Technology Security Analyst
Apache Storm is a distributed real-time big data-processing system. Storm is designed to process vast amount of data in a fault-tolerant and horizontal scalable method. 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. Storm is easy to setup, operate and it guarantees that every message will be processed through the topology at least once
Apache Kafka is a distributed data store optimized for ingesting and processing streaming data in real-time. Streaming data is data that is continuously generated by thousands of data sources, which typically send the data records in simultaneously. A streaming platform needs to handle this constant influx of data, and process the data sequentially and incrementally.
Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.
Apache Kafka use to handle a big amount of data in the fraction of seconds. It is a distributed message broker which relies on topics and partitions. Apache Storm is a fault-tolerant, distributed framework for real-time computation and processing data streams. It takes the data from various data sources such as HBase, Kafka, Cassandra, and many other applications and processes the data in real-time. It has been written in Clojure and Java.
Following is the key difference between Apache Storm and Kafka:
1) Apache Storm ensure full data security while in Kafka data loss is not guaranteed but it’s very low like Netflix achieved 0.01% of data loss for 7 Million message transactions per day.
2) Kafka can store its data on local filesystem while Apache Storm is just a data processing framework.
3) Storm works on a Real-time messaging system while Kafka used to store incoming message before processing.
4) Apache Kafka is used for processing the real-time data while Storm is being used for transforming the data.
5) Kafka gets its data from the actual source of data while Storm pulls the data from Kafka itself for further processes.
6) Kafka is an application to transfer real-time application data from source application to another while Storm is an aggregation & computation unit.
7) Kafka is a real-time streaming unit while Storm works on the stream pulled from Kafka.
8) It’s mandatory to have Apache Zookeeper while setting up the Kafka other side Storm is not Zookeeper dependent.
9) Kafka works as a water pipeline which stores and forward the data while Storm takes the data from such pipelines and process it further.
10) Kafka is a great source of data for Storm while Storm can be used to process data stored in Kafka.
11) Apache Storm has inbuilt feature to auto-restart its daemons while Kafka is fault-tolerant due to Zookeeper.
Below is the comparison table between Apache Storm and Kafka.
Apache Storm versus Kafka both are autonomous and have an alternate reason in Hadoop group climate.
Apache Storm versus Kafka both are autonomous of one another anyway it is prescribed to utilize Storm with Kafka as Kafka can reproduce the information to storm if there should be an occurrence of bundle drop likewise it verify prior to sending it to Storm.
Kafka's job is to function as middleware it takes information from different sources and afterward Storms processes the messages rapidly. Counting and isolating of online votes is the ongoing model for Apache Storm.
Apache Storm versus Kafka both are having incredible ability in the continuous spilling of information and truly proficient frameworks for performing constant examination.
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