Apache Hadoop Ecosystem

Updated: 01/08/2022 by Computer Hope

A group of open-source software initiatives and instruments known as the Hadoop ecosystem support the Apache Hadoop framework, which is used for distributed data processing and storing. Some of the most popular and well-known tools of the Hadoop ecosystem include HDFS, Hive, Pig, YARN, MapReduce, Spark, HBase, Oozie, Sqoop, Zookeeper, etc.

Here are the major Hadoop ecosystem components that are used frequently by developers :


A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.
The Apache Ambari project that is used to simplify the management of Apache Hadoop clusters using a web UI. It also integrates with other existing applications using Ambari REST APIs


A data serialization system. Avro is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.
Avro has a schema-based system. A language-independent schema is associated with its read and write operations. Avro serializes the data which has a built-in schema. Avro serializes the data into a compact binary format, which can be deserialized by any application


A scalable multi-master database with no single points of failure.


A data collection system for managing large distributed systems.


A scalable, distributed database that supports structured data storage for large tables.


A data warehouse infrastructure that provides data summarization and ad hoc querying.


A Scalable machine learning and data mining library.
It implements popular machine learning techniques such as:

    Recommendation /Collaborative Filtering

  • Item‐based Collaborative Filtering
  • Matrix Factorization with Alternating Least Squares
  • Matrix Factorization with Alternating Least Squares on Implicit Feedback
  • Classification

  • Naive Bayes
  • Complementary Naive Bayes
  • Random Forest
  • Clustering

  • Canopy Clustering
  • k‐Means Clustering
  • Fuzzy k‐Means
  • Streaming k‐Means
  • Spectral Clustering


A high-level data-flow language and execution framework for parallel computation.


A fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of applications, including ETL, machine learning, stream processing, and graph computation.


A unified AI platform which allows engineers and data scientists to run Machine Learning and Deep Learning workload in distributed cluster.


A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hive™, Pig™ and other frameworks in the Hadoop ecosystem, and also by other commercial software (e.g. ETL tools), to replace Hadoop™ MapReduce as the underlying execution engine.


A high-performance coordination service for distributed applications.
Some of the prime features of Apache ZooKeeper are:
Reliable System: This system is very reliable as it keeps working even if a node fails.
Simple Architecture: The architecture of ZooKeeper is quite simple as there is a shared hierarchical namespace which helps coordinating the processes.
Fast Processing: ZooKeeper is especially fast in "read-dominant" workloads (i.e. workloads in which reads are much more common than writes).
Scalable: The performance of ZooKeeper can be improved by adding nodes.

Apache Flume

Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data source. Review this Flume use case to learn how Mozilla collects and Analyse the Logs using Flume and Hive.
Flume is a framework for populating Hadoop with data. Agents are populated throughout ones IT infrastructure – inside web servers, application servers and mobile devices, for example – to collect data and integrate it into Hadoop.

Conclusion :

Hadoop is an open source software developed by the Apache Software Foundation (ASF). You can download Hadoop directly from the project website at http://hadoop.apache.org. Cloudera is a company that provides support, consulting, and management tools for Hadoop. Cloudera also has a distribution of software called Cloudera’s Distribution Including Apache Hadoop (CDH).
Here in this article contain a few examples of the many components within the Hadoop ecosystem. The ecosystem is diverse, and different tools are suited for different use cases and requirements in the big data landscape.