DataX Bootcamp: Learn Data Engineering from Scratch

Follow a structured 90-day learning roadmap covering SQL, Python, Data Modelling, Pandas, PySpark, Databricks and Snowflake.

Build real-world Data Engineering projects, prepare for technical interviews and get continuous support through weekly live Q&A sessions and our 24×7 WhatsApp and Discord communities.

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159+ Hours of video content

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18 Structured Modules

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3 End-to-End Projects

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Lifetime Access

Join DataX BootcampSelf-Paced Learning • Weekly Live Group Q&A • 24×7 Community Support

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See What You’ll Learn Inside DataX

Watch the complete overview of the curriculum, projects, learning structure and support included in the DataX Bootcamp.

What Is DataX Bootcamp?

DataX is a practical Data Engineering bootcamp designed for beginners, freshers, career switchers and working professionals.

The bootcamp takes you from SQL, Python and Data Modelling fundamentals to PySpark, Databricks, Snowflake, Delta Lake, cloud migration and real-world Data Engineering projects.

Along with self-paced recorded lessons, you get hands-on assignments, quizzes, dedicated interview preparation, weekly live group Q&A sessions and 24×7 community support through WhatsApp and Discord.

What Will You Learn?

You’ll learn how modern Data Engineering systems are designed, developed and optimised—from writing SQL queries and processing data with Python to building scalable pipelines using PySpark, Databricks and Snowflake.

The focus is not limited to watching tool-based lessons. You’ll understand architecture, solve practical assignments, build end-to-end projects and prepare for real Data Engineering interview scenarios.

By following the complete learning path, you will work on:

• SQL for Data Engineering and interviews
• Python, files, APIs and ETL development
• Data Modelling and Data Warehousing
• Pandas for data cleaning and transformation
• PySpark and distributed data processing
• Delta Lake and Lakehouse Architecture
• Databricks workflows and orchestration
• Snowflake, Streams, Tasks and ELT
• BigQuery, GCS and cloud migration
• Data Engineering system design
• Resume, GitHub and interview preparation

Learn at Your Own Pace—With Continuous Support

You learn at your own pace, without pressure—while still following a clear, structured roadmap built by a working Data Engineer. You’ll build real projects, prep for interviews, and master tools like SQL, Python, Pandas, PySpark, and Databricks—all without burnout or overwhelm.

• Self-Paced Recorded Lessons

• Weekly Live Group Q&A Sessions

• 24×7 WhatsApp and Discord Community Support

Build Projects You Can Confidently Explain

DataX includes practical projects that help you understand how different Data Engineering components work together—from source ingestion and transformation to automation, storage and business reporting.

Project 1

Google Jobs Aggregator: End-to-End ETL Pipeline

Design and build a complete Data Engineering pipeline from scratch.

• Understand the business requirement
• Design the complete pipeline architecture
• Ingest data from files and APIs
• Clean and transform the data
• Build Bronze, Silver and Gold layers
• Automate the pipeline using Databricks Workflows
• Create job-market insights using Databricks SQL

Technologies Used: Python, Pandas, PySpark, Databricks, Delta Lake, Databricks Workflows and Databricks SQL

Project 2

End-to-End Snowflake Data Warehouse Project

Build a complete analytical Data Warehouse pipeline using Snowflake.

• Understand Snowflake architecture
• Ingest data into Snowflake
• Build cleaning and transformation layers
• Implement incremental data processing
• Automate pipelines using Streams and Tasks
• Design a production-ready Snowflake workflow

Technologies Used: Snowflake, SQL, ELT, Snowflake Streams, Snowflake Tasks and Data Warehouse Modelling

Project 3

Production-Grade GCP-to-Databricks Migration

Understand how a real cloud data migration project is planned and implemented.

• Work with BigQuery source data
• Move data through Google Cloud Storage
• Load and process data in Databricks
• Handle cloud credentials securely
• Understand the complete migration architecture
• Learn production-level migration considerations

Technologies Used: Google Cloud Platform, BigQuery, Google Cloud Storage, Databricks, PySpark, Delta Lake and Azure-based Secret Management

Course curriculum

18 Module

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159+ Hours

|

Assignments, Quizzes and End-to-End Projects

Welcome to the Bootcamp!

Complete Data Engineering Learning Path

Start with the fundamentals, move into scalable Data Engineering tools, build production-style projects and finish with dedicated interview preparation.

Module 0 - Orientation + Setup

Goal: Understand the Data Engineering journey and prepare your complete learning environment.

Introduction to Data Engineering

Roles in a modern data team

Data Engineer responsibilities

Required tools and environment setup

GitHub, VS Code and Databricks setup

Databricks Free Edition setup

Introduction to Lakehouse Architecture

Setup assignments and orientation quiz

Module 1 - SQL for Data Engineers

Goal - Learn SQL from scratch and progress to advanced Data Engineering and interview scenarios.

SQL fundamentals and MySQL Workbench setup

SELECT, WHERE, GROUP BY and aggregations

Primary, Foreign, Composite and Candidate Keys

Table relationships and referential integrity

Inner, Left, Right, Full, Cross and Self Joins

Subqueries and Common Table Expressions

CASE WHEN and advanced SQL functions

Window Functions

SQL Normalisation

SQL optimisation

Practical assignments, quizzes and masterclasses

Module 2 - Data Modeling (Using Lucidchart)

Goal: Convert business requirements into structured da

Entities, attributes and relationships

Conceptual, Logical and Physical Data Models

Fact and Dimension tables

Star Schema and Snowflake Schema

Designing models using Lucidchart

Logical ER diagram assignment

Dataset normalisation assignment

Data Modelling quiz

Module 3 - Python for Data Engineers

Goal: Learn Python through practical Data Engineering use cases.

Python variables and data types

Lists, loops and conditions

Functions and reusable code

Working with files

Calling and processing APIs

File and API-based data ingestion

Error and exception handling

Object-Oriented Programming

Building an OOP-based ETL pipeline

String Handling Functions

Assignments and Python quizzes

Module 4 - Pandas for Data Engineering

Goal: Clean, transform and analyse messy datasets using Pandas.

Series and DataFrames

Accessing and filtering data

Null and duplicate handling

Handling invalid values and outliers

Aggregations and grouping

Pivot operations

Real-world data-cleaning scenarios

Messy-to-clean dataset project

Practical assignments and quizzes

Module 5: PySpark for Big Data

Goal: Build scalable Data Engineering pipelines using PySpark and Apache Spark.

Introduction to Big Data

PySpark DataFrames

Spark transformations and actions

SparkSession

Driver, executors, jobs, stages and tasks

Partitioning, persistence and caching

Spark performance optimisation

Delta Lake and Lakehouse Architecture

Slowly Changing Dimensions

Spark memory management

DAG and Lazy Evaluation

Shuffle and data skew

Practical assessments and interview quizzes

Module 6: Big Data Architecture and Distributed Processing

Goal: Understand how large-scale Data Engineering systems process data across multiple machines.

Big Data and distributed computing

Parallel data processing

Evolution of Big Data technologies

Hadoop and Apache Spark

Legacy vs. modern data platforms

Modern Data Architecture

Lakehouse concepts

Architecture-based quizzes

Module 7: LIVE Google Jobs Aggregator – Build an End-to-End ETL Pipeline from Scratch

Goal: Combine the concepts learned so far into a complete Data Engineering project.

Understand the business requirement

Design the pipeline architecture

Ingest source data

Build Bronze, Silver and Gold layers

Apply data cleaning and business transformations

Automate execution using workflows

Monitor pipeline runs

Create a business dashboard

Module 8: AI-Powered Data Engineering

Goal: Use AI as a practical assistant while developing Data Engineering solutions.

Prompt Engineering fundamentals

Providing proper technical context

Prompts for SQL and Python

PandasAI overview and practical use cases

LangChain overview and Data Engineering use cases

Prompts for ETL development

AI-assisted debugging

Databricks Assistant

Practical mini-projects and quizzes

Module 9: Get Hired – Resume, Projects & Interview Domination

Goal: Learn how to present your skills and projects clearly to recruiters and interviewers.

Build a focused Data Engineering resume

Organise your GitHub profile and projects

Improve your LinkedIn profile

Understand how to increase LinkedIn visibility

Explain project requirements and architecture

Present your individual contribution

Discuss challenges and solutions confidently

Create a strong Data Engineering project story

Platforms Covered: LinkedIn, GitHub and Resume-Building Tools

Module 10: SQL Interview Preparation Materials

Goal: Practise SQL questions based on real Data Engineering and business scenarios.

SQL fundamentals and deep-dive questions

Joins, CTEs and Subqueries

Window Functions

Incremental loading scenarios

SQL security and access-control concepts

Module 11: Python for Data Engineers Interview Preparation Materials

Goal: Prepare for Python questions commonly asked in Data Engineering interviews.

Python fundamentals

Lists, dictionaries and other data structures

Functions and logic-building questions

File-processing scenarios

Practical Data Engineering coding scenarios

Module 12: PySpark Mastery – Real Interview Prep Questions

Goal: To give idea about real questions in interview

PySpark Set 1_ Core Concepts, Transformations, and Practical ETL Scenarios

PySpark Set 2_ Core Concepts, Transformations, and Practical ETL Scenarios

PySpark Set 3_ Core Concepts, Transformations, and Practical ETL Scenarios

PySpark Set 4_ Core Concepts, Transformations, and Practical ETL Scenarios

PySpark Set 5_ Core Concepts, Transformations, and Practical ETL Scenarios

Module 13: Data Engineering System Design + Scenario-Based Questions

Goal: To give idea about real questions in interview

Set 1_ Data Engineering Project Thinking, Architecture & Tooling Choices

Set 2_ Data Engineering Project Thinking, Architecture & Tooling Choices

Set 3_ Data Engineering Project Thinking, Architecture & Tooling Choices

Set 4_ Data Engineering Project Thinking, Architecture & Tooling Choices

Set 5_ Data Engineering Project Thinking, Architecture & Tooling Choices

Module 13: Data Engineering System Design + Scenario-Based Questions

Goal: To give idea about real questions in interview

Set 1_ Data Engineering Project Thinking, Architecture & Tooling Choices

Set 2_ Data Engineering Project Thinking, Architecture & Tooling Choices

Set 3_ Data Engineering Project Thinking, Architecture & Tooling Choices

Set 4_ Data Engineering Project Thinking, Architecture & Tooling Choices

Set 5_ Data Engineering Project Thinking, Architecture & Tooling Choices

Module 14: Databricks for Data Engineers

Goal: Understand the Databricks platform and use it to build and manage Data Engineering workloads.

Complete Data Engineering architecture

Databricks workspace and platform fundamentals

Compute and notebooks

Lakehouse Architecture

Delta Lake concepts

Data management and governance

Databricks Workflows

Jobs and task orchestration

Pipeline execution and dependencies

Technologies Used: Databricks, Delta Lake, PySpark, Databricks Workflows and Databricks SQL

Module 15: Production-Grade GCP-to-Databricks Migration

Goal: Understand how a real cloud data migration project is planned, designed and implemented.

Understand the migration requirement

Design source and target architecture

Work with BigQuery source data

Move data through Google Cloud Storage

Load data into Databricks

Process data using PySpark

Build Delta tables

Handle credentials securely

Understand production migration considerations

Work across multiple cloud platforms

Technologies Used: Google Cloud Platform, BigQuery, Google Cloud Storage, Databricks, PySpark, Delta Lake and Azure-based Secret Management, ADF

Module 16: Snowflake Data Warehouse Analytics

Goal: Learn Snowflake from platform fundamentals to a complete Data Warehouse project.

Snowflake architecture and setup

Data ingestion into Snowflake

Data cleaning and ELT transformations

Silver-layer development

Incremental data processing

Snowflake Streams

Snowflake Tasks

Pipeline automation

Performance and security concepts

Production architecture

End-to-end Snowflake project

Scenario-based assessments

Technologies Used: Snowflake, SQL, ELT, Snowflake Streams and Snowflake Tasks

Module 17: Practical Data Engineering Interview Preparation

Goal: Revise important concepts and prepare for practical Data Engineering interview discussions.

Data Engineering interview revision

Advanced Databricks questions

Data Engineering architecture scenarios

Production-based questions

Project-explanation practice

Resume and job-search discussions

Interview-focused doubt discussions

AI Enabled Data Engineer in 90 Days

Instructed by Anurag Srivastava, Lead Data Engineer

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Testimonial

Real Learners. Real Progress.

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I attended Anurag’s job search webinar, and it was more than just a regular session. He shared a smart, job focused strategy—emphasizing that we don’t need to do everything, just do what really matters. He generously gave away free materials, along with a detailed monthly plan—it was like spoon-feeding everything we need. I’m excited to start implementing it. Highly recommend!

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Meghana Sharma

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Anurag offered an exceptional learning experience. I gained confidence and skills that landed me a great role in the industry.

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Prerna Vashisht

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Thank you, Anurag Srivastava, for an excellent bootcamp on 'Learn Python from Scratch.' The hands-on approach made learning both practical and enjoyable. I gained real confidence in my Python skills thanks to your clear and engaging teaching style!

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Vanshika Guliani

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Honestly, it felt less like a session and more like a private mentorship. Clear, actionable, and super motivating! The LinkedIn hacks were absolutely brilliant

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Sai Prasad Debata

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I'm really loving this bootcamp, sir. your content is always unique and informative. I've enjoyed my previous bootcamps with you, and this one is no exception. You’re definitely doing something different, and it's really paying off, sir🙌🏻

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DIPANJAN SAHA

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It was really a helpful session for me and anurag really motivated me towards my next step as i was stuck at one place since 3 months. If in future i found myself at the place again i would definitely reach out to him for help. Thanks bro.

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Become Data Engineer in Just 90 Days

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ATTENTION: Reister today to get yout High Paying Data Engineering Role

Frequently Asked Questions

Is this course self-paced?

Yes, 100% self-paced. You can learn anytime, at your convenience, without any deadlines.

Is this course beginner-friendly?

Yes, it's designed for complete beginners. You’ll learn everything from scratch — no prior experience required.

What tools and tech will I learn?

You’ll work hands-on with SQL, Python, Pandas, PySpark, Delta Lake, Databricks, PandasAI, and more — all focused on real job work.

Is there any certification?

Yes, you'll get a certificate of completion. But more importantly, you’ll have job-ready skills and real projects to showcase.

Will I build real projects?

Yes. You’ll complete end-to-end projects including data cleaning, ETL pipelines, and dashboards — all added to your GitHub portfolio.

How is this different from other courses?

This is not just video lectures. It’s a practical, structured, interview-focused bootcamp taught by a working Data Engineer.

Zero to Job Ready AI-Enabled Data Engineer in
90-Days

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159+ Hours of video content

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18 Modules

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4.9+ (656+ Rating)

Get my 90-day roadmap on how to build your first data pipeline, which will start bringing you interviews from recruiters and land you highly paid job roles.

Register Today

Data With Anurag

About Contact Free Resources Courses

DataX Bootcamp: Learn Data Engineering from Scratch

See What You’ll Learn Inside DataX

What Is DataX Bootcamp?

What Will You Learn?

Learn at Your Own Pace—With Continuous Support

Build Projects You Can Confidently Explain

Project 1

Project 2

Project 3

Course curriculum

Welcome to the Bootcamp!

Goal: Understand the Data Engineering journey and prepare your complete learning environment.

Goal - Learn SQL from scratch and progress to advanced Data Engineering and interview scenarios.

Goal: Convert business requirements into structured da

Goal: Learn Python through practical Data Engineering use cases.

Goal: Clean, transform and analyse messy datasets using Pandas.

Goal: Build scalable Data Engineering pipelines using PySpark and Apache Spark.

Goal: Understand how large-scale Data Engineering systems process data across multiple machines.

Goal: Combine the concepts learned so far into a complete Data Engineering project.

Goal: Use AI as a practical assistant while developing Data Engineering solutions.

Goal: Learn how to present your skills and projects clearly to recruiters and interviewers.

Goal: Practise SQL questions based on real Data Engineering and business scenarios.

Goal: Prepare for Python questions commonly asked in Data Engineering interviews.

Goal: To give idea about real questions in interview

Goal: To give idea about real questions in interview

Goal: To give idea about real questions in interview

Goal: Understand the Databricks platform and use it to build and manage Data Engineering workloads.

Goal: Understand how a real cloud data migration project is planned, designed and implemented.

Goal: Learn Snowflake from platform fundamentals to a complete Data Warehouse project.

Goal: Revise important concepts and prepare for practical Data Engineering interview discussions.

AI Enabled Data Engineer in 90 Days

Testimonial

Become Data Engineer in Just 90 Days

Frequently Asked Questions

Is this course self-paced?

Is this course beginner-friendly?

What tools and tech will I learn?

Is there any certification?

Will I build real projects?

How is this different from other courses?

Zero to Job Ready AI-Enabled Data Engineer in90-Days

Zero to Job Ready AI-Enabled Data Engineer in
90-Days