Introduction to Hadoop Ecosystem
The Hadoop ecosystem is a framework which helps in solving big data problems. The core component of the Hadoop ecosystem is a Hadoop distributed file system (HDFS). HDFS is the 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 eco-system provides many components and technologies have the capability to solve business complex tasks. The ecosystem includes open-source projects and examples
Overview of Hadoop Ecosystem
As we all know that 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 manpower 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 system are used 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 the Hadoop Ecosystem
As we have seen an overview of Hadoop Ecosystem and well-known open-source examples, now we are going to discuss deeply the list of Hadoop Components individually and their specific roles in the big data processing. The components of Hadoop ecosystems are:
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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 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.
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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, 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 role. The role of the regional server would be a worker node and responsible for reading, writing data in the cache.
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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 in data center process and allows multiple access engines.
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Sqoop:
It is a tool that helps in data transfer between HDFS and MySQL and gives hand-on to import and export of data, they have a connector for fetching and connecting a data.
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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. They are used by many companies for their high processing speed and stream processing.
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Apache Flume:
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.
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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.
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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.
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Hive:
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 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.
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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.
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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.
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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 of Hadoop Ecosystem
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 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.
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