WHAT IS BIGDATA AND HADOOP ECOSYSTEM

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Data is a collection of facts and stats for analysis. The advent along with the popularity of the internet has led to the collection of tonnes and tonnes of data every day. This data that the internet collects is not the organized kind. This data is heavily unorganized and unformatted. This unformatted data keeps on increasing with no regard to any aspects. This data is now given the term Big Data.- https://www.madridsoftwaretrainings.com/hadoop.php

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WHAT IS BIGDATA AND HADOOP ECOSYSTEM

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Data is a collection of facts and stats for analysis. The advent along with the popularity of the internet has led to the collection of tonnes and tonnes of data every day. This data that the internet collects is not the organized kind. This data is heavily unorganized and unformatted. This unformatted data keeps on increasing with no regard to any aspects. This data is now given the term Big Data. Big Data is huge amounts of data received daily from websites social media and email. This information is complex and in unstructured formats. Big data associates itself with five concepts: Volume quantity of data Variety nature of data Velocity generation and processing speed of data veracity data quality of captured data and value. Big data grows rapidly through cheap and numerous IoT devices such as mobile devices aerial software logs microphones RFID radio- frequency identification readers wireless sensor networks and cameras. To be able to extract all this information business groups need an application that can analyze this data and convert it into a readable understandable and structured batch of information.

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Hadoop is a Java-based programming platform that processes large sets of data in an assigned computing workspace. Hadoop allows running of applications on systems that are working on thousands of nodes involving thousands of terabytes. It supports the distributed file system that allows rapid data transfer rates and allows uninterrupted system execution in case of a node failure. This reduces the risk of cataclysmic system failure even if a large number of nodes stop working. Hadoop works on Google’s MapReduce which breaks down an application into a large number of smaller parts. Also Read: Relation between Big data Hadoop and R language

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The Hadoop ecosystem is built of the following: Hadoop Common: This contains java set of files that the Hadoop modules use. These libraries provide OS-level abstraction and contain the most essential java documents and boots Hadoop. Hadoop Distributed File System: This file system allows access to the data hence allowing high bandwidth across the cluster. The main components of HDFS are NameNode DataNode and Secondary NameNode. The NameNode maintains the files and manages blocks present on DataNodes. The DataNode are responsible for serving read/write requests for clients. The secondary NameNodes are to perform checkpoints. This helps start a NameNode in case of a failure. Hadoop YARN: this is responsible for job scheduling and resource management.

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Hadoop MapReduce: this element of the ecosystem is used to process large datasets. The MapReduce has two common functions the map task and the reduce task. The map task is required to convert and divide data into parts. The reduce task forms an output that is the solution to our problem. Also Read: BIG DATA VS HADOOP: Who Will Win Hadoop has picked up its notoriety because of its capacity for putting away dissecting and getting to the expansive measure of information rapidly and cost successfully through groups of production equipment. It won’t be right that we say that Apache Hadoop is really an accumulation of a few parts and not only a solitary item. Hadoop is strongly recommended because it is easy to integrate with any component. It is also capable of storing analyzing and accessing large chunks of data in limited time. Thus Hadoop is highly user convenient.

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Learn Big Data Hadoop by taking course from Big Data Hadoop Institute in Delhi. Madrid Software offers 3 months course for Big data Hadoop.

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