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Hadoop Ecosystem

Understanding the Hadoop Ecosystem​

The Apache Hadoop ecosystem encompasses various components of the Apache Hadoop software library, consisting of open-source projects and a wide range of complementary tools. Some well-known tools in the Hadoop ecosystem include _HDFS, Hive, Pig, YARN, MapReduce, Spark, HBase, Oozie, Sqoop, Zookeeper and more.

Here are the key Hadoop ecosystem components frequently used by developers:

Exploring HDFS​

The Hadoop Distributed File System (HDFS) is a major Apache project and the primary storage system for Hadoop. It utilizes a NameNode and DataNode architecture and is a distributed file system capable of storing large files across a cluster of commodity hardware.

Discovering Hive​

Hive is an ETL and Data warehousing tool for querying or analyzing large datasets stored within the Hadoop ecosystem. Its main functions are data summarization, querying, and analysis of unstructured and semi-structured data in Hadoop. Hive features an SQL-like interface called HQL, which works similarly to SQL and automatically translates queries into MapReduce jobs.

Introducing Apache Pig​

Apache Pig is a high-level scripting language for executing queries on large datasets within Hadoop. Its simple SQL-like scripting language, Pig Latin, aims to perform necessary operations and arrange the final output in the desired format.

Delving into MapReduce​

MapReduce is another data processing layer in Hadoop. It can process large structured and unstructured data and manage extremely large data files in parallel by dividing the job into a set of independent tasks (sub-jobs).

Unraveling YARN​

YARN, short for Yet Another Resource Negotiator, is a core component of open-source Apache Hadoop, suitable for resource management. It manages workloads, monitoring, and security controls implementation. YARN allocates system resources to various applications running in a Hadoop cluster and assigns tasks to each cluster node. YARN has two main components:

  • Resource Manager
  • Node Manager

A Superior Alternative to Hadoop: Apache Spark​

Apache Spark is a fast, in-memory data processing engine suitable for various applications. It can be deployed in several ways, supports Java, Python, Scala, and R programming languages, and offers SQL, streaming data, machine learning, and graph processing capabilities, which can be used together in an application.

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