LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional Cohort 2

Starts 29th Aug 97+ learners enrolled in cohort 1

Data Analyst → AI-Enabled Data Engineer

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. Limited seats available.

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10

Weeks

20

Live Sessions

10+

Tools & Platforms

8

Capstone Layers

1 Year

Access to Class Recordings

0

Enrolled Learners

LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional Cohort 2
US$630
US$840 after 25th Aug · Save US$210

Created by :

Dhaval Patel & Hemanand Vadivel Kirandeep Marala
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10

Weeks

20

Live Sessions

10+

Tools & Platforms

8

Capstone Layers

1 Year

Access to Class Recordings

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 18329+ Learners

testimonial-video-583
Asadur Rahman Nafi
View profile on LinkedIn Asadur Rahman Nafi

Data Scientist

SQL SQL

testimonial-video-557
Abhishek Rajeev
Abhishek Rajeev

Python Python

testimonial-video-4616
Sebastian Torres Franco
View profile on LinkedIn Sebastian Torres Franco

Professional expert in energy planning

SQL SQL

testimonial-video-329
Faizal Ahmed
View profile on LinkedIn Faizal Ahmed

Sr Manager, Business Analytics

Python Python

testimonial-video-132
Rishabh Mankar
View profile on LinkedIn Rishabh Mankar

Product Introducer at Hoben Infotech

SQL SQL

View All

I just finished a SQL course and was really impressed. It did a great job of breaking things down, starting with the basics and gradually moving into more complex stuff like joins and subqueries. The hands-on exercises and real-world examples made it easy to stay engaged and actually learn by doing. Overall, it gave me a solid foundation and I am confident now to apply these skills into real world projects. If you're new to SQL or just want to brush up, I'd definitely recommend it.

Krishnarjun Ghosh
View profile on LinkedIn Krishnarjun Ghosh

Application Engineer

SQL SQL

Landed a Job

It was a very helpful course that gave me a lot of information and hands-on experience, and I learned a lot about the basics and projects. it will be a good start to a career for anyone. I look forward to enrolling in a few more courses and upskilling myself more.
Thank you.

S Y Bharath
View profile on LinkedIn S Y Bharath

Data Analyst

Python Python

Landed a Job

As a data analyst stepping into the world of data engineering, I found this course extremely valuable. The concepts were explained in a way that felt approachable, even for someone without a deep engineering background.

Thank you, Dhaval sir, for designing such a practical and insightful course. I especially appreciated how the course bridged the gap between data analysis and engineering — it gave me the confidence to work with pipelines, cloud tools, and data architecture in a structured way. What seemed intimidating at first now feels doable, and I can already see how these skills will strengthen my career.

I’d highly recommend this course to anyone from an analytics background who wants to expand into data engineering without feeling overwhelmed.

Akshay Sonawane
View profile on LinkedIn Akshay Sonawane

Data Analyst

I just wanted to take a moment to genuinely appreciate the SQL course by Dhaval Sir on Codebasics. Honestly, learning SQL always felt a little dry and technical to me — but this course completely changed that.
The way Dhaval Sir explains concepts using real-world business problems is just amazing. Instead of just writing queries, you actually think like a data analyst, solving real challenges that companies face. It’s not just about syntax; it’s about understanding how SQL is used in the real world, and that made the whole learning journey so much more interesting and practical.
A huge thanks to Dhaval Sir for creating a course that doesn’t just teach SQL, but makes you actually enjoy learning it. Grateful for the effort and passion you’ve put into this course. It truly makes a difference!

Mohammad Navaman Jamadar
View profile on LinkedIn Mohammad Navaman Jamadar

SQL SQL

Landed a Job

Clear explanations, well-structured content, and practical examples that make learning easy and effective. Highly recommended for anyone looking to build or strengthen their skills.

Peter joy
View profile on LinkedIn Peter joy

Data Specialist

View All

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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

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The DE Promise

Build & Ship Production Data Pipelines in 8 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 5
The current 2026 Cloud stack On your own Often dated
Job assistance
Investment Your time ₹2,00,000+ US$630

May we help you?

Frequently Asked
Questions

Q.1 When does the bootcamp officially start?

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

You are securing your seat now. Full bootcamp access opens on 29th August 2026.

The Inner Circle is early enrollment, open until 25th August 2026. Inner Circle members enroll at a reduced price and get a direct say in the curriculum before the bootcamp launches on 29th August. You will receive a short feedback form asking which tools, topics and gaps matter most in your work, and your inputs shape what this cohort covers. You are not just enrolling early, you are helping shape what gets built.

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.

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.

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.

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.

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, 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.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.

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

Inner Circle enrollment is open until 25th August 2026. After that, Standard pricing opens from 26th August 2026 until the bootcamp starts on 29th August 2026.

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.

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.

Full refund, no questions asked, if you request it on or before 31st August 2026. That is after the first two live sessions (29th and 30th August), so you can see exactly how the bootcamp runs before deciding.

Q.1 When does the bootcamp officially start?

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

You are securing your seat now. Full bootcamp access opens on 29th August 2026.

The Inner Circle is early enrollment, open until 25th August 2026. Inner Circle members enroll at a reduced price and get a direct say in the curriculum before the bootcamp launches on 29th August. You will receive a short feedback form asking which tools, topics and gaps matter most in your work, and your inputs shape what this cohort covers. You are not just enrolling early, you are helping shape what gets built.

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.

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.

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.

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.

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, 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.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.

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

Inner Circle enrollment is open until 25th August 2026. After that, Standard pricing opens from 26th August 2026 until the bootcamp starts on 29th August 2026.

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.

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.

Full refund, no questions asked, if you request it on or before 31st August 2026. That is after the first two live sessions (29th and 30th August), so you can see exactly how the bootcamp runs before deciding.

Join Inner Circle until 25th Aug to avail price benefit. Save US$210

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