Cloudera Data Analyst

Course Curriculum

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 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

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

Relational Data Analysis with Hive and Impala

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

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

Prerequisite

• Knowledge of SQL and basic understanding of Linux

Certification

Upon completion of the course, attendees are encouraged to continue their study and register for the CCA Data Analyst exam. Certification is a great differentiator. It helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills & expertise.