Both Hadoop and Spark use HDFS

Both Hadoop and Apache Spark can use HDFS (Hadoop Distributed File System) for distributed storage of data, but they use it in slightly different ways:

1. Hadoop and HDFS:

   • Hadoop was initially developed as a framework for distributed storage (HDFS) and batch processing (MapReduce).
   • HDFS is a distributed file system designed for storing and managing large volumes of data across a cluster of commodity hardware.
   • In Hadoop, HDFS is the primary storage layer. It stores data in the form of blocks across multiple nodes in a Hadoop cluster.
   • Hadoop MapReduce is a batch processing framework that runs jobs on data stored in HDFS. It reads data from HDFS, processes it, and writes the results back to HDFS.

2. Apache Spark and HDFS:

   • Apache Spark, on the other hand, is a fast and versatile distributed data processing framework that can work with various storage systems, including HDFS.
   • While Spark has its own distributed data storage abstractions like RDDs (Resilient Distributed Datasets) and DataFrames, it can also read and write data directly from/to HDFS.
   • Spark does not rely solely on HDFS as its storage layer; it can work with data stored in various formats and systems, including HDFS, Amazon S3, Azure Data Lake Storage, and more.
   • Spark’s in-memory processing capabilities allow it to efficiently process data from HDFS or other storage systems, making it faster than traditional batch processing frameworks like Hadoop MapReduce.