What you’ll learn

  • Importing Incremental data from RDBMS to HDFS and from RDBMS to Hive
  • Hive Partitioning, Bucketing and Indexing
  • Exporting Incremental Data from hive to RDBMS and from HDFS to RDBMS
  • Creating Hive Tables for Different file formats
  • Developing the Pig Latin Scripts in Pig
  • Scheduling the OOZIE Workflow using Coordinator
  • Scheduling the OOZIE Sub-Workflow using coordinator
  • Flume Integration with HDFS
  • Reading Data from HDFS to Spark 1.x
  • Reading and Loading data from Hive to spark 1.x using spark SQL


If you are looking for building the skills and mastering in Big Data concepts, Then this is the course for you.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. In this course, you will learn about the Hadoop components, Incremental Import and export Using SQOOP, Explore on databases in Hive with different data transformations. Illustration of Hive partitioning, bucketing and indexing. You will get to know about Apache Pig with its features and functions, Pig UDF’s, data sampling and debugging, working with Oozie workflow and sub-workflow, shell action, scheduling and monitoring coordinator, Flume with its features, building blocks of Flume, API access to Cloudera manager, Scala program with example, Spark Ecosystem and its Components, and Data units in spark.

What are you waiting for?

Hurry up!!!!!!

Link description