> > > CDAPHIH Detailed outline

Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop (CDAPHIH)

Course Description Schedule Course Outline

Detailed Course Outline

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
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?
 

Accessing our website tells us you are happy to receive all our cookies. However you can change your cookie settings at any time. Find out more.   Got it!