DataLake

Big Data Tools: Hadoop, Spark, and Beyond Big data tools help teams turn large amounts of information into useful answers. They cover storage, processing, and fast queries. The field grows quickly, so a simple choice today may change later. A clear plan helps you stay useful as data needs evolve. Hadoop gave a reliable way to store huge files and run many jobs at once. It uses HDFS, a scalable file system, and a processing layer such as MapReduce or Tez. It also has YARN for resource management. Many companies use Hadoop for batch workloads that run overnight or on weekends. ...

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. ...