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What is the difference between apache storm and apache spark?


Asked by Gabriel Coffey on Nov 29, 2021 Spark Programming guide



Apache Storm is the stream processing engine for processing real time streaming data while Apache Spark is general purpose computing engine which provides Spark streaming having capability to handle streaming data to process them in near real-time.
Next,
Apache Spark is an open-source, distributed processing system used for big data workloads. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. It provides development APIs in Java, Scala, Python and R, and supports code reuse across multiple workloads-batch processing, interactive queries, real-time analytics, machine learning, and graph processing.
Similarly, Storm and Spark are designed such that they can operate in a Hadoop cluster and access Hadoop storage. The key difference between Spark and Storm is that Storm performs task parallel computations whereas Spark performs data parallel computations.
Also,
It is a streaming data framework that has the capability of highest ingestion rates. Though Storm is stateless, it manages distributed environment and cluster state via Apache ZooKeeper. It is simple and you can execute all kinds of manipulations on real-time data in parallel. Apache Storm is continuing to be a leader in real-time data analytics.
In respect to this,
In this chapter, we will learn how to integrate Kafka with Apache Storm. Storm was originally created by Nathan Marz and team at BackType. In a short time, Apache Storm became a standard for distributed real-time processing system that allows you to process a huge volume of data.