Top 7 Most Popular Real-Time Databases in 2024
While relational databases have been around for a long time, real-time databases have been on the rise because they allow storing and syncing of data in real-time with almost zero latency. Streaming platforms like Netflix, Prime Video, and others, along with companies for managing large influx of data rely on databases that can be monitored and also provide high security and encryption, and to that end, real-time databases are the preferred way. Check out the top 7 real-time databases that are being used for big organisations; with some with a small architecture that can be used for startups as well! 1. Redis One of the most popular and reliable real-time databases for speed and simplicity, Redis has a highly scalable caching layer for best enterprise performance. It can also handle identifying data using the AI-based transaction scoring for easier fraud detection. It is most commonly used for cache, message brokers, and deployment of databases across clouds and hybrid environments. Redis boasts its latency for 1GB/s network with 200 microseconds. It runs on macOSX, Linux, Windows, and BSD and is supported in all languages. Explore more about Redis 2. Firebase A Google Developers’ product, Firebase is the rising star for real-time storing and syncing of data. It is a cloud-hosted NoSQL database that enables querying app data at a global scale. It ships with mobile and web SDKs for building serverless applications and users can also execute backend code to receive responses from triggered events of the database. Along with offering strong security with authentication, Firebase is also optimised for offline use by storing data on local cache which is then uploaded and synchronised online when the device is connected. Compared to Redis and others, Firebase has a higher latency of around 100 milliseconds. Explore more about Firebase 4. Aerospike Enabling organisations to work across billions of transactions in seconds, Aeropike is another popular NoSQL real-time database. It is built for multi-cloud, leveraged on large-scale JSON documents and also for SQL use cases. Owing to its patented Hybrid Memory Architecture, it has the lowest footprint. For storing 2 TB of data, Aerospike has less than 1 millisecond latency, but it does not store data in the memory. While compared to other real time databases, it requires 80% less infrastructure to work and is therefore preferable for smaller organisations. Explore more about Aerospike 5. RethinkDB The open-source, scalable database, RethinkDB, makes the process of building apps dramatically easier and makes the cumbersome process of managing data a lot easier. It allows you to query JSON documents with dozens of languages and build modern apps with technologies like Socket.io or SignalR. The intuitive web UI allows you to scale your app clusters in merely a few clicks with a simple API for precise control. RethinkDB performed a 16-node cluster with a latency of 3 milliseconds—making it faster than a lot of its competitors. Explore more about RethinkDB 5. Apache Kafka An open-sourced distributed event streaming platform with high performance pipelines, streaming analytics, and data integration. With built-in stream processing, Kafka can work with Postgres, JMS, Elasticsearch, AWS, and many more. With a latency of less than 2 milliseconds for clusters of machines, Kafka is extremely scalable and can be integrated on various other real-time databases like Hazelcast and RethinkDB, among others. Explore more about Apache Kafka 6. AWS Kinesis Making it easier to process and analyse collected and real-time data, Amazon’s Kinesis, is managed on the AWS server, which proves its scalability. It allows buffering of data and runs fully managed on the streaming applications. The most notable application for this database is for building video analytics applications. Kinesis is very efficient for building application monitoring, fraud detection, and showcasing live leaderboards. Explore more about AWS Kinesis 7. Hazelcast Hazelcast, a real-time data stream processing platform enables you to build applications and take actions immediately. It can be coded on languages like Java, Node.js, Python, C++, and Go. Hazel can be used for many use cases like retail banking, AI ops, supply–chain logistics, among many other data management applications. It is cloud agnostic and has an average latency of Hazelcast is around 2 milliseconds for 18k/s throughput. Explore more about Hazelcast
Real-time databases are perfect for storing and syncing data for monitoring and streaming services.
Real-time databases are perfect for storing and syncing data for monitoring and streaming services.
["apache kafka", "firebase", "NoSQL Databases", "Redis"]
["Elasticsearch", "NoSQL Databases", "firebase", "AWS", "AI", "serverless", "RAG", "Python", "analytics", "Kafka", "apache kafka", "fraud detection", "Redis"]
["AI", "analytics", "RAG", "fraud detection", "AWS", "serverless", "Kafka", "Redis", "Elasticsearch", "Python"]
Mohit writes about AI in simple, explainable, and often funny words. He's especially passionate about chatting with those building AI for Bharat, with the occasional detour into AGI.