Perform Data Analytics using Pig, Hive and Yarn

This web-based training course on Perform Data Analytics using Pig, Hive and Yarn functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.

Hadoop is currently the most popular open source software framework which is being used for data processing at all levels with capabilities of being scalable, reliable and distributed. Originally developed by Google, Hadoop has grown over the years to include a number of technologies and segments inside its ecosystem to support its working and abilities. Hadoop Yarn, Pig and Hive are three of the most important elements of the Hadoop ecosystem and help in providing essential data sourcing and manipulation capabilities. Hadoop Yarn is essentially a programming model which is used for processing and creating huge data sets and data packets. Being the successor of MapReduce, Yarn provides a number of benefits that MapReduce did not such as better scalability and support for multiple MapReduce APIs in a single cluster. Apache Hive, on the other hand, is a data warehouse management and analysis program built for Hadoop. Using SQL like scripting language known as HiveQL, Apache Hive can transform the queries for MapReduce, Apache Tez and Spark jobs. Apache Pig is another big data tool which is used for analyzing large sets of data and uses a high level scripting language known as Pig Latin. With Pig Latin, manual coding can be easily automated and thus provide flexible and speedy database management.

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Course Details

In this Data Analytics with Pig, Hive and yarn online training course, the trainees will be put through a rigorous coursework which includes not just the theoretical aspects of the technology but also the applicative and practical understanding of the domain. The Hadoop yarn framework and the use cases will be explained in detail along with its growth from the MapReduce architecture. You will be taught as to how you can run a MapReduce job on Yarn. Further, the structure of Pig and Hive will be taught in detail. Through this Pig, Hive and Yarn Big Data analytics online training course, the trainees will learn to explore and transform data in HDFS and also analyze data sets using Hive tables and their implementations and definitions. The course further explains the use of Hive file formats and how Hive tables can be created and populated using ORC file formats. This and more advanced concepts will be covered in this advanced level course. To successfully complete this course it is highly recommended that the trainees have a prior understanding of big data analytics and Hadoop. Understanding of SQL scripting and database management will be an added advantage.

Introducing Big Data & Hadoop and its Ecosystem

  • Introducing Big Data
  • Why to use Hadoop
  • Hadoop Distributed File System
  • Replications, Block Size, Secondary Namenode, High Availability

Overview of Pig

  • Overview of Apache Pig
  • It's features
  • Uses and how to interact with Pig

Using Pig for data analysis

  • The syntax of Pig Latin
  • Various definitions
  • Sort and filter data
  • Data types used
  • Using Pig for ETL
  • Data loading methods
  • Schema viewing, field definitions, functions commonly used.

Using Pig for complex data processing

  • Data types
  • How to process data with Pig
  • Grouping for data iteration

How to perform multi-dataset operations

  • Joining Data set
  • Splitting data set
  • Methods for data set integration
  • Set operations

How to extend Pig

  • Working with user defined functions
  • How to perform data processing with other languages
  • Working with imports and macros
  • Streaming and UDFs for extending Pig

Overview of Hive

  • Working with Hive
  • Comparing traditional databases with Pig and Hive
  • How to store data in Hive and Hive schema
  • Interactions of Hive and various use cases

Relational data analysis with Hive

  • Working with HiveQL
  • Basic syntax of Hive
  • The various tables and databases
  • Data types used
  • Data set joining
  • Working with built-in functions
  • Hive queries on scripts, shell and Hue

How to manage data with Hive

  • The various databases and how to work with them
  • Creating databases
  • Data formats in Hive
  • Modeling of data
  • Tables managed through Hive
  • Self-managed Tables
  • Loading of Data
  • How to change databases and Tables
  • Simplification of Queries with Views
  • Storing Results of queries
  • Data access control
  • Management of data with Hive
  • Hive Metastore and Thrift server

Optimizing Hive

  • How to learn performance of query
  • Data indexing, partitioning and bucketing

Hive Extension

  • Deployment of user defined functions for Hive extension

UDF, query optimization

  • Working with User Defined Queries
  • Optimizing queries
  • Methods for performance tuning.


Cluster utilization

  • Dynamic allocation
  • Static MapReduce rules


  • ResourceManager
  • Managing through nodes


  • Integrating existing MapReduce applications

Live Instructor-led & Interactive Online Sessions

Regular Course

Duration : 40 Hours

Capsule Course

Duration : 4-8 Hours

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Training Options


Weekdays- Cloud Based Training

Mon - Fri 07:00 AM - 09:00 AM(Mon, Wed, Fri)

Weekdays Online Lab

Mon - Fri 07:00 AM - 09:00 AM(Tue, Thur)


Weekend- Cloud Based Training

Sat-Sun 09:00 AM - 11:00 AM (IST)

Weekend Online Lab

Sat-Sun 11:00 AM - 01:00 PM

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