Streaming and real-time analysis are required in many systems in big data. Batch processing is very well handled by Hadoop and integration of frameworks like Storm and Spark elevates their streaming and real-time capability.
We discussed that Storm is an open source, fast, stream processing, scalable, fault-tolerant, and reliable system that is easy to use and deploy. Storm's physical architecture comprises Nimbus, Supervisor, Worker, and Zookeeper processes. The data architecture of Storm comprises a spouts, bolts, and topology-based data flow system.
Spark is an extremely popular framework which provides in-memory data handling capability and makes it much faster than the MapReduce framework. Spark frameworks have some libraries such as Spark SQL, GraphX, MLib, Spark Streaming, and others to process specialized data and requirements. Spark Architecture is based on RDDs and the DAG engine, which provides capability of in-memory data processing and optimizes the processing, according...
