This OnDemand offering provides you with a 180-day subscription that begins on the date of purchase.
Skills Gained
- The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysis
- The fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with Hadoop tools
- How Pig, Hive, and Impala improve productivity for typical analysis tasks
- Joining diverse datasets to gain valuable business insight
- Performing real-time, complex queries on datasets
Prerequisites
This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Knowledge of SQL is assumed, as is basic Linux command-line familiarity. Knowledge of at least one scripting language (e.g., Bash scripting, Perl, Python, Ruby) would be helpful but is not essential. Prior knowledge of Apache Hadoop is not required.
Cloudera University’s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. Cloudera presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.
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?
