An Introduction to Hadoop and its ecosystem

hadoop ecosystem

Big data analysis is the future of technology and analytical research. Big data analysis deals with large data set which helps in determining patterns and trends in business. Imagine how useful it would be for finance, marketing and other kinds of research. Now, since it deals with large amounts of data, it gets a lot more complicated. If you are looking to opt for a detailed course of data analytics, you must first understand the ecosystem of Hadoop.

Not every software is capable of handling such large data in one go. However, Apache Hadoop, an open-source framework, has made its place in the tech world because it allows efficient handling of big data. The Hadoop framework uses clusters and is made into several modules creating a large ecosystem of technologies. The Hadoop Ecosystem is a suite providing a variety of services to tackle big data problems. 

Hadoop Ecosystem

hadoop and its ecosystem

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While there are many solutions and tools in the Hadoop ecosystem, these are the four major ones: HDFS, MapReduce, YARN and Hadoop Common. These tools work together and help in the absorption, analysis, storage, and maintenance of data. However, there are many other components that work in tandem with building up the entire Hadoop ecosystem. As you can see in the diagram above, each and every component of the Hadoop ecosystem has its own function. For example, the HDFS and MapReduce are responsible for distributed capabilities, i.e. distributed storage and distributed processing respectively. They are:

  1. HDFS

This is the primary component of the ecosystem. It stores large data sets of unstructured and structured data and maintains the metadata in the log file form. The core components used here are the Name Node and the Data Node. The data node is the commodity hardware present in the distributed environment and helps in the storage of data. The Name Node is the prime node and stores the metadata. It requires fewer resources than data nodes. HDFS works at the heart of the system.

  1. YARN

YARN or Yet Another Resource Negotiator helps with the management of resources across the clusters. It is responsible for resource allocation and scheduling. The main components of YARN are Resource Manager, Nodes Manager and Application Manager. Resource manager helps in the allocation of resources for the applications working in the system. Nodes Manager helps in the allocation of other resources like CPU memory, bandwidth, etc. The Application Manager acts as an interface between the two and negotiates the resource requirements.

  1. MapReduce
hadoop ecosystem

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MapReduce combines the work of parallel and distributed algorithms to convert big data sets into manageable ones. It has two functions Map () and Reduce (). Map () sorts and filters the data and therefore, organize them into groups. Reduce () takes the Map() output and summarizes them into smaller sets of tuples.

  1. PIG

Developed by Yahoo, PIG helps to structure the data flow and thus, aids in the processing and analysis of large data sets. It helps in optimizing the processing of the entire set by executing the commands in the background. After the processing, PIG stores the acquired result in HDFS.

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  1. HIVE

Combining both SQL methodology and interface, HIVE helps to write and read large sets of data. It allows both batch processing and real-time processing, therefore being highly scalable. Plus, it supports SQL data types making query processing simpler. 

  1. Mahout

Machine learning is a thing of the future and many programming languages are trying to integrate it in them. For example, Python has many libraries which help in machine learning. Mahout helps to integrate Machine Learnability with Hadoop. It gives you functions like clustering, classification, and collaborative filtering. It provides you with various libraries as well.

  1. Apache Spark
hadoop ecosystem

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If you want to engage in real-time processing, then Apache Spark is the platform that can help you. It handles a number of process-consumptive tasks like iterative and interactive real-time processing, graph conversions, batch processing, etc. 

  1. Apache HBase

Apache HBase is a NoSQL database. Hence, it can handle any kind of data and provides the capability of Google Big Table. Thus, it makes working on Big Data sets efficient and easy. HBase helps in storing a limited quantity of data and hence, initiates fast responses when you want to retrieve something small from huge databases.

  1. Solr, Lucene

These two services are used for data management. Solr and Lucene help in searching and in the indexing of certain java libraries. 

  1. Zookeeper

Lack of both coordination and synchronization could result in inconsistency within the Hadoop ecosystem. Zooker helps in synchronization, grouping, and maintaining inter-component based communication to reduce inconsistency.

  1. Oozie

Oozie is a scheduler which helps to bind and schedule jobs as a singular unit. There are two kinds of work that Oozie does – Oozie workflow and Oozie coordinator jobs. Oozie workflow helps to execute jobs sequentially while Oozie coordinator helps to perform jobs when an external stimulus triggers it.

Get yourself acquainted with the Hadoop ecosystem and you can tackle big data analytics easily. For the much-needed direction that is needed to excel in data science, you can try the course on data science by Coding Ninjas.  Best of luck.