• Big-Data Introduction and Hadoop Fundamentals
  • Data Storage and Analysis
  • Comparison with RDBMS
• Hadoop â?? A Brief History
• MapReduce â?? Part1
  • Map and Reduce
  • Sample Program
  • Combiner
  • Practitioners and Custom Partitioned
• Hadoop Streaming & Pipes
• HDFS
  • Blocks
  • NN & DN
  • HDFS Federation & High Availability
• HDFS Clients
  • HDFS Command Line
  • HDFS CLI â?? File System Operations Lab
  • HDFS Web UI
  • HDFS Java Client
  • HDFS Java Client â?? File System Operations Lab
  • CRUD Operations using Java Client
  • Anatomy of File Read and File Write
  • DistCp
  • Cluster balancing
• YARN â?? Cluster Management (Hadoop 2.x)
  • How Yarn Applications run?
  • YARN vs MapReduce
  • YARN Scheduling
    • Capacity Scheduler
    • Fair Scheduler
    • FIFO Scheduler
  • Map Reduce â?? Part2
    • Env Setup
    • Tool and ToolRunner
    • Mapper
    • Reducer
    • Driver program
    • How to package the job
    • MapReduce WebUI
    • How MapReduce Job run?
    • Shuffle & Sort
    • Speculative Execution
  • InputFormats
    • Input Splits and Record Reader
    • Default Input Formats
    • Implement Custom Input Format
  • OutputFormats
    • Default Output formats
    • Output Record Reader
  • Compression
    • Map Output
    • Final Output
    • Splittable vs Non Splittable
    • Compression Codecs
  • Serialization
    • Data types â??default
    • Writable vs Writable Comparable
    • Custom Data types â?? Custom Writable/Comparable
  • File Based Data structures
    • Sequence file
    • Reading and Writing into Sequence file
    • Map File
  • Tuning MapReduce Jobs
  • Advanced MapReduce
    • Counters
      • Built-In Counters Classification
      • User Defined Counters
    • Sorting
      • Partial Sort
      • Total Sort
      • Secondary Sort
    • Joins
      • Map-side joins
      • Reduce-side joins
      • Distributed Cache
    • Hive
      • Comparison with RDBMS
      • HQL
      • Data types
      • Tables
      • Importing and Exporting
      • Partitioning and Bucketing â?? Advanced.
      • Joins and Join Optimization.
      • Functions- Built in & user defined
      • Advanced Optimization of HQL
      • Storage File Formats â?? Advanced
      • Loading and Storing Data
      • SerDes â?? Advanced
    • Sqoop
      • Important basics
      • Import â?? Deep dive
      • Export â?? Deep dive
      • Sqoop Optimization â?? Incremental Load
      • Many more
    • PIG
      • Important basics
      • Pig Latin
      • Data types
      • Functions â?? Built-in, User Defined
      • Loading and Storing Data
    • Flume
      • Configure Flume and Import data
      • Architecture and LAB
    • Oozie
      • Different workflow jobs
      • Ooze scheduler.
      • LAB â?? covers advanced topics
    • HBase
      • NoSQL databases Introduction
      • CAP theorem
      • HBase Architecture
      • HBase Clients â?? Java Client
      • Loadling Data
      • UDF,UDAF,UDTFs
    • Zookeeper
      • Zookeeper in HBase
      • How Zookeeper is used in Production
    • Ambari
      • Real time Cluster deployment Using Ambari
      • Monitoring the Cluster
    • REST API
      • Introduction
      • Real time Use cases of How REST is used with Hadoop
    • Labs:
      • Real Time use cases and Data sets covered (10+ Real Time datasets)
      • Word count, Sensors(Weather Sensors)Dataset, Social Media data sets like YouTube, Twitter data analysis,
      • Java and Unix Basics Lab
      • Hadoop, Hive, Sqoop, Oozie, HBase, Flume Installations â??Pseudo&Cluster

• COURSE FEE â?? RS 35,000/- (Negotiable)

(FLEXIBLE PAYMENT MONTLY PAYMENT OPTIONS ARE AVAILABLE ON 0% EMI BASIS)

• 100% Job Guarantee Support including the post on job support till 1 year.

Master Project:

• • Real-time DataWarehouse migration:
  • Real-time concepts covered are
    • Hive - Advanced topics
    • Sqoop import/export
    • Oozie Scheduling
    • How Hadoop MR used in DW
    • RDBMS concepts
    • ETL tool concepts
    • Integration with Reporting tools