Hadoop Eco System


Hadoop Eco System

The Hadoop ecosystem is a collection of open-source software tools, frameworks, and libraries that work together to facilitate the processing, storage, and analysis of large volumes of data. These components are built around the core Hadoop framework and are designed to address various aspects of big data processing and analytics. Here are some key components and technologies in the Hadoop ecosystem:

  1. Hadoop Distributed File System (HDFS):

    • HDFS is the primary storage system in the Hadoop ecosystem. It provides a distributed and fault-tolerant file system for storing large datasets across multiple nodes in a Hadoop cluster.
  2. MapReduce:

    • MapReduce is a programming model and processing framework for parallel and distributed data processing. It is one of the original components of Hadoop and is used for batch processing tasks.
  3. YARN (Yet Another Resource Negotiator):

    • YARN is a resource management and job scheduling component in Hadoop. It allows multiple data processing frameworks (such as MapReduce, Spark, and Tez) to share cluster resources efficiently.
  4. Apache Spark:

    • Spark is a fast, in-memory data processing framework that can be used for batch processing, real-time stream processing, machine learning, and graph processing. It is known for its speed and ease of use.
  5. Hive:

    • Hive is a data warehousing and SQL-like query language system for Hadoop. It enables users to query and analyze data stored in HDFS using HiveQL, which is similar to SQL.
  6. Pig:

    • Pig is a high-level platform for creating MapReduce programs used for data transformation and analysis. It provides a scripting language called Pig Latin for defining data processing tasks.
  7. HBase:

    • HBase is a distributed, NoSQL database that provides real-time, random read and write access to large datasets. It is designed to handle massive amounts of data with low-latency access.
  8. Apache Mahout:

    • Mahout is a machine learning library for Hadoop. It provides scalable implementations of various machine learning algorithms for tasks such as clustering, recommendation, and classification.
  9. ZooKeeper:

    • ZooKeeper is a distributed coordination service used for managing and synchronizing distributed systems. It provides primitives for building distributed applications and ensuring high availability.
  10. Oozie:

    • Oozie is a workflow scheduling and coordination system for Hadoop jobs. It allows users to define and manage workflows that include various Hadoop ecosystem components.
  11. Sqoop:

    • Sqoop is a tool for efficiently transferring data between Hadoop and relational databases. It simplifies the process of importing and exporting data to and from Hadoop.
  12. Flume and Kafka:

    • Flume and Kafka are data ingestion and streaming platforms that allow organizations to collect, process, and ingest data from various sources into Hadoop.
  13. Ambari and Cloudera Manager:

    • Ambari and Cloudera Manager are management and monitoring tools that simplify the deployment, configuration, and management of Hadoop clusters.
  14. Zeppelin and Hue:

    • Zeppelin and Hue are web-based user interfaces for data exploration, visualization, and analysis, making it easier for non-technical users to work with Hadoop.
  15. Flink, Storm, and Samza:

    • These are additional stream processing frameworks that can be used for real-time data processing and analytics in the Hadoop ecosystem.

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