Inara Technologies | Hadoop Training
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Big Data Hadoop online Training course design to helps you learn the core techniques and concepts of Big Data and Hadoop ecosystem. Learn architecture and concepts of HDFS, Hadoop Cluster setup, Map-Reduce, Hadoop 2.x, Flume, Sqoop, Pig, Hive, HBase, Oozie etc. to become leader in this field. The course is designed in a format that meets your convenience, availability and flexibility needs.

Course Benefits:

    • Syllabus: We came up with a unique list of topics that will help you gradually work your way into the testing world.
    • Practice sessions: Assignments in a way that you will get to apply the theory you learnt immediately.
    • Video sessions of Instructor led live training sessions.
    • Practical learning experiencee with live project work and examples.
    • Support: Our Team is going to be available to you via email or the website for you to reach out to us.
    • Learn SQL Server basics from a professional trainer from your own desk.
    • Information packed practical training starting from basics to advanced testing techniques.
    • Best suitable for beginners to advanced level users and who learn faster when demonstrated.

What are the requirements?

  • Anyone with basic computer knowledge can take this course.

What am I going to get from this course?

  • Master the concepts of HDFS and MapReduce framework
  • Understand Hadoop 2.x Architecture
  • Setup Hadoop Cluster and write Complex MapReduce programs
  • Learn data loading techniques using Sqoop and Flume
  • Perform data analytics using Pig, Hive and YARN
  • Implement HBase and MapReduce integration
  • Implement Advanced Usage and Indexing
  • Schedule jobs using Oozie
  • Implement best practices for Hadoop development
  • Work on a real life Project on Big Data Analytics

Big-Data and Hadoop Training Syllabus

1 Introduction to big data and Hadoop
1.2. Hadoop Architecture
1.3. Installing Ubuntu with Java 1.8 on VM Workstation 11
1.4. Hadoop Versioning and Configuration
1.5. Single Node Hadoop 1.2.1 installation on Ubuntu 14.4.1
1.6. Multi Node Hadoop 1.2.1 installation on Ubuntu 14.4.1
1.7. Linux commands and Hadoop commands
1.8. Cluster architecture and block placement
1.9. Modes in Hadoop
1.9.1. Local Mode
1.9.2. Pseudo Distributed Mode
1.9.3. FullyDistributed Mode
1.10. Hadoop Daemon
1.10.1. Master Daemons(Name Node, SecondaryName Node, Job Tracker)
1.10.2. Slave Daemons(Job tracker, Task tracker)
1.11. Task Instance
1.12. Hadoop HDFS Commands
1.13. Accessing HDFS
1.13.1. CLI Approach
1.13.2. Java Approach

2. Map-Reduce

2.1. Understanding Map Reduce Framework
2.2. Inspiration to Word-Count Example
2.3. Developing Map-Reduce Program using Eclipse Luna
2.4. HDFS Read-Write Process
2.5. Map-Reduce Life Cycle Method
2.6. Serialization(Java)
2.7. Datatypes
2.8. Comparator and Comparable(Java)
2.9. Custom Output File
2.10. Analysing Temperature dataset using Map-Reduce
2.11. Custom Partitioner & Combiner
2.12. Running Map-Reduce in Local and Pseudo Distributed Mode.

3. Advanced Map-Reduce

3.1. Enum(Java)
3.2. Custom and Dynamic Counters
3.3. Running Map-Reduce in Multi-node Hadoop Cluster
3.4. Custom Writable
3.5. Site Data Distribution
3.5.1. Using Configuration
3.5.2. Using DistributedCache
3.5.3. Using stringifie
3.6. Input Formatters3.6.1. NLine Input Formatter
3.6.2. XML Input Formatter
3.7. Sorting
3.7.1. Primary Reverse Sorting
3.7.2. Secondary Sorting
3.8. Compression Technique
3.9. Working with Sequence File Format
3.10. Working with AVRO File Format
3.11. Testing MapReduce with MR Unit
3.12. Working with NYSE DataSets
3.13. Working with Million Song DataSets
3.14. Running Map-Reduce in Cloudera Box


4.1. Hive Introduction & Installation
4.2. Data Types in Hive
4.3. Commands in Hive
4.4. Exploring Internal and External Table
4.5. Partitions
4.6. Complex data types
4.7. UDF in Hive
4.7.1. Built-in UDF
4.7.2. Custom UDF
4.8. Thrift Server
4.9. Java to Hive Connection
4.10. Joins in Hive
4.11. Working with HWI
4.12. Bucket Map-side Join
4.13. More commands
4.13.1. View
4.13.2. SortBy
4.13.3. Distribute By
4.13.4. Lateral View
4.14. Running Hive in Cloudera


5.1. Sqoop Installations and Basics
5.2. Importing Data from Oracle to HDFS
5.3. Advance Imports
5.4. Real Time UseCase
5.5. Exporting Data from HDFS to Oracle
5.6. Running Sqoop in Cloudera

6. PIG

6.1. Installation and Introduction
6.2. WordCount in Pig
6.3. NYSE in Pig
6.4. Working With Complex Datatypes
6.5. Pig Schema
6.6. Miscellaneous Command
6.6.1. Group6.6.2. Filter
6.6.3. Order
6.6.4. Distinct
6.6.5. Join
6.6.6. Flatten
6.6.7. Co-group
6.6.8. Union
6.6.9. Illustrate
6.6.10. Explain
6.7. UDFs in Pig
6.8. Parameter Substitution and DryRun
6.9. Pig Macros
6.10. Running Pig in Cloudera

7. HBase

7.1. HBase Introduction & Installation
7.2. Exploring HBase Shell
7.3. HBase Storage Techinique
7.4. HBasing with Java
7.5. CRUD with HBase
7.6. Hive HBase Integration


8.1. Installing Oozie
8.2. Running Map-Reduce with Oozie
8.3. Running Pig and Sqoop with Oozi

Resume Preparation Tips

        1. Resume Review
        2. Resume Preparation Tips
        3. Sample Resumes

Interview Preparation

        1. Interview Preparation Tips
        2. Sample Interview Questions
        3. Tips & Guidelines for Effective Interview