Flagship Program • 16 Weeks

Complete Data EngineeringBootcamp

A detailed, structured, and beginner-friendly Data Engineering program designed to help freshers and career switchers build strong foundations in SQL, Python, PySpark, Azure, ETL, data modeling, projects, portfolio building, and interview preparation.

16-Week StructureBeginner FriendlyProject FocusedInterview OrientedPortfolio Guidance

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Program Fee

₹5,999

One-time payment

16-week structured learning plan

SQL, Python, PySpark, Azure, Airflow coverage

Data modeling, ETL/ELT, APIs, file formats

Project and portfolio building direction

Resume, LinkedIn, GitHub, and interview preparation

Not a job guarantee program.

This bootcamp is designed to help you become job-ready through structured learning, practical projects, interview preparation, and honest mentorship. Careers are built with clarity, consistency, skills, projects, and preparation — not fake promises.

Who is it for?

Freshers, students, manual testers, support engineers, analysts, and career switchers who want to enter Data Engineering with proper direction.

Main Goal

Help learners move from confusion to clarity with structured learning, practical skills, project direction, and interview preparation.

Learning Style

Practical, beginner-friendly, roadmap-based, and focused on real Data Engineering concepts used in the industry.

Timeline

16-Week Learning Plan

The bootcamp is structured week by week so beginners know what to learn, when to learn, and how each skill connects to the Data Engineering journey.

Week 1: Data Engineering Foundation

Week 2-4: SQL for Data Engineering

Week 5-6: Python for Data Engineering

Week 7: Git, GitHub, Linux, and CI/CD Basics

Week 8-9: Data Engineering Core Concepts

Week 10-11: Big Data and PySpark

Week 12-13: Cloud Data Engineering

Week 14: Airflow and Orchestration

Week 15: Real-World Projects and Portfolio Building

Week 16: Resume, LinkedIn, GitHub, and Interview Preparation

Detailed Curriculum

What You Will Learn

This syllabus is designed to cover the core foundation expected from freshers and career switchers preparing for Data Engineering roles.

Module 1

Week 1

Data Engineering Career Foundation

Understand what Data Engineering is, what a Data Engineer actually does, and how real-world data teams work.

What is Data Engineering?

Role and responsibilities of a Data Engineer

Source → Storage → Processing → Analytics flow

ETL vs ELT

Batch vs streaming overview

Data quality, performance, and reliability

How Data Engineers work with analysts, scientists, backend teams, and business users

Real-world data pipeline architecture overview

Outcome

You will understand the Data Engineering role, career path, tools, responsibilities, and how real data teams operate.

Module 2

Week 2 - Week 4

SQL for Data Engineering

Build strong SQL skills for Data Engineering, analytics, real-world querying, and interviews.

Database concepts: tables, rows, columns, primary keys, foreign keys

DDL: CREATE, ALTER, DROP, TRUNCATE, constraints, data types

DML: INSERT, UPDATE, DELETE, MERGE

SELECT, WHERE, ORDER BY, DISTINCT, LIMIT

GROUP BY, HAVING, aggregate functions

INNER, LEFT, RIGHT, FULL, SELF, and CROSS joins

Simple and correlated subqueries

CTEs and recursive CTE overview

Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD

Set operations: UNION, UNION ALL, INTERSECT, EXCEPT

Date functions, string functions, CASE WHEN, NULL handling, COALESCE

Query optimization basics: indexes, EXPLAIN plan, partitioning concept

Interview-style SQL business problems

Outcome

You will become comfortable writing SQL queries used in Data Engineering, analytics, and interviews.

Module 3

Week 5 - Week 6

Python for Data Engineering

Learn Python from a Data Engineering point of view: files, APIs, automation, data processing, and validation.

Python syntax, variables, data types, type conversion, comments, indentation

Lists, tuples, sets, and dictionaries

if/else, loops, list comprehensions, dictionary comprehensions

Functions, arguments, return values, scope, lambda, map, filter

File handling: reading and writing files

CSV, JSON, and basic Excel/file processing understanding

Modules, imports, pip, virtual environments, and project structure

try/except, finally, custom exceptions, bad data handling

OOP basics: classes, objects, inheritance, polymorphism, encapsulation overview

APIs using requests, GET/POST, JSON parsing, API response handling

Pandas: DataFrames, cleaning, groupby, merge, filtering, missing value handling

Database connection libraries, logging basics, test case writing basics

Automation scripts and data validation scripts

Outcome

You will be able to use Python for files, APIs, automation, data processing, and basic pipeline scripting.

Module 4

Week 7

Git, GitHub, Linux, and Developer Workflow

Learn how professional code is managed, versioned, documented, and shared in real projects.

What is version control?

Git init, clone, add, commit, push, pull

Branching and pull requests

Merge conflicts and collaboration basics

README writing and GitHub repository structure

Linux terminal navigation

File and folder commands

Permissions and environment variables

Basic networking commands

Shell command basics

What is CI/CD?

Why CI/CD matters in data projects

Basic deployment and code quality mindset

Outcome

You will understand how to manage code professionally using Git, GitHub, Linux basics, and CI/CD fundamentals.

Module 5

Week 8 - Week 9

Data Engineering Core Concepts

Understand the core concepts used in real Data Engineering projects, including modeling, ETL/ELT, quality, APIs, and file formats.

OLTP vs OLAP

Normalization and denormalization

Star schema and snowflake schema

Fact tables and dimension tables

Slowly Changing Dimensions overview

ETL vs ELT

Ingestion, transformation, and loading

Full load vs incremental load

CDC overview

Scheduling and orchestration concepts

REST API basics

CSV, JSON, Parquet, and Avro overview

Why file formats matter in Data Engineering

Data quality checks: null checks, duplicate checks, schema validation, count validation

Logging, monitoring, retry, error handling, and alerting overview

Outcome

You will understand the core building blocks of production-ready Data Engineering pipelines.

Module 6

Week 10 - Week 11

Big Data and PySpark

Learn Spark and PySpark fundamentals used for distributed data processing and big data transformations.

Why big data?

Distributed processing basics

Spark ecosystem overview

Spark architecture: driver, executors, cluster overview

SparkSession

PySpark setup overview

Reading and writing data

Creating DataFrames

select, filter, withColumn, groupBy, joins, sorting, aggregations

Transformations vs actions

Lazy evaluation

Narrow vs wide transformations overview

Partitioning concepts

Repartition vs coalesce overview

Broadcast join overview

Caching and avoiding unnecessary shuffles

Writing results in CSV and Parquet

Cloud/object storage concept

Outcome

You will understand PySpark basics and perform common transformations used in real data pipelines.

Module 7

Week 12 - Week 13

Cloud Data Engineering

Understand how cloud services are used in modern Data Engineering pipelines, with Azure as the primary focus and AWS/GCP awareness.

Why cloud is important for Data Engineering

Storage, compute, networking, IAM/RBAC, and cost awareness

Azure Blob Storage and Azure Data Lake concept

Azure Data Factory pipelines

Copy activity, triggers, linked services, datasets

Azure Databricks overview

Azure Synapse Analytics overview

Azure Functions overview

Azure Monitor and RBAC/IAM basics

AWS awareness: S3, Lambda, Glue, Athena, Redshift, IAM, CloudWatch

GCP awareness: GCS, Cloud Functions, BigQuery, Dataflow, Pub/Sub, Cloud Composer, IAM

Cloud data pipeline architecture thinking

Outcome

You will understand how cloud components are used to design and run Data Engineering pipelines.

Module 8

Week 14

Orchestration with Airflow

Learn how production data pipelines are scheduled, monitored, and managed using orchestration concepts.

What is orchestration?

Why scheduling is needed

Airflow overview

DAGs, operators, tasks, and scheduling

XCom basics

Connecting to APIs

Connecting to databases

Retry and failure handling

Pipeline dependency management

Designing a simple DAG flow

Outcome

You will understand how production pipelines are scheduled and managed using orchestration tools like Airflow.

Module 9

Week 15

Real-World Projects and Portfolio Building

Build practical project direction and learn how to create proof of work through GitHub, README files, and portfolio-ready case studies.

Project 1: End-to-end data pipeline using API source, SQL database, extraction, transformation, scheduling, and output storage

Project 2: Cloud data pipeline using cloud storage, PySpark processing, and analytics layer concept

Project 3: SQL business analytics project with insights and reporting-ready output

Project 4: PySpark transformation project with raw data, cleaning, transformation, aggregation, and Parquet output

Project 5: Portfolio case study project with problem statement, architecture diagram, GitHub repository, README, resume bullets, and interview explanation

How to structure GitHub repositories

How to write strong README files

How to create architecture diagrams

How to explain projects in interviews

How to add projects to resume

Outcome

You will understand how to build and present beginner-friendly Data Engineering projects for your portfolio.

Module 10

Week 16

Resume, LinkedIn, GitHub, and Interview Preparation

Prepare your profile, resume, GitHub, and interview strategy to present your skills confidently.

1-page resume structure

Skills section and project section

Writing strong resume bullet points

Avoiding fake skills

GitHub portfolio structure

README writing and project documentation

Architecture diagrams

LinkedIn headline, About section, skills, featured projects

SQL interview preparation

Python interview preparation

Data modeling questions

ETL design questions

Cloud basics

Spark/PySpark questions

Scenario-based questions

Project explanation

Tell me about yourself

Career switch explanation

Outcome

You will know how to present your skills, projects, and preparation confidently for Data Engineering interviews.

Real-World Projects and Portfolio Direction

The goal is not only to learn tools, but also to build practical proof of work through projects, GitHub repositories, README files, architecture diagrams, and interview-ready explanations.

End-to-End Data Pipeline: API source → SQL database → extraction → transformation → scheduling → output storage

Cloud Data Pipeline: cloud storage → PySpark processing → analytics layer concept

SQL Business Analytics Project: business dataset → SQL queries → insights → reporting-ready output

PySpark Transformation Project: raw data → cleaning → transformation → aggregation → Parquet output

Portfolio Case Study Project: problem statement → architecture diagram → GitHub repo → README → resume bullets

Bootcamp Outcome

What You Can Expect

By the end of this bootcamp, learners will have a clear roadmap, practical project direction, and better interview preparation confidence.

Understand how Data Engineering works in real companies

Write SQL queries confidently for interviews and projects

Use Python for files, APIs, automation, validation, and data processing

Understand data modeling, warehousing, ETL, ELT, and data quality

Work with PySpark basics and big data transformations

Understand Azure-based data pipeline components

Understand Airflow-based pipeline orchestration

Build beginner-friendly Data Engineering projects

Create GitHub portfolio-ready repositories

Prepare better for Data Engineering interviews

Frequently Asked Questions

Is this bootcamp beginner-friendly?

Yes. It is designed for freshers, beginners, manual testers, support engineers, analysts, and career switchers who want structured direction in Data Engineering.

What is the duration of the bootcamp?

The planned learning structure is 16 weeks. Each module has a timeline so beginners can clearly understand how the learning journey is organized.

Does this course guarantee a job?

No. This is not a job guarantee program. The goal is to help you become job-ready through structured learning, practical projects, interview preparation, and honest mentorship.

Will projects be included?

Yes. The bootcamp includes project and portfolio direction with beginner-friendly Data Engineering projects, GitHub structure, README guidance, architecture diagrams, and resume/project explanation support.

Which cloud is covered?

Azure is the primary focus because many Data Engineering roles use Azure services. AWS and GCP awareness are also included so learners understand cloud concepts across platforms.

Who should join this program?

This is suitable for freshers, final-year students, manual testers, support engineers, analysts, and anyone planning to switch into Data Engineering.

Ready to Start Your Data Engineering Journey?

Join the bootcamp if you want a structured path, detailed syllabus, practical project direction, and honest guidance to move closer to Data Engineering job-readiness.

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