Live cohort 3 Built for working data analysts

From dashboards to data pipelines: become a data engineer in 11 weeks.

Eleven weeks, live, on weekends. You will write warehouse-grade SQL, turn scripts into production Python, tune Spark on Databricks, run a lakehouse on Azure and Fabric, test it with dbt, schedule it with Airflow, ship it through CI/CD, and present one pipeline repo that does all of it.

Enroll now → Download the Brochure

Cohort 3 begins Saturday, 31^st October. 20 live sessions, Saturdays and Sundays, 5:00 to 8:00 PM IST.

Inner circle price

US$630

Closes Tuesday, 27^th October. After that the price is US$840.

Already own a Codebasics data engineering bootcamp? You pay the difference · Enrollment closes Saturday, 7^th November.

11 weeks

Live, with the Diwali weekend off

31^st October to 16^th January

20 live sessions

Three hours each, 5:00 to 8:00 PM IST

Recorded, with 1 year of access

30 minutes

Of doubt clearing in every session

Plus a midweek capstone session

1 capstone

One pipeline, eight layers

Presented on 16^th January

1.5M+

YouTube Subscribers

742K+

Learners

86K+

Paid learners

7000+

5 Star Reviews

Our promise

In 11 weeks, this is who you become.

Today you pull data someone else prepared.Week 11: you build the pipeline that prepares it.
Today your SQL answers one question.Week 11: you write MERGE, incremental loads and CDC that keep a warehouse current.
Today Spark and Databricks are lines on a job post.Week 11: you tune a Spark job by reading the Spark UI.
Today your report breaks when a source changes.Week 11: your pipeline is tested, scheduled, and tells you first.
Today you hand requirements to engineering.Week 11: you design the lakehouse on Azure and Fabric and defend the trade-offs.

The 11 weeks

Eleven weeks from now, you are the one who fixes the pipeline.

Scroll. Each week adds one thing you can build yourself.

What you can ownWk 1

1 Week 1 · Foundations

Write the SQL a warehouse runs on.

Semi and anti joins, the join algorithms an engine picks, window functions, then recursive CTEs, MERGE upserts, incremental loads and change data capture.

Ends with: SQL you can defend by reading the execution plan

2 Week 2 · Festival break

Diwali weekend. No sessions.

Rest, enjoy the festival, and pick up any pending practice from the first two sessions at your own pace.

Ends with: Sessions 1 and 2 practice finished, ready for Week 3

3 Week 3 · Foundations

Design the model before the pipeline.

Declare the grain, build star and snowflake schemas, handle SCD Types 1 and 2, then turn a script into config-driven Python with logging, retries and secrets.

Ends with: a dimensional model and production-grade pipeline code

4 Week 4 · Foundations

Move data fast, then see what Spark did.

Real APIs, pagination, async and parallel work, files bigger than memory, then PySpark on Databricks and the Spark UI to find skew and spill.

Ends with: tested ingestion code and a Spark job you tuned yourself

5 Week 5 · Foundations

Explain it, then plan your route.

Stakeholder communication and personal branding with templates you can use straight away, then an open career guidance session where the agenda is your questions.

Ends with: a LinkedIn you are proud of and a straight answer to your own career question

6 Week 6 · Platforms and Cloud

Make it production ready on the cloud.

Delta tables with time travel, OPTIMIZE and ZORDER, incremental MERGE across bronze, silver and gold, then ADLS Gen2, Event Hubs, Key Vault and managed identities.

Ends with: a lakehouse on Azure and a reasoned platform choice

7 Week 7 · Platforms and Cloud

Run hundreds of tables from one control table.

Metadata-driven Data Factory pipelines with parameters, triggers and a deliberate failure path, then an end-to-end Microsoft Fabric build with DirectLake.

Ends with: orchestration that scales past one hard-coded pipeline

8 Weeks 8 to 9 · Transformation and Delivery

Test it, schedule it, ship it.

A tested, documented dbt project, an Airflow DAG that retries and alerts, then GitHub Actions with automated checks and freshness monitoring.

Ends with: a pipeline that tells you it broke before a stakeholder does

9 Weeks 10 to 11 · Production and Launch

Stream it, interview for it, demo it.

Kafka and Structured Streaming into Delta, interview prep that starts from your own resume, a midweek capstone jamming session, then your final demo.

Ends with: one production-grade pipeline repo, presented and graduated

What you leave with

Eight things you build, and one pipeline you present.

Every build adds one skill the capstone needs, so by the time you get the brief you are assembling what you already know, not starting from scratch.

Build 1

Warehouse-grade SQL

MERGE upserts, watermark-based incremental loads and CDC on AdventureWorks, tuned by reading the execution plan.

Build 2

A dimensional model

Grain declared, facts and dimensions split, SCD Type 2 handled, laid out across medallion layers.

Build 3

Config-driven Python pipeline

Packaged, logged, retried and tested with pytest, with secrets handled properly from the start.

Build 4

A tuned Spark job

PySpark on Databricks, fixed with partitioning, broadcast joins and caching after reading the Spark UI.

Build 5

Incremental lakehouse ETL

Delta tables with ACID, time travel, Change Data Feed and MERGE across bronze, silver and gold.

Build 6

Metadata-driven orchestration

One control table driving many tables through Data Factory, with triggers and a failure path.

Build 7

End-to-end Microsoft Fabric

OneLake, Fabric Data Factory, Eventstream and a DirectLake dashboard on one copy of the data.

Build 8

Tested dbt project + Airflow DAG

Sources, staging, snapshots and incremental marts in dbt, scheduled by an Airflow DAG that retries and alerts.

Capstone

One production-grade pipeline repo

Ingestion, lakehouse, PySpark, dbt, Airflow, CI/CD, monitoring and Power BI in one repo you present at the final demo.

The stack

The tools data engineering teams run in production.

ADLS Gen2 + Key Vault Storage and secrets

Apache Airflow Scheduling

Apache Kafka Streaming

Azure Data Factory Orchestration

Azure Event Hubs Event ingestion

Databricks Lakehouse platform

dbt Core Transformation

Delta Lake ACID tables

Docker Local environments

GitHub Actions CI/CD

Microsoft Fabric Lakehouse and pipelines

Power BI · Reporting Reports and verified DAX

PySpark Distributed processing

Pytest Testing

Python The language of every build

SQL Server Warehouse SQL

How the capstone runs

A thread, not a last-minute assignment.

So there is no confusion about when the capstone happens, here is the full sequence.

20th Dec

Session 14 hands you the brief and how you will be assessed, with the holidays as runway

6th Jan

Midweek jamming session, 7:30 to 9:00 PM IST, to get unstuck on your own pipeline

9th & 10th Jan

No sessions. The weekend is kept free to finish, test and document

16th Jan

Final demo and graduation, 8:00 to 9:30 PM IST

11 weeks. 20 live sessions. One pipeline you ship.

Saturdays and Sundays, 5:00 to 8:00 PM IST. Open any week to see every session.

SQL for Modern Data Engineering

Session 1 - SQL for Modern Data Engineering - Part 1

Joins beyond INNER & OUTER · join algorithms (nested loop · hash · merge) · anti-joins · window functions deep dive · partition/order/frame · ranking & running totals

Session 2 - SQL for Modern Data Engineering - Part 2

Recursive CTEs · MERGE statements · incremental loading patterns · CDC concepts · query execution plans · warehouse optimisation

Festival Break

Festival Break: Diwali

No sessions. Diwali weekend

Data Modelling and Production Python

Session 3 - Data Modelling & Warehouse Engineering

OLTP vs OLAP · star & snowflake schema · fact vs dimension tables · SCD Type 1 & 2 · partitioning strategies · medallion architecture · data contracts

Session 4 - Production Python for Data Engineers

Modular Python architecture · OOP for pipelines · config-driven frameworks · logging & exception handling · retry mechanisms · environment management · secrets handling

Advanced Python and PySpark

Session 5 - Advanced Python Data Processing

APIs & ingestion patterns · async processing · parallel execution · file streaming · memory optimisation · testing with pytest · packaging basics

Session 6 - PySpark Deep Dive

Spark architecture · executors & DAGs · lazy evaluation · partitioning · broadcast joins · shuffle optimisation · Spark UI analysis · caching strategies

Stakeholders and Career Guidance

Session 7 - Stakeholder Management + Personal Branding

Stakeholder communication · framing requirements · explaining technical work to non-technical audiences · personal branding · building online credibility as a data engineer

Session 8 - Career Guidance (Open Q&A)

Open career question and answer. Where you are now and where data engineering fits · choosing a direction · moving into engineering inside your current company versus switching · career gaps and transferable skills · realistic timelines · what to do when applications go quiet · and whatever else learners bring

Delta Lake and the Azure Stack

Session 9 - Delta Lake & Lakehouse Engineering

Delta internals · ACID transactions · OPTIMIZE & ZORDER · time travel · schema evolution · Change Data Feed · incremental ETL · Bronze/Silver/Gold

Session 10 - Azure Data Engineering Stack

ADLS Gen2 · Event Hubs · Key Vault · managed identities · Integration Runtime · networking basics · Synapse vs Databricks vs Fabric

Enterprise Pipelines and Microsoft Fabric

Session 11 - Enterprise Data Pipelines

Azure Data Factory · Fabric Pipelines · Databricks Workflows · metadata-driven pipelines · config-based orchestration · parameterisation · reusable frameworks

Session 12 - Microsoft Fabric Engineering

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

dbt and Connecting the Dots

Session 13 - dbt Core Fundamentals

Models & sources · refs() · materialisations · snapshots · incremental models · tests · documentation

Session 14 - Connecting the Dots + Capstone Introduction

End-to-end view of the stack so far · how the pieces fit · architecture recap · trade-offs between platform options · concept clarity & doubt clearing · capstone brief & assessment criteria

Airflow and CI/CD

Session 15 - Apache Airflow Engineering

Airflow mental model · DAGs · tasks & operators · TaskFlow API · anatomy of a DAG file · scheduler & executor · start_date · schedule intervals & catchup · XCom · retries & alerting · connections for secrets · monitoring · top-level code traps

Session 16 - CI/CD & Reliability Engineering

Git branching strategies · GitHub Actions · automated testing · deployment pipelines · monitoring & freshness checks · cost optimisation · incident management

Streaming, Interviews and Capstone Jamming

Session 17 - Streaming Data Engineering

Kafka fundamentals · event-driven architecture · Structured Streaming · watermarking & windowing · event-time processing · CDC streaming · Event Hub integration. Capstone work continues alongside

Session 18 - Winning Data Engineering Interviews

The reframe (depth beats tricks) · your resume as the question paper · the intersection framework · thinking like the client · answering like a senior · the non-negotiables they always test (SQL & data modelling) · reverse interviewing · multi-round strategy · using AI as a sparring partner · handling what you do not know · the 7-day prep playbook

Session 19 - Capstone Jamming Session (midweek, 7:30 to 9:00 PM IST)

Implementation questions on the capstone · design review · debugging together · unblocking pipeline issues · peer feedback

Capstone Build Week

No Cohort 3 session. Capstone build time

No sessions, time to finish, test and document your capstone.

Capstone Build and Graduation

Session 20 - Final Demo + Graduation (evening, 8:00 to 9:30 PM IST)

Capstone demo covering data ingestion · lakehouse architecture · PySpark transformations · dbt modelling · Airflow orchestration · CI/CD · Power BI reporting and the monitoring layer

Who is in the room

Taught by people who build and run production data platforms.

Dhaval Patel

Founder, Codebasics · Ex-NVIDIA

Curriculum advisor. Sets what this cohort teaches and what it refuses to.

Hemanand Vadivel

CEO, Codebasics

Leads most sessions and every leadership and management block.

Kirandeep Marala

Analytics Engineer and Programme Manager, Codebasics

Plans every session, builds the assignments, and evaluates what you ship.

Harun Raseed Basheer

Team Lead, Data Engineering, Hitachi Solutions

Leads every technical session, from SQL execution plans in week one to Kafka in week ten. Teaches on the whiteboard first and in code second, then stays for the doubt clearing.

What learners say

From the analysts who went before you.

Every review below is published by a real Codebasics learner on our platform.

Cohort 1 reviews Verified

Published by learners on the Codebasics platform.

Akhil K J

Cohort 1

★★★★★

Hands down the best cohort I’ve attended so far! Every session was packed with value, unlike many bootcamps that focus on generic, basic knowledge. Harun is technically deep and made the sessions highly interactive. The assignments were well-designed to track progress, and the capstone project covered a broad tech stack while maintaining an industry-standard approach. Overall, an excellent hands-on learning experience that I’d highly recommend!

Data Engineering

Jaideep Gupta

Cohort 1

★★★★★

The Live Data Engineering Bootcamp for Analysts exceeded my expectations. Harun's teaching stands out because he explains not just how things work, but why, making even complex concepts easier to understand. The curriculum is challenging, practical, and designed for real-world engineering, while the Codebasics team provides exceptional support through live doubt resolution, recordings, and an active Discord community. What impressed me most was how the team genuinely listened to learner feedback and adapted the pace to ensure everyone could keep up. The journey wasn't easy, but it was absolutely worth it. If you're serious about transitioning from Data Analytics to Data Engineering, I highly recommend this cohort for its practical learning, expert mentorship, and supportive community.

Data Engineering

Ashish Babaria

Cohort 1

★★★★★

Codebasics' Data Engineering Bootcamp came at exactly the right time in my journey from BI to becoming a full-stack data professional. Every session is packed with practical, production-grade learning - not textbook theory. From advanced SQL and data warehousing to PySpark, Databricks, Delta Lake, and Azure Data Factory, the curriculum is thoughtfully designed, with assignments and a capstone that reinforce real-world skills. Haroon teaches like an industry practitioner, and the entire team ensures an exceptional learning experience. This cohort has given me the confidence to move from consuming data to building it. If you're a BI or analytics professional looking to transition into data engineering, I recommend this bootcamp without hesitation.

Data Engineering

Cohort 2, mid-cohort In session

Written by learners while Cohort 2 was still running.

Abdullah Faizal

Cohort 2

★★★★★

I’m really enjoying the Data Engineering for Data Analysts Bootcamp. It’s helping me strengthen my skills in SQL, data modelling, Python, PySpark, cloud technologies, and data pipelines, while giving me a better understanding of real-world data engineering. A special mention to the instructor Harun, whose clear and practical teaching style makes complex concepts easier to understand. His guidance and willingness to answer questions have made the learning experience much more valuable. Overall, this course is helping me confidently move from Data Analytics towards Data Engineering.

Data Engineering

Biraj Bhusan Sahoo

Cohort 2

★★★★★

I recently enrolled into the Data Engineering course and I am having a very positive learning experience so far. The course covers all the topics mentioned in the brochure, with a teaching approach that was highly interactive and easy to understand. The doubt-clearing is done during the class itself and even after class also , with proper guidance for both conceptual and practical issues. The assignments and quizzes are also well-designed to strengthen our understanding and provide hands-on practice. Another great aspect is that the course focuses on industry-standard tools and technologies, making the learning experience relevant to real-world Data Engineering requirements. Overall, it is a very good and well-structured course with excellent support and practical learning. Highly recommended for anyone looking to build or strengthen their career in Data Engineering!

Data Engineering

Arun Sharma

Cohort 2

★★★★★

The bootcamp has been a really good learning experience so far. Harun explains technical concepts in a very simple and relatable way, making even complex topics easier to understand. I really like how he uses practical examples to explain things clearly. Dhaval Patel also has a great way of teaching, and his explanations are easy to follow and connect with real-world scenarios. His teaching style makes technical concepts more interesting and helps me understand why we use them in actual projects. Overall, the sessions are engaging and helpful, and I feel more confident as I continue learning data engineering. I’m really glad to be part of this bootcamp and would recommend it to anyone looking to build their skills in data engineering.

Data Engineering

Cohort 3 After graduation

This column is reserved for the analysts in Cohort 3.

The first review in this column could carry your name.

Cohort 3 starts 31^st October. 20 live sessions, 11 weeks, one capstone.

Enroll now →

Straight from the live sessions

What Data Analysts typed in the cohort chat, unedited.

Messages from past cohorts, posted during and right after the weekend sessions.

Before you decide

Three ways to learn this. Only one makes you ship.

	Self-study	Recorded course	This cohort
Live, with instructors	No	No	20 live sessions, doubt clearing in each
What you produce	Notes	A certificate	8 builds and a capstone repo
Production practice	On your own	Often skipped	Testing, CI/CD, monitoring and freshness checks
Cloud platforms	Pick one and hope	One, in isolation	Databricks, Azure and Fabric, compared with reasons
Career	On your own	Rarely	Stakeholder, branding, career and interview sessions
Investment	Your time	Varies	US$630 inner circle

Questions

The things analysts ask us before they join.

When does the bootcamp officially start?

Cohort 3 starts on Saturday, 31st October 2026 and graduates on Saturday, 16th January 2027. Sessions run on Saturdays and Sundays, 5:00 to 8:00 PM IST: two hours of learning, a 30-minute break and 30 minutes of doubt clearing. There is one midweek capstone session on Wednesday, 6th January, 7:30 to 9:00 PM IST.

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.

What if I miss a live session?

Every session is recorded and you keep access to the recordings for 1 year. We still recommend attending live, because the doubt clearing is where most questions get answered.

What happens after the 11 weeks? Do I lose access?

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

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.

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.

Who is this bootcamp designed for?

Working data analysts who already write SQL and want to build and run the pipelines behind their dashboards. If you have never written SQL, start with the data analytics bootcamp first.

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.

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.

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.

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.

What is the refund policy?

If it is not what you expected, ask for a 100% refund, no questions asked, until the end of Monday, 2nd November 2026. That is the day after the second live session, so you can see how the cohort runs before you decide.

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.

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.

What is Inner Circle, and how is it different from regular enrollment?

The inner circle is early enrollment for Cohort 3 at ₹36,000, open until Tuesday, 27th October 2026. After that the price is ₹48,000 until enrollment closes on Saturday, 7th November, 2026. The cohort starts Saturday, 31st October 2026.

Is there a break for Diwali?

Yes. There are no sessions on 7th and 8th November. The Diwali weekend is a planned break, and you can use it to catch up on practice from the first two sessions.

Do I need to know Python or Spark already?

SQL is assumed. Python is taught from the production angle in sessions 4 and 5, and Spark from session 6, so you do not need either before you start.

Which cloud do we use?

Azure and Microsoft Fabric, with Databricks for Spark and Delta Lake. Session 10 compares Synapse, Databricks and Fabric so you can choose with reasons at work.

What is the capstone?

One production-grade pipeline repo you build yourself. You get the brief in session 14 on 20th December, bring questions to the midweek jamming session on 6th January, and present it on 16th January.

What is the investment, and is EMI available?

The inner circle price is ₹36,000 until Tuesday, 27th October. After that it is ₹48,000 until enrollment closes. EMI is available.

Do you promise a job?

No. You leave with a capstone, interview preparation and a portfolio you can talk through. Outcomes follow effort.

Cohort 3 · Starts Saturday, 31^st October

11 weeks from now, you are the one who fixes the pipeline.

20live sessions
8builds
1capstone

Inner circle price

US$630

Closes Tuesday, 27^th October. Then US$840.

Enroll now →

Full refund until Monday, 2^nd November

US$630 inner circle Closes Tue, 27^th Oct, then US$840

Enroll now →