Cloudera Data Analyst Training (CDAT) – 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 Scenario Explanation

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

Common Operators and Built-In Functions

• Operators
• Scalar Functions
• Aggregate Functions

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

Working with Multiple Datasets

• UNION and Joins
• Handling NULL Values in Joins
• Advanced Joins

Analytic Functions and Windowing

• Using Common Analytic Functions
• Other Analytic Functions
• Sliding Windows

Complex Data

• Complex Data with Hive
• Complex Data with Impala

Analyzing Text

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

Apache Hive Optimization

• Understanding Query Performance
• Bucketing
• 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 Hive, Impala, and Relational Databases
• Which to Choose?

Conclusion