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

Detaillierter Kursinhalt

Introduction

Apache 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
• Database Integration: Sqoop
• Other Hadoop Data Tools
• Exercise Scenarios

Introduction to Apache Pig

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

Basic Data Analysis with Apache 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 Apache Pig

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

Multi-Dataset Operations with Apache Pig

• Techniques for Combining Datasets
• Joining Datasets in Pig
• Set Operations
• Splitting Datasets

Apache 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 Pig Jobs

Introduction to Apache Hive and Impala

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

Querying with Apache Hive and Impala

• Databases and Tables
• Basic Hive and Impala Query Language Syntax
• Data Types
• Using Hue to Execute Queries
• Using Beeline (Hive’s Shell)
• Using the Impala Shell

Apache Hive and Impala 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
• Loading Data into Partitioned Tables
• When to Use Partitioning
• Choosing a File Format
• Using Avro and Parquet File Formats

Relational Data Analysis with Apache Hive and Impala

• Joining Datasets
• Common Built-In Functions
• Aggregation and Windowing

Complex Data with Apache Hive and Impala

• Complex Data with Hive
• Complex Data with Impala

Analyzing Text with Apache Hive and Impala

• Using Regular Expressions with Hive and Impala
• Processing Text Data with SerDes in Hive
• Sentiment Analysis and n-grams in Hive

Apache Hive Optimization

• Understanding Query Performance
• Bucketing
• Indexing Data
• Hive on Spark

Apache Impala Optimization

• How Impala Executes Queries
• Improving Impala Performance

Extending Apache Hive and Impala

• Custom SerDes and File Formats in Hive
• Data Transformation with
• Custom Scripts in Hive
• User-Defined Functions
• Parameterized Queries

Choosing the Best Tool for the Job

• Comparing Pig, Hive, Impala, and Relational Databases
• Which to Choose?

Conclusion