5 Streaming Analytics Platforms For All Real-time Applications

Through Streaming analytics, real-time information can be gathered and analyzed from and on the cloud. The information is captured by devices and sensors that are connected to the Internet, as part of the Internet of Things disruption.

Streaming analytics solutions allow organizations to build real-time solutions using IoT and extract information from them later using Big Data, or to churn them first-hand using real-time processing. The power of streaming analytics is such that it allows for the streaming of millions of events in a second and thus allows enterprises to build mission-critical applications that require the performance to be quick and efficient.

Real-time streaming analytics can, for example, present to you the statistics if your latest online ad campaign is working as expected, or if it needs some tweaking to work better. Such applications want to always stay upgraded for performance benefits.

Here are the top platforms being used all over the world for Streaming analytics solutions:

Apache Flink

Flink is an open-source platform that handles distributed stream and batch data processing. At its core is a streaming data engine that provides for data distribution, fault tolerance, and communication, for undertaking distributed computations over the data streams. In the last year, the Apache Flink community saw three major version releases for the platform and the community event Flink Forward in San Francisco. Apache Flink’s open-source contributors on Github have increased from 258 in December 2016 to 352 in December 2017. Apache Flink contains several APIs to enable creating applications that use the Flink engine. Some of the most popular APIs on the platform are- DataStream API for unbounded streams, DataSet API for static data embedded in Python, Java, and Scala, and the Table API with a SQL-like language.

​​Spark Streaming

Apache Spark is used to build scalable and fault-tolerant streaming applications. With Spark Streaming, you get to use Apache Spark’s language-integrated API which lets you write streaming jobs in the similar way as you write batch jobs. Spark Streaming supports the three languages- Java, Scala, Python. Apache Spark is being used in various leading industries today, such as- Healthcare, Finance, e-commerce, Media and Entertainment, Travel industry, etc. The popularity of Apache Spark adds the glitter to the platform Spark Streaming.

IBM Streams

This streaming analytics platform from IBM enables the applications developed by users to gather, analyze, and correlate information that comes to them from a variety of sources. The solution is known to handle high throughput rates and up to millions of events and messages per second, making it a leading proprietary streaming analytics solution for real-time applications. IBM Stream computing helps analyze large streams of data in the form of unstructured texts, audio, video, and geospatial, and allows for organizations to spot risks and opportunities and make efficient decisions.

Software AG’s Apama Streaming Analytics

Apama Streaming analytics platform is built for streaming analytics and automated action on fast-moving data on the basis of intelligent decisions. The software bundles up other aspects like messaging, event processing, in-memory data management and visualization and is ideal for fast-moving Big Data Analytics Solutions. Sensors that bring in loads of data from different sources can be churned using this solution in real-time. With Apama, you can act on high-volume business operations in real-time.

Azure Stream Analytics

Azure Stream Analytics facilitates the development and deployment of low-cost solutions that can gain real-time insights from devices, applications, and sensors. It is recommended to be used for IoT scenarios like real-time remote management and monitoring, connected cars, etc. It allows to easily develop and run parallel real-time time analytics on IoT and other kinds of Big Data using a simple language that resembles SQL.

These streaming applications and platforms are helping organizations drive their streaming analytics goals and IoT solutions with ease. Big Data is a source of knowledge today and organizations are increasingly trying to leverage its potential to drive their decisions and major changes.