Hadoop, Data Warehousing, and ETL for Software Developers Training

Answers to Popular Questions:

 
Yes, this class can be tailored to meet your specific training needs.
Yes, we provide Cloud consulting services.
Yes, group discounts are provided.

Course Description

 
This course enables participants to understand what the Hadoop platform is and provides hands-on lab exercises to apply the concepts, plan, run, and use the platform. Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads into the traditional BI analytics world. This course will introduce the participant to the core components of the Hadoop Eco System and its analytics, as well as planning, running, and administering a Hadoop Cluster. It will emphasize the use cases of Hadoop and Data Warehousing, and provide best practices and guidelines on combining the two.
Course Length: 3 Days
Course Tuition: $1190 (US)

Prerequisites

For software developers, business analysts, and IT administrators. Participants should be able to navigate the Linux command-line interface and have a basic knowledge of Linux editors, such as vi or nano. Also, basic knowledge of Java and understanding ETL are required.

Course Outline

 
Course Topics
• Introduction
• Data Access, Integration
• Transformation, Aggregation
• Feature Generation
• Join Various Data Sources
• Filter, Search, Transpose
• Binning and Smoothing
• More Topics
 
Course Objectives
Upon completion of this course, participants will be able to:
• Describe what the Hadoop platform is and its purpose.
• Describe the core components of the Hadoop Eco System.
• Plan, run, and use a Hadoop Cluster.
• Describe and apply best practices and guidelines on combining Hadoop and Data Warehousing.
 
 
 
Day 1:
I. Introduction
A. Hadoop Eco System Overview
B. HDFS
C. MapReduce
 
II. Data Access, Integration
A. Navigate in Hadoop
B. Access Data and Files in HDFS and Tables
C. Pig
 
Day 2: Hive
III. Transformation, Aggregation
A. Consume Large Datasets and Tables
B. Working with Dates, Timestamps, Arrays
C. Use Group By and Summarize Various Attributes
D. Converting Strings to Date/Time, Numbers
E. Concatenating Columns
F. Parsing Semi-Structured Data
 
IV. Feature Generation
A. Create New Attributes, Mathematical Calculations, Windowing Functions
B. Use Character and String Functions
 
V. Join Various Data Sources
A. Join Multiple Files and Tables in an Optimized Way
 
VI. Filter, Search, Transpose
A. Ways to Limit the Data Using Various Predicate Methods
B. Pivot the Data in Different Ways Wide to Long and Vice Versa
C. Find Missing Values
 
VII. Binning and Smoothing
A. Create Buckets and Groups for Categorization
 
Day 3:
 
VIII. More Topics
A. HBase
B. Others

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