Bigdata & Hadoop Training

For whom Hadoop is?

The Big Data training and the Hadoop training courses offered at Victorrious Digiital  have a huge demand in the job market today. Being one of the best Big Data Hadoop training institutes, Victorrious Digiital enables you to explore a unique way of learning new skills with the professional training approach. Going through the well designed, industry aligned Big Data courses certainly trains students thoroughly for the highly competitive industry. A training in Big Data and Analytics can lead to better job opportunities. To learn the latest in this technology, join Victorrious Digiital today.

  •  Hadoop is open source (Cost saving / Cheaper)
  •  Hadoop solves Big Data problem which is very difficult or impossible to solve using highly paid tools in market
  •  It can process Distributed data and no need to store entire data in centralized storage as it is there with other tools.
  •  Now a days there is job cut in market in so many existing tools and technologies because clients are moving towards a cheaper and efficient solution in market named HADOOP

Why Hadoop?

  • Solution for BigData Problem
  • Open Source Technology
  • Based on open source platforms
  • Contains several tool for entire ETL data processing Framework
  • It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.

Big Data

  • Distributed computing
  • Data management – Industry Challenges
  • Overview of Big Data
  • Characteristics of Big Data
  • Types of data
  • Sources of Big Data
  • Big Data examples
  • What is streaming data?
  • Batch vs Streaming data processing
  • Overview of Analytics
  • Big data Hadoop opportunities


  • Why we need Hadoop
  • Data centres and Hadoop Cluster overview
  • Overview of Hadoop Daemons
  • Hadoop Cluster and Racks
  • Learning Linux required for Hadoop
  • Hadoop ecosystem tools overview
  • Understanding the Hadoop configurations and Installation.

HDFS (Storage)

  • HDFS
  • HDFS Daemons – Namenode, Datanode, Secondary Namenode
  • Hadoop FS and Processing Environment’s UIs
  • Fault Tolerant
    • High Availability
    • Block Replication
  • How to read and write files
  • Hadoop FS shell commands

YARN (Hadoop Processing Framework)

  • YARN
  • YARN Daemons – Resource Manager, NodeManager etc.
  • Job assignment & Execution flow

Apache Hive

  • Data warehouse basics
  • OLTP vs OLAP Concepts
  • Hive
  • Hive Architecture
  • Metastore DB and Metastore Service
  • Hive Query Language (HQL)
  • Managed and External Tables
  • Partitioning & Bucketing
  • Query Optimization
  • Hiveserver2 (Thrift server)
  • JDBC , ODBC connection to Hive
  • Hive Transactions
  • Hive UDFs
  • Working with Avro Schema and AVRO file format

Apache Pig

  • Apache Pig
  • Advantage of Pig over MapReduce
  • Pig Latin (Scripting language for Pig)
  • Schema and Schema-less data in Pig
  • Structured , Semi-Structure data processing in Pig
  • Pig UDFs
  • HCatalog
  • Pig vs Hive Use case


  • Sqoop commands
  • Sqoop practical implementation
    • Importing data to HDFS
    • Importing data to Hive
    • Exporting data to RDBMS
  • Sqoop connectors


  • Flume commands
  • Configuration of Source, Channel and Sink
  • Fan-out flume agents
  • How to load data in Hadoop that is coming from web server or other storage
  • How to load streaming data from Twitter data in HDFS using Hadoop


  • Oozie
  • Action Node and Control Flow node
  • Designing workflow jobs
  • How to schedule jobs using Oozie
  • How to schedule jobs which are time based
  • Oozie Conf file


  • Scala
    • Syntax formation, Datatypes , Variables
  • Classes and Objects
  • Basic Types and Operations
  • Functional Objects
  • Built-in Control Structures
  • Functions and Closures
  • Composition and Inheritance
  • Scala’s Hierarchy
  • Traits
  • Packages and Imports
  • Working with Lists, Collections
  • Abstract Members
  • Implicit Conversions and Parameters
  • For Expressions Revisited
  • The Scala Collections API
  • Extractors
  • Modular Programming Using Objects


  • Spark
  • Architecture and Spark APIs
  • Spark components
    • Spark master
    • Driver
    • Executor
    • Worker
    • Significance of Spark context
  • Concept of Resilient distributed datasets (RDDs)
  • Properties of RDD
  • Creating RDDs
  • Transformations in RDD
  • Actions in RDD
  • Saving data through RDD
  • Key-value pair RDD
  • Invoking Spark shell
  • Loading a file in shell
  • Performing some basic operations on files in Spark shell
  • Spark application overview
  • Job scheduling process
  • DAG scheduler
  • RDD graph and lineage
  • Life cycle of spark application
  • How to choose between the different persistence levels for caching RDDs
  • Submit in cluster mode
  • Web UI – application monitoring
  • Important spark configuration properties
  • Spark SQL overview
  • Spark SQL demo
  • SchemaRDD and data frames
  • Joining, Filtering and Sorting Dataset
  • Spark SQL example program demo and code walk through

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