Big Data Analytics

The term Big Data Analytics relates to the concept of interpreting with relatively large levels of data and to uncover hidden patterns, ongoing trends in the market, knowing the changes in the customer's interest and in their preferences with ongoing time, finding unknown correlations and other such valuable information. This information acts as an aid for an organization in making more effective business decisions.

Big Data Analytics Online Training provided at our E school cart is mainly meant for those aspirants who are having an acute desire to mould their career in the field of Big Data Analytics. Our online training has well scheduled flexible timings and the course fee structure will also be well affordable.

Online Big Data Analytics Training will provide the best scope for endowed career opportunities by getting enrolled into E school cart.

What Exactly Big Data Analytics?

Big Data Analytics mainly relates to the techniques and functioning methodologies which can help in making an effective analysis of the inbound data sets. Thereby, it will help in drawing effective decisions for achieving the desired business goals and objectives within a quick span of time. Effective implementation of Big Data Analytics will help the implying organization to always stay ahead of their respective competitors. This is the prime reason why most of the multinational national organizations are in search for the best skilled Big Data Analytics experts. Job opportunities in this field are quite immense and the pay packages offered by the best-skilled experts are also quite high.

Big Data Analytics Online Training at our E school cart will provide you with the best course curriculum covering all the in-depth and the advanced concepts. So for bright career opportunities in the field of Big Data Analytics, getting enrolled into our institute offering Online Big Data Training will be a perfect choice.


Online Big Data Analytics Training provided by our E school cart is mainly aimed at enhancing the subject skills of the aspirants to such a high extent that they can effectively handle any sort of complex challenges that might face during their professional life as a Big Data Analytics professional. The main ordeals of providing Online Big Data Analytics Training are

  • To effectively meet the demand for the experts in this field
  • To make the interested candidates become eligible for taking up the professional role of Big Data Analytics expert
  • To make the students skillful enough that they satisfy all the present industry requirements of this field
  • To provide enriched Big Data Analytics experts by providing advanced quality oriented training

Introduction to Hadoop

  • What is Big Data
  • Need and significance of innovative technologies
  • What is Hadoop
  • 3 Vs (Characteristics)
  • History of Hadoop and its Uses
  • Different Components of Hadoop
  • Various Hadoop Distributions
  • Traditional Database vs Hadoop

HDFS (Hadoop Distributed File System)

  • Significance of HDFS in Hadoop
  • HDFS Features
  • Daemons of Hadoop And functionalities
  • NameNode
  • DataNode
  • JobTracker
  • TaskTrack
  • Secondary NameNode

Data Storage in HDFS

  • Blocks
  • Heartbeats
  • Data Replication
  • HDFS Federation
  • High Availability

Accessing HDFS

  • CLI (Command Line Interface) Unix and Hadoop Commands
  • Java Based Approach

Data Flow

  • Anatomy of a File Read
  • Anatomy of a File Write

Hadoop Archives


  • Introduction to MapReduce
  • NMapReduce Architecture
  • MapReduce Programming Model
  • MapReduce Algorithm and Phases
  • Data Types
  • Input Splits and Records
  • Blocks Vs Splits

Basic MapReduce Program

  • Driver Code
  • Mapper Code
  • Reducer Code
  • Combiner and Shuffler

Creating Input and Output formats in MapReduce Jobs

  • File Input / Output Format
  • Text Input / Output Format
  • Sequence File Input / Output Format,etc.
  • Data Localization in
  • MapReduce
  • Distributed Cache
  • A Sample Map reduce Program
  • Identity Mapper
  • IdentityReducer


  • Introduction to Apache Pig
  • MapReduce Vs. Apache Pig
  • SQL Vs. Apache Pig
  • Different Data types in Apache Pig

Modes of Execution in Apache Pig

  • Local Mode
  • Map Reduce or Distributed Mode

Execution Mechanism

  • Grunt shell
  • Script
  • Embedded

Data Processing Operators

  • Loading and Storing Data
  • Filtering Data
  • Grouping and Joining Data
  • Sorting Data
  • Combining and Splitting Data


  • How to write a simple PIG Script
  • UDFs in PIG


  • Introduction to Sqoop
  • Sqoop Architecture and Internals
  • MySQL client and server installation
  • How to connect relational database using Sqoop
  • Sqoop Commands
  • VExport
  • HIVE imports


The Metastore

Comparison with Traditional Databases

  • Schema on Read Versus Schema on Write
  • Updates, Transactions, and Indexes


  • Data Types
  • Operators and Functions


  • Managed Tables and External Tables
  • Static Partitions and Dynamic Partitions
  • Partitions and Buckets
  • Storage Formats
  • Importing Data
  • Altering Tables
  • Dropping Tables

Querying Data

  • Sorting and Aggregating
  • Hive Query Language
  • MapReduce Scripts
  • Joins
  • Subqueries
  • Views

User-Defined Functions

  • Writing a UDF
  • Writing a UDAF
  • Limitations of Hive
  • Hive vs Pig


  • Introduction to Hbase
  • HBaseVs HDFS
  • Use Cases

Basics Concepts

  • Column families
  • Scans
  • Hbase Architecture
  • Zoo Keeper
  • SQL databases vs NoSQL databases


  • REST
  • Thrift
  • Java Based
  • Avro
  • MapReduce integration
  • MapReduce over Hbase
  • Schema definition
  • Basic CRUD Operations

Introduction to Flume

  • Introduction to Flume
  • Uses of Flume

Flume Architecture

  • Flume Master
  • Flume Collectors
  • Flume Agents

Oozie, HCatalog

  • Introduction to Oozie
  • Uses of Oozie
  • Oozie workflow basics


  • Introduction to Mahout
  • Sample Profiles will be provided on Big Data & Hadoop

Mock Interviews

  • Introduction to R (Analytical Tool)
  • Introduction to Tableau (BI Tool)
  • Project :
  • Social Media Analytics
  • (Twitter, Facebook Data Processions)
  • Three Mock Interviews