Hadoop Training
Specialize in Hadoop Which is The Backbone of Big Data Solutions
THE GOAL OF COURSE
The purpose of the Hadoop Training Program is to enable participants to consolidate their knowledge in the their minds with experience sharing, example implementations and case studies, while providing detailed information on key concepts and techniques on Hadoop
WHAT YOU WILL LEARN
- Have deep knowledge on the Hadoop Ecosystem,
- Learn the terminology and basic concepts behind Hadoop Architecture and its components, selection criteria and best solution designs,
- Understand how you should prepare your Hadoop architecture to production,
- Specialize in the most appropriate cluster configurations and integrations with the hardware and systems within the data center,
- Learn the YARN Architecture, MapReduce and HDFS Concepts in detail,
- Develop technical knowledge and competencies with workshops,
- Make a significant contribution to your development in Big Data,
- Be able to find answers to your questions at the points where your head is installed while developing after the program
WHO BENEFITS
- Employees who are doing or planning to make a career in the Big Data place
- Leading teams in technology / IT functions
- Leaders responsible for Digital Transformation.
- Data Scientists and Data Analysts
- New Graduates and University Students
Learn the Hadoop Ecosystem Playing a Vital Role for Unstructured Big Data in Detail
Theories and practices you will see throughout the program will answer your questions on Hadoop Ecosystem, also you will learn key techniques to develop the right strategy while implementing your projects
COURSE OUTLINE
- Module-1: Introduction to Hadoop
- What Hadoop is
- The History of Hadoop
- The Components of Hadoop Ecosystem
- Use Cases of Hadoop Components
- Map Reduce Concept
- Hadoop Distributed File System Architecture (HDFS)
- What Hadoop is Good For?
- Module-2: Hadoop Distributed File System (HDFS)
- HDFS Architecture and Key Features
- HDFS Installation and Configuration
- HDFS Use Cases
- HDFS Command Line Usage
- HDFS Security Settings
- Web Interface for HDFS
- FileSystem Java API Usage
- Data Formats (JSON / Euro / Parquet)
- Module-3 MapReduce 2.0 and YARN Architecture
- The Constraints of Map Reduce 1.0
- MapReduce Concepts
- YARN Architecture Features
- MapReduce and YARN Command-Line Tools
- Developing MapReduce Jobs
- MapReduce API Features
- Job Configuration, Operation and Monitoring
- Running Job on YARN
- Using YARN Web Interface
- Resource Allocation and Fault Tolerance
- Using Distributed Cache
- Working with Hadoop Streaming
- Module-4 Using The Hadoop API
- ToolRunner Usage
- Testing with MRUnit
- Reducing Intermediate Results Using Combiner
- Better Load Balancing with Partitioner
- Debugging MapReduce Code
- Facilitating Debugging with LocalJobRunner Mode
- Job Information Collection Using Counter
- Module-5 Working With HBase, Pig and Hive
- HBase Installation
- Understanding HBase Data Model
- Data Operations on HBase
- Pig Installation
- Figuring Out Pig Data Model
- Preparing and Running Pig Scripts
- Hive Installation
- Understanding Hive Components and Data Model
- Working with Hive Query Language (HQL)
- Module-6 Hadoop Cluster Installation and Best Practices
- Hadoop Cluster Installation and Configuration with Ambari
- Ambari Cluster Management
- Using Ambari Web Interface
- Hadoop Security Management
- Production Setup and Performance Tips
- Lab: You will learn HDFS, MapReduce, HBase, Pig, Hive Operations on sample datasets with lots of examples