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

Busy with the festive season? We’ve got you covered. See how →

Starts on 31st Oct 168+ learners across cohorts 1 & 2

From Data Analyst to Data Engineer in 11 Weeks.

Cohort 3 · Starts Saturday, 31st Oct, 2026. Inner Circle pricing until Tuesday, 10th November. Move from data analyst to data engineer through Codebasics' live 11-week cohort for working professionals. Learn warehouse-grade SQL, production Python, PySpark, Delta Lake, Azure, Microsoft Fabric, dbt, Airflow, CI/CD and streaming while building and shipping one end-to-end pipeline. Classes run live every Saturday and Sunday, 5 to 8 PM IST, with doubt clearing in every session and recordings for revision. The Diwali weekend, 7th and 8th November, is a planned break.

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11

Weeks

20

Live Sessions

10+

Tools & Platforms

8

Capstone Layers

1 Year

Access to Class Recordings

27+

Enrolled Learners

LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional
US$630 US$840 after 10th Nov · Save US$210

Created by :

Dhaval Patel, Hemanand Vadivel, Kirandeep Marala & Harun Raseed
Join

Join a cohort of working data analysts

Build

Build production pipelines, SQL to streaming

Ship

Ship one pipeline repo you can walk any interviewer through

Enroll Now

Codebasics Promise

We stand by the quality of our content. Not satisfied? Get a full refund, no questions asked, until the day after your first 2 live sessions after enrolling.

Added Benefit

If you have already taken our paid Data Engineering course(s) or bootcamp, you will pay only the difference (This Bootcamp Price - Money You Paid for Data Engineering course(s) or bootcamp Price)

Download Brochure

11

Weeks

20

Live Sessions

10+

Tools & Platforms

8

Capstone Layers

1 Year

Access to Class Recordings

27+

Enrolled Learners

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.

Joining after 31st October

Busy with the festive season? We’ve got you covered.

Classes before Diwali: 31st Oct and 1st Nov. Missed any? They’re all recorded.

1The day you enrollEvery class you missed, recorded

Unlock instantly. Watch them during the festive week.

2Fri, 13th Nov2-hour live revision class

An instructor recaps sessions 1 and 2 and takes your questions.

3Sat, 14th NovSession 3, live with everyone

Same capstone, same instructors, same final demo.

US$630 inner circle until Tue, 10th Nov · Enrollment closes Sat, 14th Nov

Overview

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

Week-1: SQL for Modern Data Engineering

Phase 01 · Foundations

  • 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

    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

  • Festival Break: Diwali

    No sessions. Diwali weekend

  • 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

    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 & exception handling · retry mechanisms · environment management · secrets handling

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

  • Session 5 - Advanced Python Data Processing

    APIs & 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 & 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

  • 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

    Output: A stakeholder update on your own work and a LinkedIn profile that reads like a data engineer's

  • 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

    Output: Your own career question answered, with one concrete next step written down

  • Session 9 - Delta Lake & Lakehouse Engineering

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

    Output: An incremental ETL pipeline running on Delta Lake

  • Session 10 - 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

  • Session 11 - 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 12 - Microsoft Fabric Engineering

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

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

  • Session 13 - dbt Core Fundamentals

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

    Output: A tested, documented dbt project with incremental models and snapshots

  • 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

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

  • 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

    Output: Scheduled Airflow DAGs that retry, alert and survive failures

  • Session 16 - CI/CD & Reliability Engineering

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

    Output: A CI/CD workflow with automated tests and freshness checks

  • 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

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

  • 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

    Output: A seven day interview prep plan built from your own resume

  • 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

    Output: Your capstone unblocked, with a clear next step

  • No Cohort 3 session. Capstone build time

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

  • 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

    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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Learn From

Your Instructors

Dhaval Patel

Dhaval Patel

Founder, Codebasics · Ex-NVIDIA

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

I have 17 years of experience in programming and data science at leading tech companies like NVIDIA and Bloomberg. I also run Codebasics, a leading YouTube channel for data science, ML, and data structures. I believe "anyone can code", so I use analogies, simple explanations, and step-by-step storytelling to make complex concepts easy to understand, even for high school students.

 Hemanand Vadivel

Hemanand Vadivel

Co-Founder & CEO, Codebasics

Leads most sessions and every leadership and management block.

I was among the top 1% with my amazing data analytics job in the UK. I left the job to stay back in India and pursue something that gives me a better sense of being and more joy - creating a positive impact on society by sharing knowledge.

 Kirandeep Marala

Kirandeep Marala

Analytics Engineer and Programme Manager, Codebasics

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

I started in analytics four years ago, answering business questions with SQL and dashboards, until the pipelines underneath pulled me into engineering. Now I manage the Data Engineering Bootcamp: I plan sessions with Harun, build assignments and notes, and evaluate what you ship. Every case is fresh, because I want transfer, not recall.

 Harun Raseed

Harun Raseed

Lead Data Engineer, Hitachi Solutions

Leads every technical session, from SQL execution plans to Kafka. Whiteboard first, code second, then stays for doubts.

I lead a data engineering team building enterprise platforms on Azure and Databricks. Ten years taught me the hard part is not the code, it is knowing what the engine did with it. So I teach whiteboard first, then code, then the execution plan. I am a Microsoft Certified Trainer and Databricks Certified Professional. You will read pipelines like I do.

The Stack

Tools You'll Work With

ADLS Gen2 + Key Vault

ADLS Gen2 + Key Vault

Storage and secrets
Apache Airflow

Apache Airflow

Scheduling
Apache Kafka

Apache Kafka

Streaming
Azure Data Factory

Azure Data Factory

Orchestration
Azure Event Hubs

Azure Event Hubs

Event ingestion
Databricks

Databricks

Lakehouse platform
dbt Core

dbt Core

Transformation
Delta Lake

Delta Lake

ACID tables
Docker

Docker

Local environments
GitHub Actions

GitHub Actions

CI/CD
Microsoft Fabric

Microsoft Fabric

Lakehouse and pipelines
Power BI · Reporting

Power BI · Reporting

Reports and verified DAX
PySpark

PySpark

Distributed processing
Pytest

Pytest

Testing
Python

Python

The language of every build
SQL Server

SQL Server

Warehouse SQL

Hear It From

Our Happy Learners

Our content is rated 4.9/5 from 18631+ Learners

testimonial-video-557
Abhishek Rajeev
Abhishek Rajeev

Python Python

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Jorge Daniel Carreon Guzman
View profile on LinkedIn Jorge Daniel Carreon Guzman

Data scientist

SQL SQL

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Miguel Ángel Hernández
View profile on LinkedIn Miguel Ángel Hernández

SQL SQL

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Sebastian Torres Franco
View profile on LinkedIn Sebastian Torres Franco

Professional expert in energy planning

SQL SQL

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Faizal Ahmed
View profile on LinkedIn Faizal Ahmed

Sr Manager, Business Analytics

Python Python

View All

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
View profile on LinkedIn Ashish Babaria

Product Consultant

LIVE DE Bootcamp: Batch 1 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
View profile on LinkedIn Jaideep Gupta

Tableau Developer

LIVE DE Bootcamp: Batch 1 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
View profile on LinkedIn Shalini Goutam

Data Engineer

LIVE DE Bootcamp: Batch 1 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
View profile on LinkedIn Nivetha Anand

Data and AI consultant

LIVE DE Bootcamp: Batch 1 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
View profile on LinkedIn Suchit Kumar B

Principal Data Architect

LIVE DE Bootcamp: Batch 1 LIVE DE Bootcamp: Batch 1

View All

Straight from the live sessions

What Data Analysts typed in the cohort chat, unedited.

Cohort chat message Cohort chat message Cohort chat message Cohort chat message Cohort chat message Cohort chat message Cohort chat message
Cohort chat message Cohort chat message Cohort chat message Cohort chat message Cohort chat message Cohort chat message

The Codebasics Promise

Build & Ship Production Data Pipelines in 11 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
Investment Your time Usually far higher US$630

May we help you?

Frequently Asked
Questions

Q.1 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.

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.

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.

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

The inner circle is early enrollment for Cohort 3 at ₹36,000. It is extended to Tuesday, 10th November 2026, and enrollment closes on 14th November. The cohort starts Saturday, 31st October 2026. If you join after the start, sessions 1 and 2 unlock as recordings the day you enroll, and you continue live from session 3 on Saturday, 14th November.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Q.1 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.

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

The inner circle price is ₹36,000, extended to Tuesday, 10th November. EMI is available.

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.

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

You can get a full refund, no questions asked, until the end of the day after your first 2 live sessions scheduled after enrolling. For example, if your first two live sessions after enrolling fall on a Saturday and Sunday, the refund window closes at the end of the day on Monday.

Please note: The refund window is based on the two live sessions scheduled after your enrollment, not on whether you attended them. If you miss a session, the refund window still moves forward.

To request a refund, email [email protected] before the window closes.

Please read the full refund policy here.

Q.1 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.

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.

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.

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

The inner circle is early enrollment for Cohort 3 at ₹36,000. It is extended to Tuesday, 10th November 2026, and enrollment closes on 14th November. The cohort starts Saturday, 31st October 2026. If you join after the start, sessions 1 and 2 unlock as recordings the day you enroll, and you continue live from session 3 on Saturday, 14th November.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Q.1 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.

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

The inner circle price is ₹36,000, extended to Tuesday, 10th November. EMI is available.

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.

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

You can get a full refund, no questions asked, until the end of the day after your first 2 live sessions scheduled after enrolling. For example, if your first two live sessions after enrolling fall on a Saturday and Sunday, the refund window closes at the end of the day on Monday.

Please note: The refund window is based on the two live sessions scheduled after your enrollment, not on whether you attended them. If you miss a session, the refund window still moves forward.

To request a refund, email [email protected] before the window closes.

Please read the full refund policy here.

Festive season? Catch up after Diwali. Inner circle US$630 extended to Tue, 10th Nov. Save ₹12,000.

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