Last updated on 25th Sep 2020, Artciles, Blog
Hadoop is a framework which deals with Big Data but unlike any other framework it’s not a simple framework, it has its own family for processing different things which is tied up in one umbrella called the Hadoop Ecosystem.
The Hadoop Ecosystem is neither a programming language nor a service; it is a platform or framework which solves big data problems. You can consider it as a suite that encompasses a number of services (ingesting, storing, analyzing, and maintaining) inside it
Subscribe For Free Demo[contact-form-7 404 "Not Found"]
Components of Hadoop Ecosystem
As we have seen an overview of Hadoop Ecosystem and well-known open-source examples, now we are going to discuss the list of Hadoop Components individually and their specific roles in big data processing.
The components of Hadoop ecosystems are:
- 5.Apache Flume
- 6.Hadoop Map Reduce
- 7.Apache Pig
- 9.Apache Drill
- 10.Apache Zookeeper
Hadoop Distributed File System is the backbone of Hadoop which runs on java language and stores data in Hadoop applications. They act as a command interface to interact with Hadoop. the two components of HDFS – Data node, Name Node. Name node the main node manages file systems and operates all data nodes and maintains records of metadata updating. In case of deletion of data, they automatically record it in Edit Log. Data Node (Slave Node) requires vast storage space due to the performance of reading and write operations. They work according to the instructions of the Name Node. The data nodes are hardware in the distributed system.
It is an open-source framework storing all types of data and doesn’t support the SQL database. They run on top of HDFS and are written in java language. Most companies use them for its features like supporting all types of data, high security, use of HBase tables. They play a vital role in analytical processing. The two major components of HBase are HBase master, Regional Server. The HBase master is responsible for load balancing in a Hadoop cluster and controls the failover. They are responsible for performing administration roles. The role of the regional server would be a worker node and responsible for reading, writing data in the cache.
It’s an important component in the ecosystem and called an operating system in Hadoop which provides resource management and job scheduling tasks. The components are Resource and Node manager, Application manager and container. They also act as guards across Hadoop clusters. They help in the dynamic allocation of cluster resources, increase in the data center process and allow multiple access engines.
It is a tool that helps in data transfer between HDFS and MySQL and gives hand-on to
import and export data, they have a connector for fetching and connecting data.
It is an open-source cluster computing framework for data analytics and an essential data processing engine. It is written in Scala and comes with packaged standard libraries. They are used by many companies for their high processing speed and stream processing.
It is a distributed service collecting a large amount of data from the source (web server) and moves back to its origin and transferred to HDFS. The three components are Source, sink, and channel.
Hadoop Map Reduce
It is responsible for data processing and acts as a core component of Hadoop. Map Reduce is a processing engine that does parallel processing in multiple systems of the same cluster. This technique is based on the divide and conquers method and it is written in java programming. Due to parallel processing, it helps in the speedy process to avoid congestion traffic and efficiently improves data processing.
Data Manipulation of Hadoop is performed by Apache Pig and uses Pig Latin Language. It helps in the reuse of code and easy to read and write code.
It is an open-source Platform software for performing data warehousing concepts, it manages to query large data sets stored in HDFS. It is built on top of the Hadoop Ecosystem. The language used by Hive is the Hive Query language. The user submits the hive queries with metadata which converts SQL into Map-reduce jobs and given to the Hadoop cluster which consists of one master and many numbers of slaves.
Apache Drill is an open-source SQL engine which processes non-relational databases and File systems. They are designed to support Semi-structured databases found in Cloud storage. They have good Memory management capabilities to maintain garbage collection. The added features include Columnar representation and using distributed joins.
It is an API that helps in distributed Coordination. Here a node called Znode is created by an application in the Hadoop cluster. They do services like Synchronization, Configuration. It sorts out the time-consuming coordination in the Hadoop Ecosystem.
Oozie is a java web application that maintains many workflows in a Hadoop cluster. Having Web service APIs controls over a job is done anywhere. It is popular for handling Multiple jobs effectively.
This concludes a brief introductory note on Hadoop Ecosystem. Apache Hadoop has gained popularity due to its features like analyzing stack of data, parallel processing and helps in Fault Tolerance. The core components of Ecosystems involve Hadoop common, HDFS, Map-reduce and Yarn. To build an effective solution. It is necessary to learn a set of Components, each component does their unique job as they are the Hadoop Functionality.
Are you looking training with Right Jobs?Contact Us
- Hadoop Tutorial
- Hadoop Interview Questions and Answers
- How to Become a Hadoop Developer?
- Essential Concepts of Big Data and Hadoop
- What Are the Skills Needed to Learn Hadoop?
- What is Dimension Reduction? | Know the techniques
- Difference between Data Lake vs Data Warehouse: A Complete Guide For Beginners with Best Practices
- What is Dimension Reduction? | Know the techniques
- What does the Yield keyword do and How to use Yield in python ? [ OverView ]
- Agile Sprint Planning | Everything You Need to Know