Apache Hadoop vs Apache Storm

Difference Between Apache Hadoop vsApache Storm

Big Data has become a popular open source technology in recent time and every day new framework is being added to Hadoop stack to solve the complex problem related to the huge volume of data.

To perform analysis of the data Hadoop uses processing framework like Hadoop with MapReduce for batch processing and Apache storm for stream processing hence, storm and Hadoop helps an organization to choose right technology from Hadoop stack. Let’s look into what is Apache Hadoop and Apache Storm.

Apache Hadoop:

Apache Hadoop is an open-source batch processing framework used to process large datasets across the cluster of commodity computers. It was the first big data framework which uses HDFS (Hadoop Distributed File System) for storage and MapReduce framework for computation. Because of its scalability feature, new nodes can be easily added to the existing system if the amount of data increases and due to its fault tolerance nature system is prone to failure so that system s available all the time i.e. high-availability.

Apache Storm:

Apache storm provides real-time data processing capabilities to Hadoop stack and it is also an open source. Apache storm can handle the very large amount of data and delivers result with low latency (near real-time).Apache storm does not run on Hadoop cluster instead it uses Apache ZooKeeper to coordinate topologies present in DAG (Directed Acyclic Graph).

Check out the official website mention below for why to use Storm: http://storm.apache.org/

Head to Head Comparison Between Apache Hadoop vs Apache Storm (Infographics)

Let us check out Top 6  the difference between Apache Hadoop vs Apache Storm in detailed format in below tabular format:

Key Differences between Apache Hadoop vs Apache Storm

Let us discuss the key difference between Apache Hadoop vs Apache Storm

Apache Hadoop	Apache Storm
Distributed Batch processing of large volume and unstructured dataset.	Distributed real-time processing of data having a large volume and high velocity.
Framework is written in Java.	Storms is written in Half Java and Half Clojure code, but a majority of code/logic is written in Clojure.
It is Stateful streaming processing.	It is Stateless streaming processing.
It uses Apache Zookeeper coordination.	It may or may not uses Apache Zookeeper for coordination.
MapR jobs are executed in a sequential manner still it is completed.	Storm topology runs continuously until system shutdown.
It has High Latency (Slow Computation).	It has Low Latency (Fast Computation).
Architecture is based on a topology of Spouts and bolts.	Architecture consists of HDFS and MapReduce.
Data is continuously streamed and it is dynamic.	Data is static and nonvolatile (Data is Persistence).
It is easy to setup but operating Hadoop cluster is difficult.	It is easy to setup and operating storm cluster is also easy.
Use Cases: Twitter, Navisite, Wego etc.	Use Cases: Black Box Data, Search Engine Data etc.

Apache Hadoop vs Apache Storm Comparison Table

 

Following is the comparison between Apache Hadoop vs Apache Storm.

Apache Hadoop	Apache Storm
Processing framework used by Hadoop is a distributed batch processing which uses MapReduce engine for computation which follows a map, sort, shuffle, reduce algorithm.	Processing framework used by Storm is distributed real-time data processing which uses DAGs in a framework to generate topologies which are composed of Stream, Spouts, and Bolts.
Speed: Due to batch processing on a large volume of data Hadoop take longer computation time which means latency is more hence Hadoop is relatively slow.	Speed: Due to near real-time processing Storm handle data with very low latency to give a result with minimum delay.
Development Ease: Hadoop MapReduce framework is written in Java programming language. Hadoop development is made easier by the use of Apache pig (Scripting Language) and Apache Hive (SQL compatible) on top of Hadoop.	Development Ease: Apache Storm is written in Clojure.It uses DAGs for processing model. In Storm Spouts and Bolts make topology and it can be written in any language. Every node in DAG transforms data to continue the process.
Architecture: The architecture of Hadoop consists of HDFS for data storage and MapReduce for Computation.	Architecture: The Architecture of Storm consists of stream, spouts, and bolts this describe the steps that will be performed
Data Availability: Hadoop uses HDFS as a storage which is persistent storage and provides static data for processing.	Data Availability: Storm can integrate with YARN resource negotiator of Hadoop to use Hadoop storage and data which is dynamic and continuously streamed
Current Release: As of February 2018 latest version of Apache Hadoop is 3.0.0 and it is easy to set up but difficult to operate.	Current Release: As of February 2018 latest version of Apache storm is 1.2.0 and it is easy to set up and operate.

Apart from differences, there are some similarities also available in Hadoop and Storm like both are Open Source technologies with a scalable and fault-tolerant feature used in business intelligence and big data analytics sector in organizations.

Conclusion

Apache Hadoop provides batch processing for handling very large datasets with high latency and uses commodity hardware which makes it less expensive and it also supports other frameworks with diverse technology. But for near real-time processing with very low latency storm is the best option which can be used with multiple programming languages. Hence, as per the need of organization, we can use Apache storm or Apache Hadoop for real-time or batch processing.

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