Working with Hadoop (Dec 2022)

Working with Hadoop (Dec 2022), available at $44.99, has an average rating of 4.5, with 24 lectures, 8 quizzes, based on 1 reviews, and has 42 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Base and Data Warehouse Developers or Big Data Developers and Architects or Data Scientists and Analysts or Any technical personnel who are interested learning and Exploring the features of Big Data and Tools It is particularly useful for Data Base and Data Warehouse Developers or Big Data Developers and Architects or Data Scientists and Analysts or Any technical personnel who are interested learning and Exploring the features of Big Data and Tools.

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Summary

Title: Working with Hadoop (Dec 2022)

Price: $44.99

Average Rating: 4.5

Number of Lectures: 24

Number of Quizzes: 8

Number of Published Lectures: 24

Number of Published Quizzes: 8

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: ₹3,299

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

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Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Lesson 1: Working with SQOOP

Lecture 1: Lesson 1: Working with SQOOP

Lecture 2: Practice 1-1: Import Incremental Data from RDBMS to HDFS and from RDBMS to Hive

Lecture 3: Practice 1-2: Export Incremental Data from HIVE to RDBMS and from HDFS to RDBMS

Chapter 3: Hive Concepts

Lecture 1: Lesson 2: Working with HIVE

Lecture 2: Practice 2-1: Working with HQL Scripts in HIVE

Chapter 4: Data Storage and Performance in HIVE

Lecture 1: Lesson 3: Data Storage and Performance in HIVE

Lecture 2: Practice 3-1: Hive Partitioning

Lecture 3: Practice 3-2: Hive Bucketing

Lecture 4: Practice 3-3: Hive Indexing

Lecture 5: Practice 3-4: Creating Hive Tables for Different File Formats

Chapter 5: Working with Pig

Lecture 1: Lesson 4: Working with Pig – Troubleshooting and Optimization

Lecture 2: Practice 4-1: Developing the Pig Latin Scripts in Pig

Chapter 6: Oozie Concepts

Lecture 1: Lesson 5: Working with Oozie

Lecture 2: Practice 5-1: Scheduling the OOZIE Workflow using Coordinator

Lecture 3: Practice 5-2: Scheduling the OOZIE Sub-Workflow using coordinator

Chapter 7: Flume Integration with HDFS

Lecture 1: Lesson 6: Integration of Flume with HDFS

Lecture 2: Practice 6-1: Flume Integration with HDFS

Chapter 8: Cloudera Administration

Lecture 1: Lesson 7: Cloudera Administration

Lecture 2: Practice 7-1: Creating the Dashboard in Cloudera Manager

Lecture 3: Practice 7-2: Verifying the Logs and status of Job in Cloudera Manager

Chapter 9: Scala and Apache Spark

Lecture 1: Lesson 8: Introduction to Scala and Apache Spark

Lecture 2: Practice 8-1: Read Data from HDFS to Spark 1.x

Lecture 3: Practice 8-2: Read and Load data from Hive to spark 1.x using spark SQL

Instructors

Proton Expert Systems & Solutions
Proton Expert Systems & Solutions

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