL to access Cassandra.
4. Fault Tolerance: Multiple copies of data are taken and stored to find the Faulty nodes and replace them quickly with ease.

Where to Use: Best for applications that require high availability, fault tolerance, and massive scalability. It excels in environments where data is distributed across multiple data centers and needs to be highly available and resilient.

Best Use Cases:

• Real-time analytics on large datasets.
• Applications that need to handle large write volumes, such as IoT platforms, sensor data, and time-series data.
• Global applications with zero downtime requirements, such as e-commerce and financial systems.

3. Redis

Redis is an open-source real-time database that provides services as a database, cache, and also as message broker to real-time applications. It supports data storage and manipulation by using various data structures like Strings, lists, and sets.

Key Features

1. In-memory Storage: Performs rapid read and write operations as data is stored in memory. This is highly preferred by the applications that require quick access to the data.
2. Variety of Data Structures: Uses various data structures like Strings, lists, hashes, sets, Geospatial indices, etc for data modeling.
3. Replication: Supports duplication of data by following the master-slave concept. Here the data is copied from a master instance and then duplicated into several slave instances.
4. Memory Management: Has effective memory management techniques like memory optimization and memory eviction.

Where to Use: Redis is perfect for applications needing rapid data access, often used as a cache or message broker for real-time systems. It’s commonly used in scenarios requiring fast reads and writes, such as gaming, recommendation engines, and social media apps.

Best Use Cases:

• Real-time applications like session storage, caching, and queuing systems.
• Applications requiring fast, in-memory operations like leaderboards and counters.
• Chat apps or real-time notifications.

4. Amazon Dynamo DB

Amazon Dynamo DB has a fast single-digit millisecond response rate and enables you to host and run modern applications. It is hosted by Amazon and is a NoSQL database service available in AWS. This service provides immediate or fast data access and data retrieval.

Key Features

1. Serverless: Amazon DynamoDB is a completely cloud-based platform that doesn't require a server to manage and patch. It doesn't have any major, minor, or patch versions, and also doesn't require any software installation.
2. Security: Provides high security and encryption of data with frequent backups.
3. Cost-effectiveness: Generally, the service comes with a free tier that gives you access for a while. Later on, you can use the Amazon DynamoDB Standard-IA table class that costs 60% less on the usage of Amazon DynamoDB.
4. Integration with other AWS Services: Amazon DynamoDB lets you integrate with various other AWS Services like Microsecond latency with Amazon DynamoDB Accelerator, Bulk export and import from AWS's S3 Storage service, and many more.

Where to Use: As a fully managed cloud service, DynamoDB is ideal for serverless applications that need to scale with traffic. It’s suited for applications hosted in AWS, particularly those needing low-latency access to data with minimal operational overhead.

Best Use Cases:

• Mobile and web apps with unpredictable workloads.
• E-commerce and gaming applications requiring high availability and low-latency data access.
• IoT applications, and systems with frequent read and write operations.

5. Microsoft Azure Cosmos Database

Azure Cosmos DB is a cloud-based NoSQL Platform hosted by Microsoft which is a serverless and low-latency database that can handle very large volumes of data globally. It can index the data automatically and query using SQL query language and is free of schema, it also has UDFs (User Defined Functions) that are written in JavaScript.

Key Features

1. Globally Accessed: Azure regions are spread out all around the globe, as a result of this, the data can be replicated globally.
2. High Scalability: Azure Cosmos DB can be scaled horizontally and supports millions of reads and writes per second.
3. Multi-model: The data is stored in Graph-Based, Key-value pairs, Document-based, and Column Family-based databases, the global accessibility and its scalability are always the same for every data model.
4. Low Latency: It has very low latency as the data is distributed globally, which makes it easily available to the customers.

Where to Use: Best for global applications that require low-latency access to data and can scale horizontally across regions. Azure Cosmos DB is ideal for multi-model applications that need to handle various types of data (documents, key-value pairs, graphs).

Best Use Cases:

• Globally distributed applications, such as social media platforms and e-commerce sites.
• Applications requiring fast responses regardless of location, such as mobile and gaming apps.
• Multi-region data replication and processing for enterprise applications.

6. Firebase Real-time Database

Firebase Database is a cloud-based NoSQL Platform that uses JSON format to store the data which makes the development process easy and flexible and the data is synchronized to all the connected clients in real-time. Although Firebase itself isn't a database, it is a Backend-as-a-Service (BaaS) that includes two types of databases which are - Cloud Firestore and real-time database.

Key Features

1. Analytics: To access this feature Firebase SDK must be installed. It keeps track of all the activities done by the user and gives analysis.
2. Authentication: Email ID can be used to set up authentication or social media authentication can also be used.
3. Storage: This lets the user store data like images, videos, GIFs, etc. Data stored is secured in Firebase.
4. Cloud Messaging: Allows users to send messages to different platforms either to a single person or to a group of people at once.

Where to Use: Firebase is ideal for rapid development of mobile and web applications with real-time data synchronization. It's perfect for apps that need constant updates, such as chat applications or real-time collaboration tools.

Best Use Cases:

• Real-time collaboration apps like document editing, whiteboards, etc.
• Mobile apps with real-time user data updates.
• IoT applications, where device data needs to be streamed in real-time to users.

7. RethinkDB

RethinkDB makes the process of making apps and managing data easier. It can query JSON documents with many languages with technologies like Socket.io or SignalR. It allows you to develop applications free of cost and stores the JSON documents with dynamic schemas which makes the process highly efficient.

Key Features

1. Control: It has an intuitive web UI that allows you to scale your apps in just a few clicks and a simple API for control
2. Speed: It is faster than many of its competitors as it performed a 16-node cluster with a latency of 3 milliseconds.
3. User-friendly: The open-source and scalable database has a simple design and is easy to use. It reduces the time and effort required to build real-time apps.
4. Capability: RethinkDB is a general-purpose database, that can run subqueries, aggregation, geospatial queries, table joins, map-reduce, and many other arbitrary queries.

Where to Use: RethinkDB is best for applications requiring real-time, push-based data updates and querying capabilities. It is ideal for building reactive apps where changes to the database need to be reflected in real-time.

Best Use Cases:

• Real-time apps, such as live-streaming, gaming, and social media platforms.
• Collaborative tools like live editing and instant notifications.
• Use cases where dynamic schemas and real-time queries are a requirement.

8. Hazelcast

Hazelcast is a real-time data stream processing platform based on Java that allows you to build applications and take action quickly with precise control. It combines stream processing with a fast data store. Hazelcast is an In-Memory Data Grid (IMDG) that also provides plugins and APIs for building caches for your data.

Key Features

1. Many Uses: Hazelcast can be used in many cases like supply-chain logistics, AI ops, and retail banking
2. Low Latency: It is cloud agnostic with a latency of 2 milliseconds for 18k/s throughput.
3. Programmability: It can be coded using languages like Node.js, Python, Java, C++, and GO.
4. Node efficiency: Hazelcast can tolerate node failures, it automatically partitions and replicates data in the cluster. It allows you to add new nodes to increase storage capacity instantly.

Where to Use: Hazelcast is excellent for low-latency, in-memory computing and stream processing. It works well in scenarios where you need to process data at a very high throughput and in real-time.

Best Use Cases:

• Real-time analytics, AI/ML applications, and stream processing.
• Applications requiring high-throughput data streaming, such as supply-chain management and financial trading systems.
• Low-latency, high-throughput use cases like recommendation systems, real-time fraud detection.

9. Apache Kafka

Apache Kafka is an open-source event streaming platform with high-performance pipelines, data integration, and streaming analytics. In the beginning, Apache Kafka’s APIs were available only in Scala and Java, and later on, It was built for various languages and allows us to choose whatever language we choose.

Key Features

1. Built-in stream processing: With built-in stream processing, it can work with JMS, Postgres, Elasticsearch, AWS, and many other systems.
2. High scalability: It is highly scalable and can be integrated into various other real-time databases like RethinkDB and Hazelcast.
3. Low latency: Even for clusters or machines, it provides a latency of less than 2 milliseconds making it fast among many others.
4. Suggested by users: Apache Kafka is used by many companies including over 80% of the Fortune 100.

Where to Use: Kafka is perfect for real-time data streaming, event-driven architectures, and integrating large-scale data pipelines. It’s well-suited for systems that need to handle high volumes of real-time event data.

Best Use Cases:

• Event streaming and log aggregation, such as real-time user activity feeds.
• Data integration across multiple systems (e.g., enterprise systems, cloud services).
• Real-time analytics platforms and monitoring systems.

10. Aerospike

Just like Firebase, Aerospike is another popular multi-model NoSQL real-time database enabling organizations to work across billions of transactions in a few seconds. Aerospike is a row-oriented database, meaning the data is organized in records. It is a quick and traditional way of organizing data.

Key Features

1. Extremely low latency: For storing 2TB of data, Aerospike has less than 1 millisecond latency, although it doesn't store data in memory.
2. Less Infrastructure: It is preferred by small organizations as it requires 80% less infrastructure to work
3. Applications: It is leveraged on large-scare documents and also for SQL use cases and it is built for multi-cloud.
4. Client-Server solution: Aerospike allows you to build app server clusters that attach over TCP to your database cluster using Aerospike’s provided libraries.

Where to Use: Aerospike is best for applications that require extreme low-latency data access and high throughput, such as ad tech, financial services, and real-time analytics. It’s especially useful for systems requiring fast reads and writes on large datasets.

Best Use Cases:

• Real-time decision-making systems, such as fraud detection or personalized ad serving.
• Applications requiring high scalability with minimal infrastructure, like real-time bidding or recommendation engines.
• Large-scale operational databases for financial services, gaming, and telecom.

Comparison Table for Real-Time Databases

These databases offer a range of features and are suitable for various use cases in real-time data management.

Also Read:

• Top 15 Vector Databases that You Must Try
• Top 10 Open Source Graph Databases
• Top 7 Databases for Mobile App Development

Conclusion

This is the list of the top 10 real-time databases frequently used in the industry made to ease the task of finding a suitable database for developing and building applications. Each of them has its features and rival each other. These real-time databases can be used anywhere as per the requirement of the application. There still are quite a few databases out there with advanced features still being developed, the above list of databases is the most advanced databases of the year which meet the requirements of most users.