Updated June 5, 2023
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 can store a large stack of data sets. With the help of shell commands, HADOOP is 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 can solve complex business tasks. In addition, the ecosystem includes open-source projects and examples.
Overview of the 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 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 the workforce and helps in job Scheduling. To process this data, we need 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 in distributed systems, which use multiple computers to synchronize the data. To tackle this processing system, it is mandatory to discover a software platform to handle data-related issues. There evolved Hadoop to solve big data problems.
Components of the Hadoop Ecosystem
As we have seen an overview of the Hadoop Ecosystem and well-known open-source examples, we will 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. The name node manages file systems, operates all data nodes, and maintains records of metadata updating. In case of deletion of data, they automatically record it in Edit Log. The 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 data types and doesn’t support the SQL database. They run on top of HDFS and are written in java language. Most companies use them for features like supporting all data types, high security, and HBase tables. They play a vital role in analytical processing. The two major components of HBase are the HBase master and Regional Server. The HBase master controls the failover and load balancing in a Hadoop cluster. They are responsible for performing administration roles. The regional server’s role would be a worker node responsible for reading and writing data in the cache.
3. YARN
It’s an essential component in the ecosystem and is 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 processes, and allow multiple access engines.
4. Sqoop
It is a tool that helps in data transfer between HDFS and MySQL and gives hands-on to import and exporting data; they have a connector for fetching and connecting 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), moving back to its origin, and transferring it to HDFS. The three components are the source, sink, and channel.
7. Hadoop Map Reduce
It is responsible for data processing and is 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 conquer method, written in java programming. Parallel processing helps in the speedy process to avoid congestion traffic and efficiently improves data processing.
8. Apache Pig
Apache Pig performs Data Manipulation of Hadoop and uses Pig Latin Language. It helps reuse code and is 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. On top of the Hadoop Ecosystem, it is constructed. Hive Query Language is the language that Hive employs. The user submits the hive queries with metadata which converts SQL into Map-reduce jobs and is given to the Hadoop cluster, consisting of one master and many numbers slaves.
10. Apache Drill
Apache Drill is an open-source SQL engine that 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.
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 API controls over a job is done anywhere. It is famous 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 counts in a sentence using map-reduce. MAP performs by taking the count as input and performing functions such as Filtering and sorting; the reduce () consolidates the result. Hive example on bringing 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 its unique job as they are the Hadoop Functionality.
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