Big Data

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Big Data

Data are now woven into every sector and function in the global economy and like other essential factors of production such as hard assets and human capital, much of modern economic activity simply could not take place without them. The use of Big Data – large pools of data that can be brought together and analyzed to discern patterns and make better decisions – will become the basis of competition and growth for individual firms, enhancing productivity and creating significant value for the world economy by reducing waste and increasing the quality of products and services. Until now, the torrent of data flooding our world has been a phenomenon that probably only excited a few data geeks. But we are now at an inflection point. According to research from the McKinsey Global Institute (MGI) and McKinsey & Company’s Business Technology Office, the sheer volume of data generated, stored, and mined for insights has become economically relevant to businesses, government and consumers.

Technologies


The following are the various technologies related to Big Data

  • Hadoop
  • Databases and Data Warehouses
  • Map Reduce
  • Pig
  • Hive
  • Oozie
  • NoSQL
  • Apache Spark

BIGDATA AND ANALYTICS JOB DESIGNATIONS

  • Data Scientist
  • Data Engineer
  • Big Data Engineer
  • Machine Learning Scientist
  • Business Analytics Specialist
  • Data Visualization Developer
  • Business Intelligence (BI) Engineer
  • BI Solutions Architect
  • BI Specialist
  • Analytics Manager
  • Machine Learning Engineer
  • Hadoop Developer
  • Hadoop Architect
  • Hadoop Tester
  • Hadoop Administrator
  • Data Scientist

  • Course Structure

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    • Introduction to Bigdata and Hadoop Ecosystem
    • HDFS and Hadoop Architecture
    • MapReduce and Sqoop
    • Basics of Impala and Hive
    • Working with Impala and Hive
    • Type of Data Formats
    • Advance HIVE concept and Data File Partitioning
    • Apache Flume and HBase
    • Apache Pig
    • Basics of Apache Spark
    • RDDs in Spark
    • Implementation of Spark Applications
    • Spark Parallel Processing
    • Spark SQL

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