Expert and Credible Training


This 4-day hands-on training course teaches students how to develop applications and analyze Big Data stored in Apache Hadoop 2.0 using Pig and Hive. Students will learn the details of Hadoop 2.0, YARN, the Hadoop Distributed File System (HDFS), an overview of MapReduce, and a deep dive into using Pig and Hive to perform data analytics on Big Data. Other topics covered include data ingestion using Sqoop and Flume, and defining workflow using Oozie.

Note: this course was formerly named: Developing Apache Hadoop 2.0 Solutions for Data Analysts

Students should be familiar with programming principles and have experience in software development. SQL knowledge is also helpful. No prior Hadoop knowledge is required.


Day 1

  • Understanding Hadoop 2.0
  • The Hadoop Distributed File System (HDFS)
  • Inputting Data into HDFS
  • The MapReduce Framework and YARN

Day 2

  • Introduction to Pig
  • Advanced Pig Programming

Day 3

  • Hive Programming
  • Using HCatalog
  • Advanced Hive Programming

Day 4

  • Advanced Hive Programming (cont.)
  • Data Analysis and Statistics
  • Defining Workflow with Oozie

Lab Content

Students will work through the following lab exercises using the Hortonworks Data Platform 2.0:

  • Use HDFS commands to add/remove files and folders from HDFS
  • Use Sqoop to transfer data between HDFS and a RDBMS
  • Run a MapReduce job
  • Run a YARN application
  • Explore and transform data using Pig
  • Split a dataset using Pig
  • Join two datasets using Pig
  • Use Pig to transform and export a dataset for use with Hive
  • Use HCatLoader and HCatStorer to retrieve HCatalog schemas from within a Pig script
  • Understand how a Hive table is stored in HDFS
  • Use Hive to discover useful information in a dataset
  • Understand how Hive queries get executed as MapReduce jobs
  • Perform a join of two datasets with Hive
  • Use advanced Hive features like windowing, views and ORC files
  • Use the Hive analytics functions (rank, dense_rank, cume_dist, row_number)
  • Write a custom reducer in Python that reduces the number of underlying MapReduce jobs generated from a Hive query
  • Analyze and sessionize clickstream data using the Pig DataFu library
  • Compute quantiles of NYSE stock prices
  • Use Hive to compute ngrams on Avro-formatted files
  • Define an Oozie workflow


At the completion of the course students will be able to:

  • Explain Hadoop 2.0 and YARN
  • Explain use cases for Hadoop
  • Explain how HDFS Federation works in Hadoop 2.0
  • Explain the various tools and frameworks in the Hadoop 2.0 ecosystem
  • Explain the architecture of the Hadoop Distributed File System (HDFS)
  • Use the Hadoop client to input data into HDFS
  • Use Sqoop to transfer data between Hadoop and a relational database
  • Explain the architecture of MapReduce
  • Explain the architecture of YARN
  • Run a MapReduce job on YARN
  • Write a Pig script to explore and transform data in HDFS
  • Define advanced Pig relations
  • Use Pig to apply structure to unstructured Big Data
  • Invoke a Pig User-Defined Function
  • Use Pig to organize and analyze Big Data
  • Understand how Hive tables are defined and implemented
  • Use the new Hive windowing functions
  • Explain and use the various Hive file formats
  • Create and populate a Hive table that uses the new ORC file format
  • Use Hive to run SQL-like queries to perform data analysis
  • Use Hive to join datasets using a variety of techniques, including Map-side joins and Sort-Merge-Bucket joins
  • Write efficient Hive queries
  • Create ngrams and context ngrams using Hive
  • Perform data analytics like quantiles and page rank on Big Data using the DataFu Pig library
  • Explain the uses and purpose of HCatalog
  • Use HCatalog with Pig and Hive
  • Define a workflow using Oozie
  • Schedule a recurring workflow using the Oozie Coordinator


Data Analysts, BI Analysts, BI Developers, SAS Developers and other types of analysts who need to answer questions and analyze Big Data stored in a Hadoop cluster.

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