How Apache Spark Works
Apache Spark is a powerful, distributed computing framework designed for fast data processing and analysis on a large scale. It helps users run complex computations on huge datasets and is faster than other frameworks like Hadoop MapReduce, thanks to its memory-based architecture. Key Concepts of Apache Spark: RDD (Resilient Distributed Dataset): RDD is the core concept of Spark. It's a distributed collection of data that can be processed across multiple machines (or nodes) in parallel. RDDs are immutable (they can't be changed after creation) and are stored in memory by default, which makes processing faster. If needed, Spark can also save RDDs to disk. Parallel and Distributed Processing: Spark can process data in parallel on multiple machines. Each machine (or node) processes part of the data. Spark uses a resource manager like YARN or Mesos to manage the resources and schedule the jobs across the cluster. Lazy Evaluation: ...