Hadoop for Data Analysts Course | DevelopIntelligence

Course Summary

The Hadoop for Data Analysts training course is designed to demonstrate how to manage, manipulate, and query large complex data in real time, using SL and familiar scripting languages on Hadoop.

The course begins with an introduction to Hadoop basics. Next, it explores how Apache Pig and Apache Hive enable data transformations and analyses via filters, joins, and user-defined functions. The course concludes by examining how to analyze and process data with Pig, and how to optimize Hive.

Purpose

Learn how to use Hadoop to manage, manipulate, and query large complex data in real time.

Audience

This class is targeted at the non-technical data analyst role. Previous experience with a scripting language like Python recommended.

Role

Software Developer

Skill Level

Intermediate

Style

Workshops

Duration

3 Days

Related Technologies

Java

Hadoop

Apache

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Productivity Objectives:

Understand Hadoop fundamentals
Know how to use Pig to analyze data
Understand how to process complex data with Pig
Troubleshoot Pig
Know when to use Hive
Know how to manage data with Hive
Understand how to optimize Hive

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about our training

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Hadoop for Data Analysts training course, you'll learn:

Understanding Hadoop
- Hadoop Overview
- The Hadoop Ecosystem
- The Hadoop Distributed File System (HDFS)
- Input Data into HDFS
- The MapReduce Framework and YARN
- Overview of Sqoop/Flume
- Overview of Ozzie Workflow Engine
Introduction to Pig
- Pig’s Features/Use Cases
- Interact with Pig
Basic Data Analysis with Pig
- Pig Latin
- Load Data
- Field Definitions and Simple Data Types
- Data Output
- View the Schema
- Filter/Sort Data
- Common Functions
Processing Complex Data with Pig
- Storage Formats
- Complex/Nested data types
- Groups
- Built-in functions for working with complex data
- Iterate grouped data
MultiData Set Operations with Pig
- Combine Data Sets
- Join Data Sets
- Set Operations
- Split Data Sets
Extending Pig
- Parameters
- Macros/Imports
- UDFs
- Use Other Languages to Process Data with Pig
Pig Troubleshooting and Optimization
- Logs
- Hadoop’s Web UI
- Data samples and debugs
- Understand the execution plan
- Improve the performance
Introduction to Hive
- Hive schema and data storage
- Hive vs. traditional databases
- Hive vs. pig
- When to use Hive
- Relational data analysis with Hive
- Hive databases and tables
- Basic HiveQL syntax
- Data types
- Joining data sets
- Common built-in functions
Hive Data Management
- Hive data formats
- Create databases and Hivemanaged tables
- Load Data into Hive
- Alter databases and tables
- Self-managed tables
- Simplify queries with views
- Store query results
- Control access to data
Text Processing with Hive
- Text Processes
- Important string functions
- Use regular expressions in Hive
Hive Optimization
- Understand query performance
- Control job execution plan
- Partitioning
- Bucketing
- Index Data
Extending Hive
- Data Transformation with Custom Scripts
- User-defined Functions
- Parameterized Queries