LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional

Live 169+ learners enrolled in cohort 1 & 2

From Data Analyst to Data Engineer in 10 Weeks.

Cohort 2 · Started Saturday, 29th Aug, 2026. Enrollments are still open. Transform from a Data Analyst into an AI-Enabled Data Engineer through Codebasics' live 10-week cohort designed for working professionals. Learn advanced SQL, PySpark, dbt, Airflow, Microsoft Fabric, CI/CD, and production-grade data engineering workflows while building and shipping a complete end-to-end pipeline. Classes run live every Saturday and Sunday, 4 to 7 PM IST, fully interactive, with hands-on labs and real-time Q&A, and recordings available for revision. Sessions 1 and 2 are already recorded and available the moment you enrol.

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Weeks

Live Sessions

10+

Tools & Platforms

Capstone Layers

1 Year

Access to Class Recordings

81+

Enrolled Learners

LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional

US$840

Created by :

Dhaval Patel & Hemanand Vadivel Kirandeep Marala

Join our Bootcamp

Learn In-demand Skills

Get Hired by Top Companies

Download Brochure

Weeks

Live Sessions

10+

Tools & Platforms

Capstone Layers

1 Year

Access to Class Recordings

81+

Enrolled Learners

What Makes This Bootcamp Different?

100% LIVE, instructor-led sessions across 10 weeks. Real-time Q&A, live walk-throughs, and direct doubt-clearing with the faculty.
Built to make you the end-to-end data person on your team - the one who builds the pipeline, models the data, and ships the report.
Covers the FULL Modern Data Engineering spectrum, from Advanced SQL and PySpark to Databricks, Microsoft Fabric, dbt, Airflow, Kafka streaming, and CI/CD - the complete production stack.

Designed and taught by data industry experts & engineering leaders with real-world experience building and shipping production data systems at scale.
Master the complete modern DE stack: Python, PySpark, Delta Lake, Databricks, Microsoft Fabric, dbt, Airflow, Kafka, ADF, GitHub Actions, Power BI & more.
Production-first mindset: Spark internals, OPTIMIZE & ZORDER, schema evolution, idempotency, CI/CD, observability - the engineering practices that matter in production data systems.
Streaming engineering with Kafka, Structured Streaming, watermarking, windowing, and event-time processing - the patterns Indian product teams run today.
AI-Assisted Data Engineering: Copilot, Cursor, and Claude Code for SQL, dbt, PySpark, and Airflow - the productivity patterns top DE teams are adopting now.
End-to-end capstone integrating 8 production layers- API ingestion, lakehouse, transformation, orchestration, CI/CD, monitoring, and Power BI. One artefact recruiters read in five minutes.

Hear It From

Our Happy Learners

Our content is rated 4.9/5 from 18571+ Learners

Jorge Daniel Carreon Guzman

Data scientist

SQL

Miguel Ángel Hernández

SQL

Sebastian Torres Franco

Professional expert in energy planning

SQL

Faizal Ahmed

Sr Manager, Business Analytics

Python

Abhishek Rajeev

Python

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Some things arrive at exactly the right moment. Codebasics launching its live cohorts was one of them for me, landing just as I set out to become a full-stack data professional. My story with Codebasics actually began with their Power BI course, which quietly shaped everything I do today, so I walked into the Data Engineering for Data Analysts cohort with high expectations. It is exceeding them, session after session.

Haroon teaches like a practitioner, not a lecturer. He shows us what data engineers really do in production, the messy, decision-heavy reality rather than tidy textbook examples. Every session is three focused hours of pure signal, no filler. So far we have journeyed through modern SQL (window analytics, recursive CTEs, MERGE-based change loading, execution-plan tuning), dimensional modeling and warehouse engineering (star schemas, SCD Type 2, partitioning), production and advanced Python, PySpark on Databricks, Delta Lake and lakehouse engineering, and orchestration with Azure Data Factory, with a few sessions still ahead. The assignments feel handcrafted; each one snaps neatly onto the classwork and cements it. And the capstone project is a genuine gem, pulling the entire curriculum into one realistic, end-to-end challenge.

Behind the scenes, Kirandeep runs the cohort with a care and precision that makes everything feel effortless for the participants. I am equally grateful to Dhaval Patel and Hemanand Vadivel, the cofounders of Codebasics, who have been mentors and good friends, and whose vision set all of this in motion.

This cohort is steadily carrying me from the consumption layer up into the engineering layers I once only admired from below. If you are a BI or analytics professional serious about going full-stack, I recommend the Codebasics DE for DA bootcamp without a second thought. Thank you, Haroon, Kirandeep, Dhaval, Hemanand, and the entire Codebasics team.

Ashish Babaria

Product Consultant

LIVE DE Bootcamp: Batch 1

Landed a Job

I really enjoyed the LIVE Data Engineering Bootcamp for Analysts. The best part of the course was definitely Harun. His depth of knowledge is incredible, and he doesn't just teach how things work, he explains why they work that way. That made it much easier to understand concepts that initially felt overwhelming.

Another thing I appreciated was the Codebasics team. During the live cohort, they were always available to answer questions, so the class didn't have to stop every few minutes for doubts. That kept the sessions running smoothly while making sure everyone's questions were answered.

One thing I realized during this cohort is that Data Engineering is not as easy as Data Analytics. As someone transitioning from a Tableau and Data Analytics background, I found that having a strong foundation in SQL and at least the basics of Python makes a huge difference. My suggestion to future learners is to complete the Codebasics Data Engineering Bootcamp first. If that's not possible, at least become comfortable with SQL and Python before joining the live cohort. Since each three hour class covers a major topic, the pace can feel fast if you are completely new to these technologies.

That said, the support system is excellent. We have recorded sessions, an active Discord community where learners help each other, and the instructors genuinely listen to feedback. There were times when many of us felt the pace was a little too fast, and the team actually slowed things down after listening to our feedback. That showed they genuinely care about helping students learn instead of just finishing the syllabus.

Overall, I had a great learning experience. The journey was challenging, but it was absolutely worth it. If you are serious about moving from Data Analytics to Data Engineering, I would definitely recommend this cohort. It gives you the right guidance, practical learning, and a supportive community to help you make that transition.

Jaideep Gupta

Tableau Developer

LIVE DE Bootcamp: Batch 1

Landed a Job

With seven years in data engineering, I wanted to strengthen my fundamentals, learn modern DE technologies, and build interview confidence, ideally through a practical, production-focused crash course rather than a purely theoretical one. The CodeBasics Data Engineering Bootcamp matched that exactly, with its syllabus and focus on building a real, production-ready ETL pipeline.
Here's my genuine feedback.

Teaching Style: Concepts were taught with real context, why, where, and how they're used in production, not just theory.

Learning Material: The learning material is excellent and well organized. We received live session recordings, concise key notes, and detailed material, plus compiled Q&A from live chats, sample code for topic discussed with brief notes. Recoded sessions are also available technology wise.

Assignments: Designed around real-world scenarios, which made them especially valuable for interview prep.

Interview-Readiness Tracker: A standout feature, giving a clear checklist across topics like SQL and DE concepts, so I always knew what to revise and what was already covered.

Quizzes: Regular quizzes kept things engaging with healthy competition, and answer PDFs helped us pinpoint exactly where we went wrong.

Team Support: The instructor was patient and thorough, and both the instructor and support team made sure no question went unanswered.

Discord Community: A great space for ongoing discussions, technical questions, and connecting with fellow learners outside class.

Flexibility: The team genuinely listened to feedback after sessions and acted on it, the interview tracker itself was born from a suggestion.

Understanding: They extended the capstone submission timeline knowing everyone was balancing full-time jobs, and built in a week off between sessions to let us catch up and connect the dots, rather than just rushing through the syllabus.

Overall, this bootcamp delivered exactly what I needed: structured, practical, interview-focused learning that strengthened my fundamentals and boosted my confidence for both real-world projects and interviews. I'd highly recommend it to anyone looking to upskill in data engineering.

Special thanks to Harun and Kiran for their support throughout the course, to Hem and Varun for the stakeholder management, personal branding and interview-specific sessions, and to Dhaval for building such an excellent platform for real-time learning.

Shalini Goutam

Data Engineer

LIVE DE Bootcamp: Batch 1

An Excellent Journey into Modern Data Engineering

The AI Data Engineering Bootcamp has been an excellent and highly practical learning experience. It covered everything from SQL, Python, data modelling and warehousing to PySpark, Delta Lake, Lakehouse, Azure Data Factory, Microsoft Fabric, dbt, CI/CD and end-to-end data engineering.
The progression from fundamentals to real-world engineering practices made the learning journey both structured and impactful.

A special shoutout to Harun, who was an outstanding instructor. His ability to simplify complex concepts, patiently address questions, and ensure everyone understood before moving forward made a huge difference.

And a big thank you to Kiran from Codebasics team for the incredible coordination throughout the cohort. Managing a diverse group of learners, coordinating with the management team, resolving questions promptly, and keeping everything running smoothly was truly commendable.

Overall, a fantastic experience that has strengthened my data engineering skills and given me greater confidence to work with modern data platforms. Highly recommended!

Nivetha Anand

Data and AI consultant

LIVE DE Bootcamp: Batch 1

The Codebasics Cohort Training on DA to DE has Surpassed my Expectation. I Would like to Mention few things here
Harun - Has Great Skills on Multiple Technologies and Great at Training ,
Kiran - As a back up or Leading the Chat and Providing all the related Content and looking after Issues was in Sync with the Cohort.

The Concepts Explanations and Real world Scenarios were on Point.
Although i had personal Commitments and could not attend sometime, Every Class is Recorded and uploaded in 24 hours which is great boon.

I Appreciate the Patience the Team had and hoping to Continue with them for other learnings also.

Thank you Soo much Codebasics Team.

Suchit Kumar B

Principal Data Architect

LIVE DE Bootcamp: Batch 1

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Overview

What you'll learn in
this Live Data Engineering for Data Analyst Bootcamp

Week-1: Foundations & Advanced SQL
SQL Engineering

Session 1 - SQL for Modern Data Engineering, Part 1

Joins beyond INNER and OUTER · Join algorithms: nested loop, hash, merge · Anti-joins · Window functions deep dive · Partition, order and frame · Ranking and running totals

Output: A ranking and running totals query pack on a sample warehouse
Session 2 - SQL for Modern Data Engineering, Part 2

Recursive CTEs · MERGE statements · Incremental loading patterns · CDC concepts · Query execution plans · Warehouse optimisation

Output: An incremental load built with MERGE and tuned from the execution plan

Week-2: Modelling & Production Python
Modelling & Python

Session 3 - Data Modelling & Warehouse Engineering

OLTP vs OLAP · Star and snowflake schema · Fact vs dimension tables · SCD Type 1 and Type 2 · Partitioning strategies · Medallion architecture · Data contracts

Output: A dimensional model with SCDs on a Bronze, Silver, Gold layout
Session 4 - Production Python for Data Engineers

Modular Python architecture · OOP for pipelines · Config-driven frameworks · Logging and exception handling · Retry mechanisms · Environment management · Secrets handling

Output: A config-driven Python pipeline framework with logging and retries

Week-3: Python at Scale & Spark
Python & PySpark

Session 5 - Advanced Python Data Processing

APIs and ingestion patterns · Async processing · Parallel execution · File streaming · Memory optimisation · Testing with pytest · Packaging basics

Output: A tested async ingestion job that streams large files without blowing memory
Session 6 - PySpark Deep Dive

Spark architecture · Executors and DAGs · Lazy evaluation · Partitioning · Broadcast joins · Shuffle optimisation · Spark UI analysis · Caching strategies

Output: A Spark job you have tuned yourself using the Spark UI

Week-4: Lakehouse & Azure
Delta Lake & Azure

Session 7 - Delta Lake & Lakehouse Engineering

Delta internals · ACID transactions · OPTIMIZE and ZORDER · Time travel · Schema evolution · Change Data Feed · Incremental ETL · Bronze, Silver, Gold

Output: An incremental ETL pipeline running on Delta Lake
Session 8 - Azure Data Engineering Stack

ADLS Gen2 · Event Hubs · Key Vault · Managed identities · Integration Runtime · Networking basics · Synapse vs Databricks vs Fabric

Output: A secured Azure data landing zone using Key Vault and managed identities

Week-5: Orchestration & Fabric
Pipelines & Fabric

Session 9 - Enterprise Data Pipelines

Azure Data Factory · Fabric Pipelines · Databricks Workflows · Metadata-driven pipelines · Config-based orchestration · Parameterisation · Reusable frameworks

Output: A metadata-driven, parameterised orchestration pipeline
Session 10 - Microsoft Fabric Engineering

OneLake · Lakehouse and Warehouse · Fabric Data Factory · Eventstream · Real-Time Intelligence · DirectLake · Fabric governance

Output: An end-to-end Fabric solution with Direct Lake reporting on top

Week-6: Connecting the Stack
Architecture Recap

Session 11 - Connecting the Dots

End-to-end view of the stack so far · How the pieces fit together · Architecture recap · Trade-offs between platform options · Concept clarity and doubt clearing

Output: One architecture diagram of the full stack, explained in your own words

Week-7: Transformation & Delivery
dbt & CI/CD

Session 12 - dbt Core Fundamentals

Models and sources · refs() · Materialisations · Snapshots · Incremental models · Tests · Documentation

Output: A tested, documented dbt project with incremental models and snapshots
Session 13 - CI/CD & Reliability Engineering, plus Capstone Introduction

Git branching strategies · GitHub Actions · Automated testing · Deployment pipelines · Monitoring and freshness checks · Cost optimisation · Incident management · Capstone brief and assessment criteria

Output: A CI/CD workflow with automated tests and freshness checks, plus your capstone brief

Week-8: Communication & Positioning
Stakeholders & Branding

Session 14 - Stakeholder Management & Personal Branding

Stakeholder communication · Framing requirements · Explaining technical work to non-technical audiences · Personal branding · Building online credibility as a data engineer

Output: A stakeholder-ready update on your own work and a refreshed professional profile

Week-9: Analytics Engineering, Airflow & Streaming
Airflow & Streaming

Session 15 - Capstone Jamming Session

Implementation questions on your capstone · Design review · Debugging together · Unblocking pipeline issues · Peer feedback

Output: Your capstone unblocked, with a clear next step
Session 16 - Advanced Analytics & Apache Airflow Engineering

Macros and Jinja · Semantic layer · MetricFlow · SQLFluff · Lineage · Data quality frameworks · Governance · DAG architecture · Dynamic DAGs · Sensors · XCom · Scheduling and monitoring · Retry patterns · Failure handling

Output: A semantic layer with lineage, plus scheduled Airflow DAGs that survive failures
Session 17 - Streaming Data Engineering

Kafka fundamentals · Event-driven architecture · Structured Streaming · Watermarking and windowing · Event-time processing · CDC streaming · Event Hubs integration

Output: A streaming pipeline that handles late-arriving data with watermarks

Week-10: Interview Readiness & Showcase
Interviews & Demo Day

Session 18 - Interview Prep & System Design

SQL interview rounds · PySpark interview questions · Data modelling rounds · System design · Resume transformation · LinkedIn optimisation · Mock interviews

Output: A DE-positioned resume and profile, tested in a mock interview
Session 19 - Final Demo & Graduation

Capstone demo covering API ingestion · Lakehouse architecture · PySpark transformations · dbt modelling · Airflow orchestration · CI/CD · Power BI reporting · Monitoring

Output: One production-grade pipeline repo covering all eight layers, ready for a hiring manager to read in five minutes

This cohort includes Data Engineering Bootcamp 1.0 worth US$240

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The DE Promise

Build & Ship Production Data Pipelines in 10 Weeks.

Not a tutorial. A working end-to-end pipeline defended in front of mentors.

What you get	Self-study	Other live bootcamps	Our live cohort
Live, mentor-led sessions
Real projects shipped to GitHub	Rarely	Sometimes	1 pipeline repo
The current 2026 Cloud stack	On your own	Often outdated
Job assistance
Investment	Your time	₹2,00,000+	US$840

May we help you?

Frequently Asked
Questions

Q.1 When does the bootcamp officially start?

The bootcamp officially commences on Saturday, 29th August 2026.

Q.2 When do I get access after enrolling as an Inner Circle member?

Access is immediate. The moment you enrol you get the recordings of every session already delivered, the materials, and Discord access. Live sessions continue every Saturday and Sunday.

Q.3 What is the Inner Circle, and how is it different from regular enrollment?

The Inner Circle was early enrollment at a reduced price and closed on 2nd September 2026. Standard pricing now applies and enrollment closes on 5th September 2026. Learners who enrolled before the bootcamp began on 29th August also received a short form asking which tools and gaps matter most in their work, and those inputs shaped what this cohort covers.

Q.4 Do I also get the Data Engineering Bootcamp 1.0?

Yes. Every enrollment includes full access to the Data Engineering Bootcamp 1.0 at no extra cost. It includes Job Assistance, Live Problem Solving, and a Virtual Internship. You get both for the price of one.

Q.5 When are the live sessions?

Saturdays and Sundays, 4 to 7 PM IST. Sessions are fully live and interactive with hands-on labs and real-time Q&A. Recordings are available for revision.

Q.6 What if I miss a live session?

All live sessions are recorded and available within 24 hours. You can catch up at your own pace, though live attendance is strongly recommended as the labs and discussions are where most of the real learning happens.

Q.7 What happens after the 10 weeks? Do I lose access?

No. You keep access to all session recordings for 1 year from the bootcamp start date.

Q.1 Do I need prior data engineering experience?

No. This bootcamp is built for working data analysts who want to cross into data engineering. If you have at least 1 year of analyst experience and are comfortable with SQL, you are ready.

Q.2 I am a fresher with no work experience. Can I join?

We strongly advise against it. This bootcamp moves fast and assumes analyst-level SQL fluency and data literacy. If you are starting from zero, the Codebasics Data Analytics Bootcamp is the right first step. Build that foundation and come back.

Q.3 Who is this bootcamp designed for?

Working data analysts, BI developers, and business analysts with 1 to 4 years of experience who want to own the full data stack, not just the dashboard layer. If you already work as a data engineer, this bootcamp is likely below your current level.

Q.4 I am enrolling after the cohort has started. What do I get access to?

Everything unlocks the same day you enrol: the Session 1 recording delivered on 29th August, all code, notebooks and materials from those sessions, Discord access so you can ask catch-up questions straight away, and every live session from 5th September onward on Saturdays and Sundays, 4 to 7 PM IST. You are a couple of sessions behind out of nineteen across a ten-week programme, so there is room to catch up.

Q.1 How do I get help if I am stuck?

Every enrolled learner gets access to the Discord community where you can ask questions, connect with fellow learners, share progress, and learn from each other throughout the bootcamp. The mentor team also provides weekly hands-on lab support.

Q.2 Is there job assistance?

The Data Engineering Bootcamp 1.0 included with your enrollment has dedicated job assistance. The bootcamp itself focuses on building your skills, shipping a production-grade capstone on GitHub, and preparing you for system design interviews with the core faculty.

Q.1 What is the Inner Circle price and when does it close?

Inner Circle pricing is open until 2nd September 2026. Standard pricing of ₹48,000 applies from 3rd September. Enrollment for Cohort 2 closes on the morning of 5th September 2026 and does not reopen.

Q.2 I already own the Data Engineering Bootcamp 1.0. What do I pay?

The amount you paid for the Data Engineering Bootcamp 1.0 is fully adjusted and deducted from your enrollment fee.

Q.3 I bought only some individual courses from the Data Engineering Bootcamp 1.0. What do I pay?

The amount you paid for those individual courses is deducted from your enrollment fee.

Q.4 Has the Inner Circle deadline changed?

Yes. Inner Circle pricing is now open until 2nd September 2026, extended from the original 25th August. Most of our learners are working professionals whose salaries arrive at the end of the month, and a deadline on the 25th asks people to make a considered decision in the week they have the least room to make it. The price is unchanged for everyone. Nobody who enrolled before 25th August has paid more than somebody enrolling on 2nd September.

Q.5 Our ad mentions 25th August as the Inner Circle closing date. Which is correct?

2nd September 2026 is correct. The Inner Circle was originally set to close on 25th August and has since been extended. Some advertising created before the extension still shows the earlier date. You get the Inner Circle price on any enrollment completed on or before 2nd September.

Q.6 When does enrollment for Cohort 2 close?

There are two key dates. Inner Circle pricing of ₹36,000 is open until 2nd September 2026. From 3rd September the price is ₹48,000. Enrollment closes on the morning of 5th September 2026 and does not reopen, because Cohort 2 has already started.

Q.1 I used a subsidy and now want to refund this Bootcamp itself. What happens?

You get back the amount you actually paid for this Bootcamp. Your original purchase stays intact and you keep access to it.

Q.2 I used a subsidy (my existing Data Engineering Bootcamp or individual course purchase). Can I refund my original purchase after enrolling?

No. Once your existing purchase is applied as a subsidy to reduce your price, that original purchase becomes non-refundable.

Q.2 I used a subsidy (my existing Data Engineering Bootcamp or individual course purchase). Can I refund my original purchase after enrolling?

No. Once your existing purchase is applied as a subsidy to reduce your price, that original purchase becomes non-refundable.

LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional

From Data Analyst to Data Engineer in 10 Weeks.

Join our Bootcamp

Learn In-demand Skills

Get Hired by Top Companies

What Makes This Bootcamp Different?

Our Happy Learners

What you'll learn in this Live Data Engineering for Data Analyst Bootcamp

Build & Ship Production Data Pipelines in 10 Weeks.

Frequently AskedQuestions

Features and Course Content

Eligibility

Job Assistance and Support

Payment Options

Refund Policy

Features and Course Content

Eligibility

Job Assistance and Support

Payment Options

Refund Policy

What you'll learn in
this Live Data Engineering for Data Analyst Bootcamp

Frequently Asked
Questions