BIG Data Hadoop Data analyst training-PERIDOT SYSTEMSchennai

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BIG Data Hadoop Data analyst Course content:
To learn hadoop admin, basic knowledge of SQL is needed. We provide a complimentary Course “SQL basic” to all learners to brush up SQL.
Target learners:
If you are Interested in SQL & Scripting Languages, Large Scale Date set operation, Creating values then you can be a Hadoop data analyst.
• About this Course
• About Cloudera
• Course Logistics
• Introductions
Hadoop Fundamentals
• The Motivation for Hadoop
• Hadoop Overview
• MapReduce
• The Hadoop Ecosystem
• Lab Scenario Explanation
• Hands-On Exercise: Data Ingest with Hadoop Tools
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
• Hands-On Exercise: Using Pig for ETL Processing
Processing Complex Data with Pig
• Storage Formats
• Complex/Nested Data Types
• Grouping
• Built-in Functions for Complex Data
• Iterating Grouped Data
• Hands-On Exercise: Analyzing Ad Campaign Data with Pig
Multi-Dataset Operations with Pig
• Techniques for Combining Data Sets
• Joining Data Sets in Pig
• Set Operations
• Splitting Data Sets
• Hands-On Exercise: Analyzing Disparate Data Sets with Pig
Extending Pig
• Adding Flexibility with Parameters
• Macros and Imports
• UDFs
• Contributed Functions
• Using Other Languages to Process Data with Pig
• Hands-On Exercise: Extending Pig with Streaming and UDFs
Pig Troubleshooting and Optimization
• Troubleshooting Pig
• Logging
• Using Hadoop’s Web UI
• Optional Demo: Troubleshooting a Failed Job with the Web UI
• Data Sampling and Debugging
• Performance Overview
• Understanding the Execution Plan
• Tips for Improving the Performance of Your Pig Jobs
Introduction to Hive
• What Is Hive?
• Hive Schema and Data Storage
• Comparing Hive to Traditional Databases
• Hive vs. Pig
• Hive Use Cases
• Interacting with Hive
Relational Data Analysis with Hive
• Hive Databases and Tables
• Basic HiveQL Syntax
• Data Types
• Joining Data Sets
• Common Built-in Functions
• Hands-On Exercise: Running Hive Queries on the Shell, Scripts, and Hue
Hive Data Management
• Hive Data Formats
• Creating Databases and Hive-Managed Tables
• Loading Data into Hive
• Altering Databases and Tables
• Self-Managed Tables
• Simplifying Queries with Views
• Storing Query Results
• Controlling Access to Data
• Hands-On Exercise: Data Management with Hive
Text Processing with Hive
• Overview of Text Processing
• Important String Functions
• Using Regular Expressions in Hive
• Sentiment Analysis and N-Grams
• Hands-On Exercise (Optional): Gaining Insight with Sentiment Analysis
Hive Optimization
• Understanding Query Performance
• Controlling Job Execution Plan
• Partitioning
• Bucketing
• Indexing Data
Extending Hive
• SerDes
• Data Transformation with Custom Scripts
• User-Defined Functions
• Parameterized Queries
• Hands-On Exercise: Data Transformation with Hive
Introduction to Impala
• What is Impala?
• How Impala Differs from Hive and Pig
• How Impala Differs from Relational Databases
• Limitations and Future Directions
• Using the Impala Shell
Analyzing Data with Impala
• Basic Syntax
• Data Types
• Filtering, Sorting, and Limiting Results
• Joining and Grouping Data
• Improving Impala Performance
• Hands-On Exercise: Interactive Analysis with Impala
Choosing the Best Tool for the Job
• Comparing MapReduce, Pig, Hive, Impala, and Relational Databases
• Which to Choose?
For More Details,
Contact: 044-42115526,
99520 10141(RUBAN)
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LB Road, Adyar, Chennai,
Tamil Nadu – 600020

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