Specialization in BigData - Become a Bigdata Expert in 2 months

Industry Relevant & Recognized Training | Gain HandsOn Experience | Best-In-Class Content | Industry Use-Cases

Vimal Daga

The World Record Holder, Founder at LinuxWorld & #13, Sr. Principal IT Consultant, TEDx Speaker & Philanthropist

Only for Last 2 Days

Tools included:

What will you learn in this BigData Specialization Program?

  • Understanding the basics of big data and its characteristics
    • Definition of big data
    • Sources of big data
    • Types of big data
    • Big data processing technologies
    • Big data analytics
    • Challenges of big data
    • Applications of big data:
  • Exploring the challenges of storing, processing, and analyzing big data
  • Introduction to big data technologies and tools
  • Understanding different types of data storage systems
  • Relational and non-relational databases


  • Introduction
  • Configuring HDFS parameters and tuning its performance
  • Performing file read and write operations on HDFS
  • Managing block replication and storage space
  • Implementing HDFS security and access control policies
  •  Setting up a Hadoop cluster and HDFS
  • Configuring HDFS parameters and tuning its performance
  • Performing file read and write operations on HDFS
  • Managing block replication and storage space
  • Implementing HDFS security and access control policies
  • HDFS Overview
  • HDFS Operations
  • Command Reference
  • MapReduce
  • Streaming
  •  Multi-Node Cluster


  • What Is Hive?
  • Data Units, Type System, Built In Operators and Functions, Creating, Showing,   Altering, and Dropping Tables
  • Loading Data, Querying, and Inserting Data
  • Hive SQL Language Manual:  Commands, CLIs, Data Types,
  • File Formats and Compression:  RCFile, Avro, ORC, Parquet; Compression, LZO
  • Procedural Language:  Hive HPL/SQL
  • Hive Web Interface
  • Hive SerDes:  Avro SerDe, Parquet SerDe, CSV SerDe, JSON SerDe   
  • Hive Accumulo Integration
  • Using TiDB as the Hive Metastore database
  • Installing Hive
  • Configuring Hive
  • Setting Up Metastore
  • Hive Schema Tool
  • Setting Up Hive Web Interface
  • Setting Up Hive Server (JDBC, ODBC, Thrift, HiveServer2)
  • Hive Replication
  • Hive on Spark: Getting Started


  • Quick Start: a quick introduction to the Spark API; start here!
  • RDD Programming: an overview of Spark basics – RDDs (core but old API)
    • Spark SQL, Datasets, and DataFrames: processing structured data with relational queries (newer API than RDDs)
  • Structured Streaming: processing structured data streams with related queries .
  • Spark Streaming: processing data streams using DStreams (old API)
  • MLlib: applying machine learning algorithms
  • PySpark: processing data with Spark in Python
  • Spark SQL CLI: processing data with SQL on the command line.


  • Introduction
  • Apache HBase Configuration: Standalone 
  • Managing HBase
  • HBase Replication
  • HBase High Availability
  • Troubleshooting HBase
  • Upstream Information for HBase
  • Creating HBase tables and performing CRUD operations
  • Using HBase shell commands and API operations
  • Implementing schema design and modeling in HBase
  • Processing HBase data with MapReduce and Apache Spark
  • Integrating HBase with other Hadoop ecosystem components like HDFS and yarn
  • Tuning HBase performance and optimizing HBase queries
  • Implementing security and access control policies in HBase


  • User Interfaces
  • Payload, Mapper, Reducer, Partitioner, Counter
  • Job Configuration
  • Task Execution & Environment
  • Memory Management
  • Map Parameters
  • Shuffle/Reduce Parameters
  • Configured Parameters
  • Task Logs
  • Distributing Libraries
  • Job Submission and Monitoring, Job Control, Job Input
  • InputSplit
  • RecordReader, Job Output
  • OutputCommitter
  • Task Side-Effect Files
  • RecordWriter
  • Other Useful Features
  • Submitting Jobs to Queues
  • Counters
  • DistributedCache
  • Profiling
  • Debugging
  • Data Compression
  • Skipping Bad Records


  • Installation
  • Import
  • Import-All-Tables
  • Export
  • Sqoop Job
  • Codegen
  • Eval
  • List Databases
  • List Tables


  • What is a NoSQL Database?
  • Brief History of NoSQL Databases
  • NoSQL Database Features
  • Types of NoSQL Database
  • Difference between RDBMS and NoSQL
  • Why NoSQL?
  • When should NoSQL be Used?
  • NoSQL Database Misconceptions
  • NoSQL Query
  • NoSQL clusters
  • NoSQL data modeling
  •  NoSQL databases like Hadoop MongoDB Cassandra


  • Setting up a MongoDB instance and configuring it
  • Create an Atlas Free Tier Cluster
  • Creating databases and collections in MongoDB
  • Documents
  • MongoDB Query API
  • BSON Types
  • Installation
  • MongoDB Shell (mongosh)
  • Performing CRUD operations and queries in MongoDB
  • Implementing data modeling and schema design in MongoDB
  • Using indexing and performance optimization techniques in MongoDB
  • Implementing data aggregation and map-reduce operations in MongoDB
  • Setting up and configuring MongoDB sharding and replication
  • Implementing security and access control policies in MongoDB
  • Using MongoDB drivers for various programming languages
  • Administering and monitoring MongoDB instances
  • Replication, Sharding, Change Streams, Time Series, Transactions, Administration, Storage.


  • Getting Started
  • The Yarn Workflow
  • CLI Commands
  • Migrating from npm client
  • Creating a Package
  • Dependencies & Versions
  • Configuration
  • Offline Mirror
  • Workspaces
  • Plug’n’Play


  • Introduction
  • Environment
  • Installation
  • Execution
  • Grunt Shell
  • Basics
  • Load & Store Operators
  • Reading Data
  • Storing Data
  • Describe Operator
  • Union Operator
  • Split Operator
  • Filter Operator
  • Distinct Operator
  • Foreach Operator
  • Order By
  • Limit Operator
  • Eval Functions
  • Bag & Tuple Functions
  • String Functions


  • Concepts and Architecture
  • Deployment Planning
  • Administration
  • SQL
  • Resource Management
  • Performance Tuning
  • Scalability Considerations
  • Partitioning
  • File Formats
  • Using Impala to Query Kudu Tables
  • HBase Tables
  • S3 Tables
  • ADLS Tables
  • Logging
  • Impala Client Access
  • Troubleshooting Impala


  • Services: EC2, EMR, S3, Redshift, Athena, AWS ECS
  • Data processing using Apache Spark and MapReduce
    • Data analysis using R and Python
    • Data visualization using Tableau and other tools
    • Connecting to and extracting data from various Big Data sources
    • Transforming and cleaning data in preparation for analysis in Tableau
    • Creating interactive dashboards and visualizations in Tableau for Big Data
    • Optimising Tableau performance for large-scale data analysis
    • Collaborating and sharing Tableau dashboards with others
    • Integrating Tableau with other Big Data tools and technologies
  • Batch processing vs. stream processing
  • Data processing using Apache Flink
  • Understanding big data infrastructure and architecture
  • Cluster computing with Hadoop and Apache Spark
  • Containerization with Docker and Kubernetes
  • Infrastructure as a service (IaaS) and platform as a service (PaaS) on the cloud
  • Apache Kafka for real-time data streaming
    • Introduction to Apache Kafka
    • Setting up a Kafka Cluster
    • Producers and Consumers
    • Kafka APIs
    • Configuration 
    • Connectors and Kafka Connect
    • Replication
    • Monitoring and Operations
    • Security
  • Splunk Introduction:
    • Introduction to search language
    • Overview of Splunk and its capabilities
    • Understanding Splunk data models
    • Splunk Architecture
  • Installation and Configuration:
    • Installing and configuring Splunk
    • Splunk configuration files and their purpose
    • Deployment Server configuration
    • Managing Splunk instances
    • Upgrading Splunk instances
    • Best practices for Splunk deployment
  • Data Inputs:
    • Understanding various data input sources
    • Configuring data inputs
    • Parsing and indexing data
  • Searching and Reporting:
    • Search fundamentals
    • Creating search queries
    • Visualizing search results
    • Understanding reporting options
    • Creating reports and dashboards
  • Splunk Administration:
    • User management and authentication
    • Configuring roles and permissions
    • Monitoring and troubleshooting Splunk instances
    • Backup and recovery of Splunk instances
  • Splunk Apps and Add-ons:
    • Understanding Splunk apps and add-ons
    • Installing and configuring apps and add-ons
    • Managing app and add-on configurations
  • Monitoring and Alerting:
    • Setting up monitoring and alerting
    • Creating alerts and triggers
    • Configuring notifications
  • Advanced Topics:
    • Advanced search techniques
    • Performance tuning and optimization
    • Advanced deployment and scalability
    • Integrating Splunk with other systems
  • Working on a real-world project in big data
  • Applying the knowledge and skills learned in the course
  • Presenting the project work to the class

Who is this training for?

Data Analysts
IT Professionals
Business Professionals
Data Scientists

4 Reasons to learn BigData under Mr Vimal Daga


Teaching beyond the certification



Practical Industry knowledge, Creator mentality


90 days technical support and a community for lifetime networking 


Exclusive training of most demanded & market valued BigData

You probably already know...

$273.4 billion by 2026

90.07 USD billion data size

MarketShare of Big data

75% of the Hiring Managers

₹8.0 lakhs for Fresher

The 4-Step to become a Big data Expert in 2 months

Still not sure if the training is for YOU?

Please see if you can resonate with any, tick where your answer is YES !

If you have ticked any of the above box, then you are invited to join The Specialization in Big Data Training

Get Certified

Yes! You will be certified for this training once you submit the task given, if any

Official and verified:

Receive an instructor signed certificate with institution’s logo to verify your achievements and increase your job prospects

Easily shareable

Add the certificate to your CV or your Resume or post it directly on LInkedin. You can even post it on instagram and twitter.

Enhances Credibility

Use your certificate to enhance your professional credibility and stand out among your peers as an expert

Increase potential opportunities

By showcasing your achieved skill set using your certificate, attracting the employer for the desired job opportunities becomes easy

Know Your Mentor

None of the technologies is complex since created by human beings. Hence, anyone can learn it and create something new.

#13 proudly presents Vimal Daga as the mentor for this program

A world record holder, Mr. Vimal Daga is a Technologist, Philanthropist & A TEDx Speaker who is dedicatedly working towards his vision- “Awakening the youth through a culture of right education”.

He is the first one in the world to become “RedHat Certified Architect Level 25 along with Enterprise Application Level 10”. Companies benefited from his 19+ years of experience

He has expertise in multitude of latest and high-end technologies namely Machine Learning, Deep Learning, Delphix, AppDynamics, Docker, DevOps, Cloud Computing, AWS, and many more.

Students from various background trained


Professionals from various MNCs trained


Global IT Certifications Achieved


Companies benefited from Consultancy


Vimal's Journey
From humble beginnings to winning learners' hearts across the globe

With the expertise to deliver any technology in an easy way and a heart to share his knowledge, Vimal Daga is a self-made IT enthusiast. He is meticulous about researching the skills needed for the future and making them available for the entrepreneurs & professionals of tomorrow. The masterly IT consultant has changed the lives of many students with his inspiring teachings. 

You can be the next!

Stepping Stones of Vimal’s vision: 

Vimal Daga, in his near 20 years of experience has earned many laurels. To mention a few:

  • Became Young Entrepreneur 
  • A TedX speaker
  • Trained more than 3,50,000+ students for free
  • Two-time world record holder
  • Fastest achiever of 11 AWS global certifications (in 11 days)
  • Highest RHCA level holder (25th level with 10th level EA)
  • Creating 100s and more of entrepreneurs through his trainings

Book your spot ! We will be increasing the price soon…

Specialization in BigData - Become a Bigdata Expert in 2 months

₹ 15,000 ₹ 45,000 (+ taxes)

What you’ll learn...

And bonuses too...

For us, our learners are the heart of our institution.

Our community is a mix of students, professionals, and budding entrepreneurs, who come as learners and become the torchbearers of our vision. They are the source of our inspiration and the drivers of our passion.

Let’s look at what some of our learners have to say about us.

Frequently Asked Questions

  • Duration: 75 Days
  • Live Online Training
  • No prior knowledge required
  • Interview Questions

No, we are not offering any corporate or group discount.

We start from the very basics, so no previous knowledge is required.

Yes DEFINITELY..You will be added to a community where technical support team members will answer your queries for 90 days from the completion of the program.

Our BigData alumni works at: