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@vikrantrana
February 20, 2020

Hadoop - Need of The Hour

Our Big Data Hadoop expert master’s course lets you gain proficiency in Big Data Hadoop. You will work on real-world projects in Hadoop. Big data and Hadoop are technologies used to handle large amounts of data. Big data consist of structured and unstructured data that cannot be stored and processed by traditional storage techniques. Big data means a huge amount of data. Hadoop is a tool that is used to handle Big Data. Hadoop is a software framework for storing information and processing applications. It provides huge storage for any kind of data. Hadoop provides high processing power and the capability to handle and control virtually limitless concurrent tasks or jobs. The framework is written completely in Java, one of the most widely used programming languages. New age learners enroll in big data courses for in-depth knowledge.

Evolution of Hadoop: In 2006, Yahoo created Hadoop on GFS (Google File System) and started using it in 2007 on a 1000 node cluster. Later in January 2008 Hadoop, Yahoo announced Hadoop as an open-source project to apache software. Hadoop is not a database it is a software ecosystem that allows for massively parallel computing.

Hadoop Framework:

  • HDFS(storing)
  • YARN(processing)

HDFS:

Allows to process any kind of data across the cluster. HDFS (Hadoop distributed file system) is a single unit that can store a large amount of data. HDFS uses master-slave architecture. Name node is the master node and other nodes are slaves (data nodes).

YARN:

Yarn performs all processing tasks by allocating resources and scheduling jobs. Yarn also has two major components, the resource manager and node manager. Resource manager is a master node. Node manager is responsible for the execution of tasks on every single data node.

Uses of Hadoop :

  • Search
  • Log processing
  • Data warehouse
  • Video and Image processing

Apache Hadoop:

It is a framework that allows for the widespread processing of huge datasets. It is designed, to sum up from single servers to thousands of machines. At the application layer, the library itself detects and handles failures, delivering a highly-available service on top of the cluster of computers. HDFS and MapReduce are the two components of Apache Hadoop.

Hadoop-MapReduce

It is a processing technique for distributed computing based on Java. The algorithm consists of two important tasks namely map and reduction. Map converts one set of data into another set of data, where elements are broken into tuples (key and value pairs). Reduce takes the output from a map as an input and forms those tuples into a smaller set of tuples. As the name implies Reduce job is always performed after the Map task.

The MapReduce Algorithm:

  • Map stage: Processes the input data which is in the form of file or directory and stores it in the Hadoop file system. The input data is processed to the mapper function line by line. The mapper processes the data and creates a smaller set of data.
  • Reduce stage: It is two-step process of the shuffle stage and reduces stage. Reducer’s task is to process the data that comes from the mapper. After the process, the new set of output is stored in HDFS.

Is Hadoop and Big data the same?

Big data is a concept which handles a large number of data set. Hadoop is just a single framework. Hadoop is used for batch processing. In big data, a developer develops an application in MapReduce, spark, hive pig, etc., Hadoop admin is responsible for performing operational tasks, and admin uses Cloud era manager, Ambari, Apache CLI interface.

Hadoop tools for destructing Big data:

  • HDFS
  • Hbase
  • HIVE
  • Sqoop
  • Pig
  • Zookeeper
  • NoSQL
  • Mahout
  • Lucene
  • Avro
  • Oozie
  • GIS tools
  • Flume
  • Clouds
  • Spark
  • Ambary
  • Mapreduce
  • SQL on Hadoop
  • Impala
  • MongoDB

 Applications of Hadoop:

  • Used for storing and processing Big data
  • Hadoop MapReduce programming is used for faster storage and processing of data.

Real-time Industry applications of Hadoop:

  • Controlling traffic on streets
  • Streaming processing
  • Content management and archiving emails
  • Processing neuronal signals
  • Fraud detection and prevention

Advantages:

  • Hadoop is a highly scalable data storage platform.
  • It can store and process very large data sets across hundreds of inexpensive servers.
  • Scalable
  • Cost-effective
  • Fast
  • Resilient to failure
  • Flexible
@vikrantrana
February 20, 2020, 14:14
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