Course Details

We are established to serve our clients with best of education. Our intensive coaching program is dealt with diligence and support for superior leaning. The course contents are:

Introduction to Pig

• What Is Pig?
• Pig’s Features
• Pig Use Cases
• Interacting with Pig

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

Processing Complex Data with Pig

• Storage Formats
• Complex/Nested Data Types
• Grouping
• Built-In Functions for Complex Data
• Iterating Grouped Data

Multi-Dataset Operations with Pig

• Techniques for Combining Data Sets
• Joining Data Sets in Pig
• Set Operations
• Splitting Data Sets

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

Introduction to Hive and Impala

• What Is Hive?
• What Is Impala?
• Schema and Data Storage
• Comparing Hive to Traditional Databases
• Hive Use Cases?

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
• Data Management

Data Storage

• Creating Databases and Tables
• Loading Data
• Altering Databases and Tables
• Simplifying Queries with Views
• Storing Query Results

Data Storage and Performance

• Partitioning Tables
• Choosing a File Format
• Managing Metadata
• Controlling Access to Data     
• Relational Data Analysis with Hive and Impala
• Joining Datasets
• Common Built-In Functions
• Aggregation and Windowing

Working with Impala

• How Impala Executes Queries
• Extending Impala with User-Defined
• Functions
• Improving Impala Performance
• Analyzing Text and Complex Data with Hive
• Complex Values in Hive
• Using Regular Expressions in Hive
• Sentiment Analysis and N-Grams
• Conclusion
• Hive Optimization
• Understanding Query Performance
• Controlling Job Execution Plan
• Bucketing
• Indexing Data
• Extending Hive
• SerDes
• Data Transformation with Custom Scripts
• User-Defined Functions
• Parameterized Queries
• Choosing the Best Tool for the Job
• Comparing MapReduce, Pig, Hive, Impala, and
• Relational Databases
• Which to Choose?