Apache has given to the IT world two robust frameworks, both effective and efficient, with certain similar features but with certain distinguished differences too. Yes, this is about Apache Storm and Apache Spark. Recently, we read about Apache Storm and a few days earlier, about Apache Spark. Individually, a lot has been said and read about both of them. Let us now evaluate both of them and cull out an analysis.
Apache Storm is a task parallel, real time, distributed computing system, which has its workflows in topologies (Directed Acyclic Graphs). The topologies are executed till the system is shutdown or encounters some sort of a disturbance. Storm makes use of Apache ZooKeeper and other processes to manage its entire process and does not run on Hadoop clusters. It can use files from HDFS and also write back to HDFS.
Apache Spark is a data parallel, general use framework for big data processing; which has its workflows in MapReduce but is much more efficient than Hadoop MapReduce. Spark does not need Hadoop YARN to function; it has its own independent processes and has a streaming API which lets continual processing through short interval batches.
A Sleek Comparison Between The Two Big Data Giants
- Both are Open Source Frameworks
- Both of them are implemented in JVM based languages
- They have simple implementation methodology, which attracts the developers
- Both of them provide real time analytics
- They both provide Scalability and fault tolerance
Both these frameworks possess their own plus points. There are certain factors like Latency, Cost of development, fault tolerance, message delivery guarantees and the like which play an important role in deciding which one is better. They both are great solutions to solve the streaming and transformation issue. Each one of them can be an ideal choice for the organization depending upon which one you choose by observing the above points and understanding the requirements.