BigData Specialization Program

About The Program:

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings a Summer Training/Internship Program on “Power BI & BigData”. We organize Summer Training/Internship Program for improving the knowledge and skills of the Students/Professionals, so that they can become expert in the field of BigData and get their Dream Job in Software Development Field in Big MNCs.

REGex Software Services’s Big-Data program is a valuable resource for both beginners and experts. This specialization program will introduce you to the domain of Data Engineering include Hadoop, Map-Reduce, HIVE, Apache Spark, Kafka Streaming ,SQL, Power BI,  Amazon EMR and much more starting from Basics to Advance. If you want to become Data Engineer / Business Analyst, REGex introduce this program for you.  

July Batches Dates

Batch 1: 3rd July 2023
Batch 2: 10th July 2023
Batch 3: 21st July 2023


Online(Google Meet)


8 Months


15 per Batch

What People Tell About Us

What you will Learn


Duration: 20 Hours


Duration: 60 Hours

Power BI

Duration: 50 Hours


Duration: 20 Hours


Duration: 20 Hours


Duration: 40 Hours

Apache Kafka

Duration: 20 Hours



Duration: 20 Hours

Amazon Emr

Duration: 10 Hours

What you will Learn

  • Linux basics
  • Big Data Analytics & Hadoop
  • HDFS [ Hadoop Distributed File System ]
  • Map-Reduce [ Data Processing ]
  • HIVE
  • Apache Spark on Azure DataBricks
  • NoSQL DataBase
  • Data visualization
  • Power BI
  • SQL
  • Power Query & Editor
  • Dashboard & Graph
  • Amazon EMR
  • Learn how to use these tools in the field of Data Analytics

Study Material

  • E-Notes.
  • Poll Test & Assignments .
  • Over 450+ hours of Live Video Lectures available on demand.
  • Accessing lecture videos and notes.
  • 24*7 Mentorship Support
  • Engaging in real-time project assignments


  • Help you in Data Analytics Domain
  • Able to think out of the box
  • Expertise in different Big Data Tools like HDFS, Hive, Apache Spark, Amazon EMR
  • Become expert in multiple technology domains like: Python, SQL, Power BI etc.
  • Able to solve many Interview Questions of Top MNCs
  • Understand creating Data Insights by connecting data sets, transform & clean the data into data models and then create chars/graphs to provide visuals of the data
  • Build Charts/Graphs/Insights of data sets
  • Work on multiple data sets
  • Become Power BI Analyst / Power BI Developer after completion of this program
  • Able to get package  in Big MNCs upto 30 LPA

Live Sessions

Live Sessions by Expertise Trainers and Access of Recorded Session is also available

Live Projects

Get a chance to work on Industry Oriented Projects to implement your learning

24*7 Support

24*7 Mentorship Support available for all Students to clear all of your doubts

ISO Certification

Get Certificate of Workshop Completion from ISO Certified Company

Package Offered So Far

Minimum Package


Average Package

8 – 12 LPA

Highest Package

36 LPA

Our Students Placed // Partnership


Placed Students

Course Content

  • Basics of Python
  • OOPs Concepts
  • File & Exception Handling
  • Working with Pandas, Numpy & Matplotlib ■ Working with Missing Data ■ Data Grouping ■ Data Subsetting ■ Merging & Joining Data Frames
  • Importing Libraries & Datasets
● Introduction to LINUX Operating System and Basic LINUX commands ● Operating System ● Basic LINUX Commands Linux File System
  • LINUX File System
  • File Types
  • File Permissions
  • File Related Commands
  • Filters
  • Simple Filters
  • Advanced Filters
Vi Editor
  • Vi Editor
  • Input Mode Commands
  • Vi Editor – Save & Quit
  • Cursor Movement Commands
Shell Programming
  • Shell Variables
  • Environmental Variables
  • Shell script Commands
  • Arithmetic Operations
  • Command Substitution
  • Command Line Arguments
  •  Business Intelligence
  •  Need for Business Intelligence
  • Terms used in BI
  • Components of BI
General concept of Data Warehouse
  • Data Warehouse
  • History of Data Warehousing
  • Need for Data Warehouse
  • Data Warehouse Architecture
  • Data Mining Works with DWH
  • Features of Data warehouse
  • Data Mart
  • Application Areas
Dimensional modeling
  • Dimension modeling
  • Fact and Dimension tables
  • Database schema
  • Schema Design for Modeling
  • Star, SnowFlake
  • Fact Constellation schema
  • Use of Data mining
  • Data mining and Business Intelligence
  • Types of data used in Data mining
  • Data mining applications
  • Data mining Products

● What’s Big Data?
● Big Data: 3V’s
● Explosion of Data
● What’s driving Big Data
● Applications for Big Data Analytics
● Big Data Use Cases
● Benefits of Big Data

    • Functional Dependency
    • Closure of Attributes
    • Types of Keys: PrimaryKey CandidateKey & Super Key in DBMS
    • Normalization
    • Indexing
    • Transaction and Concurrency Control
    • Transaction in DBMS
    • ACID Propertise in DBMS
    • Joins in DBMS
    • Create & Alter Table
    • Constraints in SQL
    • Sql Queries & Sub Queries
    • SQL Stored Procedure
    • View, Cursor & Trigger in SQL
    • Common Table Expession
    • Replace Null and Coalesce Function
    • Running Total In SQL

Power BI

  • Introduction
  • Meet Microsoft Power BI Desktop
  • Interface & Workflow
  • Helpful Power BI Resources
  • New Power BI Ribbon
    • Introduction
    • Types of Data Connectors in Power BI Desktop
    • The Power BI Query Editor
    • Demo: Basic Table Transformations in Power BI
    • Power BI Demo:


  • Working with Text Tools
  • Numerical Values
  • Date & Time Tools
  • Creating a Rolling Calendar
  • Grouping & Aggregating Records
  • Pivoting & Unpivoting Data


    • Merging Queries in Power BI Desktop
    • Appending Queries in Power BI Desktop
    • Configuring Power BI Data Source Settings
    • Configuring Power BI Query Refresh Settings
    • Additional Data Types & Categories in Power BI
    • Defining Hierarchies in Power BI Desktop


  • Importing Models from Excel to Power BI



    • Introduction
    • Types of Data Connectors in Power BI Desktop
    • The Power BI Query Editor
    • Demo: Basic Table Transformations in Power BI
    • Power BI Demo:


  • Working with Text Tools
  • Numerical Values
  • Date & Time Tools
  • Creating a Rolling Calendar
  • Grouping & Aggregating Records
  • Pivoting & Unpivoting Data


    • Merging Queries in Power BI Desktop
    • Appending Queries in Power BI Desktop
    • Configuring Power BI Data Source Settings
    • Configuring Power BI Query Refresh Settings
    • Additional Data Types & Categories in Power BI
    • Defining Hierarchies in Power BI Desktop


  • Importing Models from Excel to Power BI



  • Introduction
  • Meet Data Analysis Expressions (DAX)
  • Intro to DAX Calculated Columns
  • Intro to DAX Measures
  • Adding Columns & DAX Measures in Power BI Desktop
  • Implicit vs. Explicit DAX Measures
  • Filter Context Examples in Power BI
  • Understanding DAX Syntax & Operators
  • Common DAX Function Categories
  • DAX Demo: 
    • Basic Date & Time Functions
    • Conditional & Logical Functions (IF/AND/OR)
    • Common Text Functions
    • Joining Data with RELATED
    • Basic Math & Stats Functions
    • Iterator Functions (SUMX, RANKX)
    • Time Intelligence Formulas


  • FILTER Function
  • LOGICAL Functions
  • MATHEMATICAL Functions
  • STATISTICAL Functions
  • TEXT Functions
  • Other Functions
  • Power Query Editor
  • Power BI Aggregation & Template
    • Introduction
    • Exploring the “Report” View in Power BI Desktop
    • Adding Simple Objects to the Power BI Report Canvas
    • Inserting Basic Charts & Visuals in Power BI
    • Conditional Formatting
    • Power BI Report Formatting Options
    • Power BI Report Filtering Options
    • Power BI Demo:
      • Exploring Data with Matrix Visuals
      • Filtering with Date Slicers


  • Showing Key metrics with Cards & KPI Visuals


    • Inserting Text Cards
    • Visualizing Geospatial Data with Maps
    • Visualizing Data with Treemaps
    • Showing Trends with Line & Area Charts
    • Adding Trend Lines & Forecasts 
    • Goal pacing with Gauge Chart
    • Adding Drillthrough Filters
  • Editing Power BI Report Interactions
  • Managing & Viewing Roles in Power BI Desktop

Big Data Tools

● History of Hadoop
● Distributed File System
● What is Hadoop
● Characteristics of Hadoop
● RDBMS Vs Hadoop
● Hadoop Generations
● Components of Hadoop
● HDFS Blocks and Replication
● How Files Are Stored
● HDFS Commands
● Hadoop Daemons

● Difference between Hadoop 1.0 and 2.0
● New Components in Hadoop 2.x
● Configuration Files in Hadoop 2.x
● Major Hadoop Distributors/Vendors
● Cluster Management & Monitoring
● Hadoop Downloads

● What is distributed computing
● Introduction to Map Reduce
● Map Reduce components
● How MapReduce works
● Word Count execution
● Suitable & unsuitable use cases for MapReduce

● Architecture
● Basic Syntax
● Import data from a table in a relational database into HDFS
● import the results of a query from a relational database into HDFS
● Import a table from a relational database into a new or existing Hive table
● Insert or update data from HDFS into a table in a relational database

● Define a Hive-managed table
● Define a Hive external table
● Define a partitioned Hive table
● Define a bucketed Hive table
● Define a Hive table from a select query
● Define a Hive table that uses the ORCFile format
● Create a new ORCFile table from the data in an existing non-ORCFile Hive table
● Specify the delimiter of a Hive table
● Load data into a Hive table from a local directory
● Load data into a Hive table from an HDFS directory
● Load data into a Hive table as the result of a query
● Load a compressed data file into a Hive table
● Update a row in a Hive table
● Delete a row from a Hive table
● Insert a new row into a Hive table
● Join two Hive tables
● Use a subquery within a Hive query

● What is Spark?
● History of Spark
● Spark Architecture
● Spark Shell

● RDD Basics
● Creating RDDs in Spark
● RDD Operations
● Passing Functions to Spark
● Transformations and Actions in Spark
● Spark RDD Persistence

● Pair RDDs
● Transformations on Pair RDDs
● Actions Available on Pair RDDs
● Data Partitioning (Advanced)
● Loading and Saving the Data

● Accumulators
● Broadcast Variables
● Piping to External Programs
● Numeric RDD Operations
● Spark Runtime Architecture
● Deploying Applications

  •  Spark SQL Overview
  • Spark SQL Architecture

Data Frame

  • What are dataframe
  • Manipulating Dataframes
  • Reading new data from different file format
  • Group By & Aggregations functions
  •  What is Spark streaming?

  • Spark Streaming example

  • Understand the fundamentals of Kafka.
  • Understand the distributed nature of Kafka and its scalability.
  • Understand how data is organized into topics and partitions.
  • Install and set up Kafka on your local machine or a cluster.
  • Learn how to create topics, produce messages, and consume messages using
    Kafka APIs.
  • Overview of Amazon EMR and its features.
  • Setting up and configuring Amazon EMR clusters.
  • Running big data processing jobs on EMR.
  • Integrating Amazon EMR with other AWS services.
  • Monitoring and optimizing EMR clusters.
  • Security considerations for EMR.

● Introduction of HBase
● Comparison with traditional database
● HBase Data Model (Logical and Physical models)
● Hbase Architecture
● Regions and Region Servers
● Partitions
● Compaction (Major and Minor)
● Shell Commands
● HBase using APIs


  • Introduction to NoSQL databases and their characteristics.
  • Types of NoSQL databases: Document-oriented, Key-Value, Column-Family, Graph.
  • Use cases for NoSQL databases.
  • MongoDB: A popular document-oriented NoSQL database.
  • Redis: A widely used key-value NoSQL database.
  • Cassandra: A column-family NoSQL database.
  • Understand the fundamentals of ETL (Extract, Transform, Load) processes.
  • Learn how to install and configure Taland.
  • Explore Taland’s interface and understand its key components.
  • Practice using Taland to extract data from different sources, perform
    transformations, and load it into target systems.

● Pre-requisites
● Introduction
● Architecture

● Installation and Configuration
● Repository
● Projects
● Metadata Connection
● Context Parameters
● Jobs / Joblets
● Components
● Important components
● Aggregation & working with Input & output data

● Pseudo Live Project (PLP) program is primarily to handhold participants who are fresh into the technology. In PLP, more importance given to “Process Adherence”
● The following SDLC activities are carried out during PLP
o Requirement Analysis
o Design ( High Level Design and Low Level Design)
o Design of UTP(Unit Test Plan) with test cases
o Coding
o Code Review
o Testing
o Deployment
o Configuration Management
o Final Presentation

Extra Sessions

Additinal Session on GIT, Linux, Docker, AWS Basics, Jenkins and many more for all students.

Fee Structure

Indian Fee

Price: ₹1,39,999/- (Flat 75% off) => ₹34,999/- 

International Fee

Price: $4000 (Flat 75% off) => $1000
Fee can be paid in No Cost EMI @3500/month

Cashback Policy

  • You will get your Unique Referral Code after successful paid registration.
  • You will get ₹2000 Cashback directly in your account for each paid registration from your Unique Referral Code on monthly basis(After Closing Registrations of this program) .
  • For Example:- If we received 10 paid registration from your Unique Referral Code then you will receive ₹2000*10 = ₹20,000 on monthly basis.


You may get Goodies like: REGex T-shirt, REGex Stickers, REGex Key Chain, REGex Cap, REGex Bag etc. after successfully enrollment in this program.

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(Batches Start from  May, June & July 2023)

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*Extra off is applicable on 1 time payment only. Seats can be filled or Price can be increased at any time. Refund policy is not available*