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How does apache storm work with hadoop and hadoop?


Asked by Jasiah Perez on Dec 04, 2021 Hadoop



Similar to what Hadoop does for batch processing, Apache Storm does for unbounded streams of data in a reliable manner. Apache Storm is able to process over a million jobs on a node in a fraction of a second. It is integrated with Hadoop to harness higher throughputs.
Consequently,
Storm is for Fast Data (real time) & Hadoop is for Big data(pre-existing tons of data). Storm can't process Big data but it can generate Big data as a output. Apache Storm is a free and open source distributed realtime computation system.
In this manner, Apache Storm is a distributed, fault-tolerant, open-source computation system. You can use Storm to process streams of data in real time with Apache Hadoop. Storm solutions can also provide guaranteed processing of data, with the ability to replay data that wasn't successfully processed the first time.
Accordingly,
Hadoop store data using HDFS and process data using MapReduce. Hadoop works step by step: Step1- Input data is broken into blocks of size 64 Mb or 128 Mb and then blocks are moved to different nodes. Step 2- Once all the blocks of the data are stored on data-nodes, user can process the data.
Also Know,
Typically Hadoop is used as storage layer whenever Storm and Kafka are used. Hadoop stores either raw data or processed data or usually summarized view of data from [ kafka and Storm integrated system]. If in case hadoop is not used, a nosql data store is used as an alternative storage system.