Data streaming has been around since the dawn of time, but with the big-data boom, companies are scrambling to come up with new and innovative ways to deal with the influx of data generated by their users every day. Here are the top 8 data streaming tools that you can't afford to miss!
#1 - Kafka
Kafka is an open-source software framework which can be used as a publish-subscribe messaging system. The primary benefit of Kafka is that codeigniter development services india it handles the majority of the heavy lifting involved in a publish-subscribe architecture, simplifying the development process for developers. A large part of Kafka's popularity stems from its robust design and scalability.
#2 - Kinesis Streams
Kinesis Streams is a managed service designed for streaming big data for applications that need massive scale and high availability. Built for the AWS cloud, Kinesis streams captures up to thousands of data records per second from various data sources, transforms it into a stream of events, applies additional processing if necessary, replicates it across multiple streams (called shards) in order to tolerate outages and other errors. Processing power is optimized by fanning out the work across several nodes within one or more stream groups.
#3 - Firehose
Firehose is one of the newer data streaming tools, yet it's already shaping up to be a leading contender in the market. It was built for big data processing and analytics (hence the name). The creators have taken a lot of time focusing on performance, as well as robust system design. If you're looking for an analytics platform that can process large amounts of data quickly, this is worth checking out.
#4 - SignalR
SignalR is an open-source library that includes both a client and server for real-time web communication. SignalR handles much of the plumbing, so you can build rich, interactive applications with relatively few lines of code. SignalR supports bi-directional communications from the browser back to the server without incurring prohibitive latency when transmitting data over HTTP/S. It also has inbuilt automatic reconnection and long polling when using WCF (Windows Communication Foundation).
#5 - Azure Stream Analytics
Azure Stream Analytics is a fully managed analytics service that allows customers of all skill levels to rapidly build and run powerful real-time applications without having to invest in infrastructure. For example, customers can instantly gain insights into their internet of things data using pre-built logic and an interactive drag-and-drop UI.
#6 - Redis Labs Streams
Redis Labs is a new startup that's giving competitors like Kafka and Storm a run for their money. The company, founded by former Redis employees and scientists, has developed an enterprise-grade stream processing engine built on top of Redis that is capable of ingesting billions of messages per second, in real time. Best yet? It can handle this amount of throughput while maintaining the simplicity and usability that have made Redis popular with its users over the years.
#7 - Apache Flink
Developed by the Apache Software Foundation, Apache Flink is an open-source, powerful and scalable tool for building streaming data applications. In a nutshell, it is a one-stop shop for all things data, with support for high-level abstractions such as transformation of data or a data processing application. It allows you to implement distributed, fault-tolerant streaming applications in Java and Scala. Flink also boasts features such as in-built linear algebra libraries, stateful stream processing and SQL interfaces.
#8 - Storm
Storm is a distributed, fault-tolerant, and open source real-time computation system. Storm makes it easy to reliably process unbounded streams of data. It's free, super easy to use and very scalable. Storm offers powerful primitives that make the whole process of stream analysis easier by abstracting away low level operational details so users can work at a higher level of abstraction and deal with complex event patterns in a scalable way.
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