<ul><li>Apache Hive enables querying HDFS data using a SQL-like language without complex MapReduce processes.</li><li>Hive was developed by Facebook for processing structured and semi-structured data, useful for batch analyses.</li><li>Metastore in Hive stores metadata like table definitions and column names to manage large datasets.</li><li>HiveQL queries are converted by the execution engine into tasks for processing by Hadoop.</li><li>Hive performance can be optimized using partitioning for faster searching and organizing data into buckets for efficient joins.</li><li>Apache Pig facilitates parallel processing of data in Hadoop using Pig Latin language for ETL of semi-structured data.</li><li>HBase is a NoSQL database in Hadoop that stores data in a column-oriented manner for efficient querying.</li><li>Amazon EMR offers managed big data service with support for Hadoop, Spark, and other frameworks in the cloud.</li><li>Apache Presto allows real-time distributed SQL queries in large systems without schema definition.</li><li>Apache Flink is designed for distributed stream processing in real-time with low latency.</li></ul>

Mastering Hadoop, Part 3: Hadoop Ecosystem: Get the most out of your cluster

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