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Using Pig, Hive, and Impala with Hadoop

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Duration: 3 Days

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

  • The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysis
  • The fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with Hadoop tools
  • How Pig, Hive, and Impala improve productivity for typical analysis tasks
  • Joining diverse datasets to gain valuable business insight
  • Performing real-time, complex queries on datasets

This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Knowledge of SQL is assumed, as is basic Linux command-line familiarity. Knowledge of at least one scripting language (e.g., Bash scripting, Perl, Python, Ruby) would be helpful but is not essential. Prior knowledge of Apache Hadoop is not required.

  1. Hadoop Fundamentals
  • The Motivation for Hadoop
  • Hadoop Overview
  • Data Storage: HDFS
  • Distributed Data Processing: YARN, MapReduce, and Spark
  • Data Processing and Analysis: Pig, Hive, and Impala
  • Data Integration: Sqoop
  • Other Hadoop Data Tools
  • Exercise Scenarios Explanation
  1. Introduction to Pig
  • What Is Pig?
  • Pig’s Features
  • Pig Use Cases
  • Interacting with Pig
  1. Basic Data Analysis with Pig
  • Pig Latin Syntax
  • Loading Data
  • Simple Data Types
  • Field Definitions
  • Data Output
  • Viewing the Schema
  • Filtering and Sorting Data
  • Commonly-Used Functions
  1. Processing Complex Data with Pig
  • Storage Formats
  • Complex / Nested Data Types
  • Grouping
  • Built-In Functions for Complex Data
  • Iterating Grouped Data
  1. Multi-Dataset Operations with Pig
  • Techniques for Combining Data Sets
  • Joining Data Sets in Pig
  • Set Operations
  • Splitting Data Sets
  1. Pig Troubleshooting and Optimization
  • Troubleshooting Pig
  • Logging
  • Using Hadoop’s Web UI
  • Data Sampling and Debugging
  • Performance Overview
  • Understanding the Execution Plan
  • Tips for Improving the Performance of Your Pig Jobs
  1. Introduction to Hive and Impala
  • What Is Hive?
  • What Is Impala?
  • Schema and Data Storage
  • Comparing Hive to Traditional Databases
  • Hive Use Cases
  1. Querying with Hive and Impala
  • Databases and Tables
  • Basic Hive and Impala Query Language Syntax
  • Data Types
  • Differences Between Hive and Impala Query Syntax
  • Using Hue to Execute Queries
  • Using the Impala Shell
  1. Data Management
  • Data Storage
  • Creating Databases and Tables
  • Loading Data
  • Altering Databases and Tables
  • Simplifying Queries with Views
  • Storing Query Results
  1. Data Storage and Performance
  • Partitioning Tables
  • Choosing a File Format
  • Managing Metadata
  • Controlling Access to Data

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