Introduction to the Hadoop Ecosystem

The Hadoop ecosystem is a framework that helps in solving big data problems. The core component of the Hadoop ecosystem is a Hadoop distributed file system (HDFS). HDFS is a distributed file system that has the capability to store a large stack of data sets. With the help of shell-commands, HADOOP interactive with HDFS. Hadoop Breaks up unstructured data and distributes it to different sections for Data Analysis. The ecosystem provides many components and technologies that have the capability to solve complex business tasks. In addition, the ecosystem includes open-source projects and examples.

Overview of Hadoop Ecosystem

As we all know, the Internet plays a vital role in the electronic industry and the amount of data generated through nodes is very vast and leads to the data revolution. Data is huge in volume, so there is a need for a platform that takes care of it. The Hadoop Architecture minimizes workforce and helps in job Scheduling. To process this data, we need a strong computation power to tackle it. As data grows drastically, it requires large volumes of memory and faster speed to process terabytes of data, to meet challenges distributed systems, which uses multiple computers to synchronize the data. To tackle this processing system, it is mandatory to discover software platform to handle data-related issues. There evolves Hadoop to solve big data problems.

Components of Hadoop Ecosystem

As we have seen an overview of Hadoop Ecosystem and well-known open-source examples, we will now discuss the list of Hadoop Components individually and their specific roles in the big data processing. The components of Hadoop ecosystems are:

1. HDFS

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 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 reading and writing operations. They work according to the instructions of the Name Node. The data nodes are hardware in the distributed system.

2. HBASE

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 written in java language. Most companies use them for its features like supporting all types of data, high security, and 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 role. The regional server’s role would be a worker node and responsible for reading, writing data in the cache.

3. YARN

It’s an important component in the ecosystem and called an operating system in Hadoop which provides resource management and job scheduling task. 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 data center process, and allow multiple access engines.

4. Sqoop

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 a data.

5. Apache Spark

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. Many companies use them for their high processing speed and stream processing.

6. Apache Flume

It is a distributed service collecting a large amount of data from the source (webserver) and moves back to its origin and transferred to HDFS. The three components are Source, sink, and channel.

7. 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.

8. Apache Pig

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.

9. Hive

It is an open-source platform 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 Hive Query language. The user submits the hive queries with metadata which converts SQL into Map-reduce jobs and given to the Hadoop cluster, consisting of one master and many numbers slaves.

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10. Apache Drill

Apache Drill is an open-source SQL engine which process non-relational databases and File system. 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.

11. Apache Zookeeper

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.

12. Oozie

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.

Examples

Regarding map-reduce, we can see an example and use case. one such case is Skybox which uses Hadoop to analyze a huge volume of data. Hive can find simplicity on Facebook. Frequency of word count in a sentence using map-reduce. MAP performs by taking the count as input and perform functions such as Filtering and sorting, and the reduce () consolidates the result. Hive example on taking students from different states from student databases using various DML commands

Conclusion

This concludes a brief introductory note on Hadoop Ecosystem. Apache Hadoop has gained popularity due to its features like analyzing a stack of data, parallel processing, and 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.

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