In the era of big data, organizations are constantly seeking efficient ways to manage, process, and analyze large volumes of structured and unstructured data. Enter Apache Hadoop , an open-source framework that provides scalable, reliable, and distributed computing solutions. With its rich ecosystem of tools, Hadoop has become a cornerstone for big data projects. Let’s explore the various components and layers of the Hadoop ecosystem and how they work together to deliver insights. Data Processing Layer 🛠️🔍 The heart of Hadoop lies in its data processing capabilities, powered by several essential tools: Apache Pig 🐷 : Allows Hadoop users to write complex MapReduce transformations using a scripting language called Pig Latin , which translates to MapReduce and executes efficiently on large datasets. Apache Hive 🐝 : Provides a SQL-like query language called HiveQL for summarizing, querying, and analyzing data stored in Hadoop’s HDFS or compatible systems like Amazon S3. It makes inter...