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Hadoop Training in Bhopal

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Hadoop

Whether you are a neophyte, a full-fledged professional or someone looking to gain advanced knowledge of new technologies, the big data training we impart at Praadis Training is the thing for you. We help you most favorably to gain insight of the real world use cases of big data. Moreover, with Hadoop knowledge you can look forward to launching a new career and changeover into an additional challenging role. More interestingly, all industries are already showing an interest in Hadoop and big data. They are eager to learn from their data and deliver solutions that will help them carve a niche in their industry. Enroll now for the course Big Data Hadoop Certification Training from us.

BIG DATA (HADOOP) COURSE MODULE
PRAADIS TRAINING



Introduction to Big Data & Hadoop

  1. What is Big Data?
  2. What are the challenges for processing big data?
  3. What technologies support big data?
  4. What is Hadoop?
  5. Why Hadoop?
  6. History of Hadoop
  7. Use cases of Hadoop
  8. RDBMS vs Hadoop
  9. When to use and when not to use Hadoop
  10. Ecosystem tour
  11. Vendor comparison
  12. Hardware Recommendations & Statistics

HDFS: Hadoop Distributed File System

  1. Significance of HDFS in Hadoop
  2. Features of HDFS
  3. 5 daemons of Hadoop
  4. Data Node and its functionality
  5. Secondary Name Node and its functionality
  6. Job Tracker and its functionality
  7. Task Tracker and its functionality
  8. Data Storage in HDFS
  9. Introduction about Blocks
  10. Data replication
  11. Accessing HDFS
  12. CLI (Command Line Interface) and admin commands
  13. Java Based Approach
  14. Fault tolerance
  15. Download Hadoop
  16. Installation and set-up of Hadoop
  17. Start-up & Shut down process
  18. HDFS Federation

Map Reduce

  1. Map Reduce Story
  2. Map Reduce Architecture
  3. How Map Reduce works
  4. Developing Map Reduce
  5. Map Reduce Programming Model
  6. Different phases of Map Reduce Algorithm
  7. Different Data types in Map Reduce
  8. how Write a basic Map Reduce Program
  9. 3Mapper
  10. Reducer
  11. Creating Input and Output Formats in Map Reduce Jobs
  12. Text Input Format
  13. Key Value Input Format
  14. Key Value Input Format
  15. Key Value Input Format
  16. Sequence File Input Format
  17. Data localization in Map Reduce
  18. Combiner (Mini Reducer) and Partitioner
  19. Hadoop I/O
  20. Distributed cache

PIG

  1. Introduction to Apache Pig
  2. Map Reduce Vs. Apache Pig
  3. SQL vs. Apache Pig
  4. Different data types in Pig
  5. Multi-Threading and Concurrency
  6. Modes of Execution in Pig
  7. Grunt shell
  8. Loading data
  9. Exploring Pig
  10. Latin commands

HIVE

  1. Hive introduction
  2. Hive architecture
  3. Hive vs RDBMS
  4. HiveQL and the shell
  5. Managing tables (external vs managed)
  6. Data types and schemas
  7. Partitions and buckets

HBASE

  1. Architecture and schema design
  2. HBase vs. RDBMS
  3. HMaster and Region Servers
  4. Write pipeline
  5. Read pipeline
  6. Data types and schemas
  7. HBase commands

Flume

  1. Flume introduction
  2. Components of Flume agent
  3. Flume dataflow
  4. Flume Installation
  5. Flume Configuration
  6. Using single and multiple Flume Agent

SQOOP

  1. Sqoop Architecture
  2. Sqoop installation
  3. Sqoop import
  4. Sqoop implementation