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What is the role of Big Data and Hadoop.?

Big Data refers to the huge amount of data stored on the company servers and can not be handled using traditional tools and technologies. Now the question is how big.? let me guide you, according to a survey some government projects are generating 20000 Peta Byte Data per day that is why Big Data technology came into existence and Hadoop is a solution to Big data that can process Big volume of data using the distributed processing system.

Big Data is a revolutionary field changing data processing methods moreover, this field will give a lot of job opportunity for the students.

How it work.?

Google invented the Big Data processing technique Map and Reduce, This technique work in a distributed computing environment where we create a cluster of computers that is hadoop, store data across multiple computers, and finally, at the time of data processing, we fetch data from these computers simultaneously.
Hadoop is Framework having varities of processing tools such as Apache Hive, Apache Pig, Sqoop, Flume and a distributed file system.

Scope of Big Data.

  1. Big Data Scope for companies

    Companies like Google, Yahoo, Facebook, Amazon are generating a huge volume of data and they can not store and process this data into a single server that is why there is a need for distributed file systems and distributed data processing techniques, these companies moving towards the big data processing systems like Hadoop provide the facility to manage big data and its processing using a couple of tools like hive, sqoop, and flume.

  2. Big Data Scope for Students

    Big Data Technologies is also providing a good scope for the students as there is a need for professionals who can work on the Hadoop ecosystem to manage distributed data moreover, there is a need for experts who can work on apache spark and Kafka for real-time data processing, Cloudera and IBM infosphere Biginsights are the two popular Hadoop echo system being used for big data processing.

  3. Big Data Scope in Data Analysis

    Data Analysis is the common part for every organization as after data loading and managing companies also find the hidden pattern in the dataset using data mining and data analytical techniques, professionals are working on Big Data Management and machine learning models to analyze a distributed data. some companies use the apache spark machine learning module for the same process.

  4. Big Data Scope for ETL Technologies

    Data volume is huge and traditional tools are not good for Big Data processing as they might take a big time, Hadoop provides a solution for ETL Operation using Apache Sqoop and Flume. Apache Spark Streaming module also helps to process real-time data for the organizations that is why most of the IT companies implementing Hadoop and Cloud-based solutions for big data processing.

Hadoop Distributed File System.

Hadoop Distributed File System provides the distributed file storage for the big data and it is used by applications that run at the top of the Hadoop cluster.it is a primary data storage system and an essential part of Hadoop, as it provides reliability and it is a fault-tolerant system, which is designed to run on commodity hardware. HDFS uses a master-slave architecture to load data into a cluster of computers.

How I can make career in Big Data technology.

  1. Learn Apache Hive

    The Apache hive is a part of Hadoop and it is an open-source data warehousing tool developed by Facebook for distributed data processing. It is developed at top of the Hadoop eco System and also provides SQL-like language called HiveQL being used for big data processing. The Apache Hive story begins in the year 2007 and Facebook was the first company to come up with Apache Hive. you should learn apache hive if you want to be a Hadoop developer.

  2. Learn Apache Pig

    Apache pig is configured at the top of the Hadoop ecosystem and developed by Yahoo. It was developed with the goal to analyze and process large datasets without using Java codes. The pig was developed especially for non-programmers. Pig uses a simple scripting language to analyze data. Pig is a scripting platform that runs on Hadoop clusters, designed to process and analyze large datasets.

  3. Learn Apache Sqoop and Flume

    When Big Data tools such as MapReduce, Hive, zookeeper, and Pig came into existence, they required a tool so that they can interact with the relational database systems for importing and exporting the Big Data. Sqoop is a part of the Hadoop ecosystem that provides interaction between the relational database server and Hadoop’s HDFS and in simple words it is the latest ETL tool for big data loading. you must learn sqoop and real-time data loading techniques of hadoop..

  4. Learn Apache Spark

    Spark started as a project in 2009. In 2010 it was an open-source project under the BSD license. Apache Spark provides in-memory data processing engine with elegant development. Apache Spark is an in-memory big data processing engine to store and process data in real-time across various clusters of computers and runs 100 times faster than Apache Hadoop.