Real-time databases are ideal for data storage and synchronization in monitoring and streaming. Although relational databases have existed for a long time, real-time databases are becoming more popular because they allow for real-time data storage and synchronization with nearly no delay. Online streaming services like Netflix, Prime Video, and others, as well as businesses that deal with huge amounts of data, rely on databases that can be watched while also providing high security and encryption. Real-time databases are the best way to do this.
Let’s look at the top 10 real-time databases used by large organizations; some with a simple architecture that may also be utilized by startups.
Firebase
Firebase, a Google Developers product, is the emerging star for real-time data storage and synchronization. It is a cloud-hosted NoSQL database that allows for worldwide app data queries. It includes mobile and web SDKs for developing serverless apps, and users may also run backend code to get replies via database-triggered events.
Firebase, in addition to providing high security through authentication, is optimized for offline use by storing data in the local cache, which is subsequently uploaded and synchronized online when the device is connected. Firebase has a greater latency of roughly 100 milliseconds when compared to Redis and others.
Aerospike
Aerospike is another prominent NoSQL real-time database that allows organizations to operate across billions of transactions in seconds. It is designed for multi-cloud, large-scale JSON documents, and SQL use cases. It has the smallest footprint due to its proprietary Hybrid Memory Architecture. Aerospike boasts less than 1-millisecond latency for storing 2 TB of data, although it does not keep data in memory. When compared to other real-time databases, it takes 80% less infrastructure to operate, making it ideal for smaller businesses.
Redis
Redis is a popular and dependable real-time database for speed and simplicity, with a highly scalable caching layer for optimal corporate performance. It may also identify data using AI-based transaction grading to aid in fraud detection. It’s most typically used for caching, message brokers, and database deployment across clouds and hybrid systems. Redis promises a latency of 200 microseconds on a 1GB/s network. It is available in all languages and operates on macOSX, Linux, Windows, and BSD.
Apache Kafka
A global event streaming platform featuring open-source pipelines, streaming analytics, and data integration. Kafka can operate with Postgres, JMS, Elasticsearch, AWS, and many more databases because of its built-in stream processing. Kafka is incredibly scalable, with a latency of fewer than 2 milliseconds for clusters of servers, and it can be combined with other real-time databases such as Hazelcast and RethinkDB, among others.
RethinkDB
RethinkDB, an open-source, scalable database, simplifies the process of designing apps as well as the laborious process of maintaining data. It enables you to query JSON documents in dozens of languages and create contemporary apps using technologies such as Socket.io and SignalR.
The easy API for precise control allows you to scale your app clusters with a few clicks using the intuitive web UI. RethinkDB completed a 16-node cluster at a delay of 3 milliseconds, outperforming several of its competitors.
AWS Kinesis
Amazon’s Kinesis, which makes it easier to handle and analyze gathered and real-time data, is administered on the AWS server, demonstrating its scalability. It supports data buffering and operates fully controlled on streaming applications. This database’s most prominent application is for developing video analytics apps. Kinesis is an excellent tool for developing application monitoring, detecting fraud, and displaying live leaderboards.
Hazelcast
Hazelcast, a real-time data stream processing platform, allows you to create apps and perform rapid actions. It can be written in Java, Node.js, Python, C++, and Go. Hazel may be used for a variety of applications, including retail banking, AI operations, and supply-chain logistics, among many others. It is cloud-independent, with an average latency of roughly 2 milliseconds for 18k/s throughput.
PostgreSQL
To solve the shortcomings of current database efforts, the great Michael Stonebraker headed the POSTGRES (Post-Ingres) project in 1986. The POSTGRES project, which was a Relational Database Management System, gave birth to PostgreSQL. For the last 30 years, PostgreSQL has led the way in modern database development, producing many advancements, and Michael Stonebraker got a Turing Award in 2014 mostly for his work in PostgreSQL. PostgreSQL is presently one of the most popular database systems. It is also the most sophisticated Open-Source Relational Database.
Oracle
The article (current CTO of Oracle Corporation), notably motivated Larry Ellison, a young software developer He created Oracle, the world’s first commercially available RDBMS system, in 1979. Since then, Oracle has been the premier commercial RDMBS system and has dominated the Unix and Linux platforms. Over the previous 41 years, Oracle has matured, contributing to RDBMS and overall database System improvements.
MySQL
In 1995, two software programmers named Michael Widenius and David Axmark built the Open-Source Relational Database Management System (RDBMS) MySQL. Because of its enterprise-grade capabilities, free, flexible (GPL) community license, and improved commercial license, MySQL quickly acquired favor in the industry and community. PostgreSQL stresses innovation and sophisticated functionality among open-source databases, whereas MySQL emphasizes robustness, stability, and maturity.
MySQL is currently widely recognized as one of the most well-known and commonly used SQL databases. It’s also one of the most popular databases used in online applications. Some of the world’s largest Web-Scale applications rely on MySQL (for example, Facebook and Uber).
Author- Toshank Bhardwaj, AI Content Creator
