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
Requirements
Hadoop Fundamentals (one of our courses in Udemy)
Basic Python Programming Knowledge
Working Knowledge on Data Base Systems and Data Warehouses
Basic Java Programming Knowledge
Basic Linux Commands
Description
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!!!!!!
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Lesson 1: Working with SQOOP
Lecture 2 Lesson 1: Working with SQOOP
Lecture 3 Practice 1-1: Import Incremental Data from RDBMS to HDFS and from RDBMS to Hive
Lecture 4 Practice 1-2: Export Incremental Data from HIVE to RDBMS and from HDFS to RDBMS
Section 3: Hive Concepts
Lecture 5 Lesson 2: Working with HIVE
Lecture 6 Practice 2-1: Working with HQL Scripts in HIVE
Section 4: Data Storage and Performance in HIVE
Lecture 7 Lesson 3: Data Storage and Performance in HIVE
Lecture 8 Practice 3-1: Hive Partitioning
Lecture 9 Practice 3-2: Hive Bucketing
Lecture 10 Practice 3-3: Hive Indexing
Lecture 11 Practice 3-4: Creating Hive Tables for Different File Formats
Section 5: Working with Pig
Lecture 12 Lesson 4: Working with Pig - Troubleshooting and Optimization
Lecture 13 Practice 4-1: Developing the Pig Latin Scripts in Pig
Section 6: Oozie Concepts
Lecture 14 Lesson 5: Working with Oozie
Lecture 15 Practice 5-1: Scheduling the OOZIE Workflow using Coordinator
Lecture 16 Practice 5-2: Scheduling the OOZIE Sub-Workflow using coordinator
Section 7: Flume Integration with HDFS
Lecture 17 Lesson 6: Integration of Flume with HDFS
Lecture 18 Practice 6-1: Flume Integration with HDFS
Section 8: Cloudera Administration
Lecture 19 Lesson 7: Cloudera Administration
Lecture 20 Practice 7-1: Creating the Dashboard in Cloudera Manager
Lecture 21 Practice 7-2: Verifying the Logs and status of Job in Cloudera Manager
Section 9: Scala and Apache Spark
Lecture 22 Lesson 8: Introduction to Scala and Apache Spark
Lecture 23 Practice 8-1: Read Data from HDFS to Spark 1.x
Lecture 24 Practice 8-2: Read and Load data from Hive to spark 1.x using spark SQL
Data Base and Data Warehouse Developers,Big Data Developers and Architects,Data Scientists and Analysts,Any technical personnel who are interested learning and Exploring the features of Big Data and Tools


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