Hadoop Essentials Training Course

Course Summary

The Hadoop Essentials training course is designed to demonstrate how to develop on the Hadoop platform.

The course begins with an overview of common use cases for Hadoop. Next, it explores the Hadoop Distributed File System (HDFS) Shell. The course concludes by assessing MapReduce jobs, Pig and Apache HIVE.

Purpose

Learn the fundamentals of the Hadoop platform.

Audience

Anyone wanting to develop solutions on the Hadoop platform. Basic Java experience recommended.

Role

Business Analyst - Project Manager - Software Developer - Technical Manager - Web Developer

Skill Level

Intermediate

Style

Workshops

Duration

4 Days

Related Technologies

Java

Hadoop

Apache

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Productivity Objectives:

Discover the fundamentals of the Hadoop ecosystem
Gain exposure to the main Hadoop processing products
Identify the fundamentals of MapReduce at a low level as well as higher level frameworks

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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 Essentials training course, you'll learn:

Hadoop
- Overview
- Common use cases
HDFS
- Overview
- Architecture
HDFS Shell
- HDFS Components
- HDFS Shell
HDFS Java API
- Overview
- Java basics
Moving Data – Sqoop
- Use Cases/examples
- How to use Sqoop to move data
Moving Data – Flume
- Use Cases/Examples
- How to use Flume to move data
- What tool when?
HBASE
- Overview
- Use Cases (When would you use it)
- HBASE Architecture
- Design HBASE tables
- Storage Model
HBASE Shell
- Runtime Modes
- HBASE Shell overview
- HBASE DML
- HBASE DDL
HBASE Java Client API (Data Access and Admin)
- Overview
- Use the Client API to Access HBASE
- Basic HBASE operations
HBASE: Advanced Data Access
- Architecture
- Scan Data Retrieval
- Result Scanner
- Scanner Cache
- Scanner Batch
- Filters
Map Reduce on YARN
- Overview
- History (V1 vs V2)
- Map Reduce Workflow
- Case Study/Example
- Map Reduce Framework Components
- Map Reduce Configuration
First Map Reduce Job with Java
- Overview
- Job Components (Inputformats, OutputFormat, etc)
- Mapper
- Reducer
- Job configuration
Map Reduce Job Execution
- Components
- Distributed Cache
- Job Execution on YARN
- Failures
Hadoop Streaming
- Overview
- Other Languages for Map Reduce
- Streaming Job Components
Workflows
- Code workflows in Java
- Use Oozie for Workflows
- Comparison
- Use Case example
Apache Pig
- Pig Architecture
- Pig and Map Reduce
- Pig access options
- Pig Components
- Run Pig
- Basic Pig Scripts
Joining Data Sets with Pig
- Inner/Outer/Full Joins
- Build a Pig Script to Join Datasets
- Cogroups
Apache HIVE
- Overview
- Example/Use Case from Industry
- Hive Architecture
- Hive MetaStore
- Hive access options
- Create DatabasesTables
- Loading data
- External vs Internal tables
- Partitions
- Bucketing
- Joins