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

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

Introduction:

Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop Training Course by Example

Cloudera University’s four-day Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop 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.

Apache Hive makes multi-structured data accessible to analysts, database administrators, and others without Java programming expertise. Apache Pig applies the fundamentals of familiar scripting languages to the Hadoop cluster. Cloudera Impala enables real-time interactive analysis of the data stored in Hadoop via a native SQL environment.

Customize It:

With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.

Audience/Target Group

Data Analysts
Business Intelligence Specialists
Developers
System Architects
Database Administrators

Related Courses:

Duration: 4 days

Class Prerequisites:

Knowledge of SQL
Basic Linux command-line familiarity
Knowledge of at least one scripting language (e.g., Bash scripting, Perl, Python, Ruby)
Prior knowledge of Apache Hadoop is not required

What You Will Learn:

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

Course Content:

Module 1: 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

Module 2: Introduction to Pig

What Is Pig?
Pig’s Features
Pig Use Cases
Interacting with Pig

Module 3: 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

Module 4: Processing Complex Data with Pig

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

Module 5: Multi-Dataset Operations with Pig

Techniques for Combining Data Sets
Joining Data Sets in Pig
Set Operations
Splitting Data Sets

Module 6: 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

Module 7: Introduction to Hive and Impala

What Is Hive?
What Is Impala?
Schema and Data Storage
Comparing Hive to Traditional Databases
Hive Use Cases

Module 8: 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

Module 9: Data Management

Data Storage
Creating Databases and Tables
Loading Data
Altering Databases and Tables
Simplifying Queries with Views
Storing Query Results

Module 10: Data Storage and Performance

Partitioning Tables
Choosing a File Format
Managing Metadata
Controlling Access to Data

Module 11: Relational Data Analysis with Hive and Impala

Joining Datasets
Common Built-In Functions
Aggregation and Windowing

Module 12: Working with Impala

How Impala Executes Queries
Extending Impala with User-Defined Functions
Improving Impala Performance

Module 13: Analyzing Text and Complex Data with Hive

Complex Values in Hive
Using Regular Expressions in Hive
Sentiment Analysis and N-Grams
Conclusion

Module 14: Hive Optimization

Understanding Query Performance
Controlling Job Execution Plan
Bucketing
Indexing Data

Module 15: Extending Hive

SerDes
Data Transformation with Custom Scripts
User-Defined Functions
Parameterized Queries

Module 16: Choosing the Best Tool for the Job

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

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