Big Data Tooling: Spark, Hadoop, and Beyond
Big Data Tooling: Spark, Hadoop, and Beyond Big data tooling helps teams collect, transform, and analyze data at scale. The field today centers on two classic engines, Spark and Hadoop, plus a growing set of modern options that aim for speed, simplicity, or portability. Understanding where these tools fit can save time and reduce costs. Apache Hadoop started as a way to store and process data across many machines with a distributed file system and MapReduce. The ecosystem grew to include YARN, Hive, and HBase. Apache Spark arrived later as an in‑memory engine that handles batch and streaming workloads with a friendlier API and faster processing. It can run on Hadoop clusters or on its own, making it a versatile workhorse for many teams. ...