Apache Hadoop is an open-source framework responsible for distributed storage and processes a huge amount of data sets too. If Hadoop was a home, then it would be a very comfortable place to live in. The framework has doors, wires, pipes, windows etc. Hadoop ecosystem provides the furnishing that converts the framework into a comfortable house for big data processing and reflects your specific needs too.
What is Apache Hadoop Ecosystem?
Apache Hadoop ecosystem comprises both open source projects and a complete range of data management tools or components. Some of the best-known examples of Hadoop ecosystem include Spark, Hive, HBase, YARN, MapReduce, Oozie, Sqoop, Pig, Zookeeper, HDFS etc. The objective of each of the Hadoop components is to extend its capabilities and make data processing easier.
The top-level Apache Hadoop ecosystem components are intended to manage Hadoop data flow and robust data processing. The more customized third-party solutions can also be developed within Hadoop ecosystem. In this blog, we will discuss on some of the most popular Hadoop ecosystem components and their functionalities.
List of Hadoop Ecosystem Components
HDFS – Hadoop Distributed File System
This is one of the largest Apache projects and primary storage system of Hadoop. It has the capability to store very large files running over the cluster of commodity hardware. It is based on the principle of storing a limited number of big data files instead of storing a huge number of small data files. This is a reliable platform even in the case of failure of any hardware. The application access is also maximized by running processes in parallel.
The two most common HDFS components are –
Hive – Data Query System
This is an open-source data warehouse used to query or analyze large datasets stored within Hadoop ecosystem. It is responsible for processing unstructured and semi-structured data in Hadoop. It can work along with HDFS components to increase the functionalities of Hadoop. It is based on HQL language that works similar to SQL and automatically translates queries into MapReduce jobs.
Pig – Data Query System
This is a high-level language used to execute queries for larger data sets that are stored within Hadoop. The component is using Pig Latin language that is very much similar to SQL. The objective of Pig is data loading, perform the necessary operations and arrange the final output in the required format. The main benefits of Pig platform are extensible, self-optimizing, and handling a different kind of data etc.
MapReduce – A data processing Layer
This is a data processing layer to process large structured and unstructured data in Hadoop. It has the capability to manage huge data files in parallel. This is based on the concept of breaking jobs into multiple independent tasks and process them one by one.
- Map: This is the initial phase where all complex logic code is defined. This is a data processing layer to process large structured and unstructured data in Hadoop.
- Reduce: Here, the jobs are broken down into small independent tasks and managed one by one. This is also popular with the name light-weight processing.
HBASE – Columnar Store
This is a No SQL database runs over the top of the Hadoop. This is a database that could store structured data in the table that could have millions of rows or million of columns. It also provides real-time access to read and write operations in HDFS.
HCatalog – Data Storage System
This is a table storage management layer at the top of Hadoop. This is a major component of Hive and enables users to store data in multiple formats. It also offers support for various Hadoop components for an easy read and write operations of data in the cluster. The major advantages of HCatalog are data cleaning, transparent data processing, prevents the overhead of data storage, enables notifications for data availability.
YARN – Yet Another source Navigator
As the name suggests, this component is suitable for resource management and taken as the operating system of Hadoop. It is responsible for managing workloads, monitoring, and security controls implementation. The component is responsible to deliver data governance tools across various Hadoop clusters. The applications of YARN include batch processing, or real-time streaming etc.
- Resource Manager
- Node Manager
This component is responsible to provide data serialization and data exchange facilities in Hadoop. With the help of serialization process, data is added to files in the form of messages. It also stores the definition of data in the form of a single message and file. Hence, it makes data easy to understand even if it is stored dynamically. It used container file for persistent storage of data. It is responsible for remote procedure calls and rich data structures too. This is compact, fast, and binary data format.
This is a data processing tool for large-scale projects. It is designed to manage thousands of nodes together and stores data in petabytes. It is also defined as the first SQL query engine based on the schema-free model. The major characteristics of Drill are – decentralization of data, flexibility, and dynamic schema designing.
- Decentralization of data,
- Flexibility, and
- Dynamic schema designing
This is an open source data management platform responsible to monitor, store, provisioning, and securing Hadoop data clusters. The data management gets simpler with the help of this component and operation controls.
The discussion doesn’t end here but the list of components is just the endless. We have covered the major Hadoop ecosystem components that are used frequently by the developers. Due to these components, there are multiple job roles available in the market.
A deep knowledge of these components allows understanding of different roles perfectly. You could join Hadoop training program to learn all components in detail and get hands-on expertise to make your selection easy and faster.