--- source_url: "https://codebasics.io/bootcamps/data-engineering-bootcamp-for-analysts" title: "Data Engineering Bootcamp for Analysts | Codebasics" mirrored_at: 2026-08-27T01:31:44.799Z host: codebasics.io cited_in_42a: true mirror_canonical: "https://index.42a.ai/codebasics.io/bootcamps/data-engineering-bootcamp-for-analysts" --- > **Original source:** https://codebasics.io/bootcamps/data-engineering-bootcamp-for-analysts 1. [Home](https://codebasics.io/) 2. LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional ## LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional [ 4.9 (1948 Verified ratings) ](#review) ## Data Analyst → AI-Enabled Data Engineer Transform from a Data Analyst into an AI-Enabled Data Engineer through Codebasics' live 8-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. 8 Weeks * * * 16 Live Sessions * * * 10+ Tools & Platforms 8 Capstone Layers * * * 1 Year Access to Class Recordings * * * 98+ Enrolled Learners ![LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional](https://files.codebasics.io/164231/Website_Thumbnail_DA_DE_Cohort_we.webp) US$840 ![Join our Bootcamp](https://images.codebasics.io/v3/images/join.svg) #### Join our Bootcamp ![In-demand Skills](https://images.codebasics.io/v3/images/learn.svg) #### Learn In-demand Skills ![Get Hired](https://images.codebasics.io/v3/images/hired.svg) #### Get Hired by Top Companies 8 Weeks * * * 16 Live Sessions * * * 10+ Tools & Platforms 8 Capstone Layers * * * 1 Year Access to Class Recordings * * * 98+ Enrolled Learners ## What Makes This Bootcamp Different? - 100% LIVE, instructor-led sessions across 8 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 18493+ Learners 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! Landed a Job The Data Engineering Basics for Data Analysts course provides a strong foundation in core data engineering concepts with a practical, hands-on approach. I particularly enjoyed building an end-to-end ETL pipeline using AWS services such as S3, Glue, Lambda, Athena, and Redshift. The explanations were clear, the projects were relevant, and the course effectively connected theory with real-world implementation. This course helped me better understand how modern data pipelines are built and managed in the cloud. I highly recommend it to data analysts, aspiring data engineers, and professionals looking to strengthen their AWS data engineering fundamentals. Highly recommended! 🌟 This is my third consecutive course with Codebasics with Dahaval Sir. I am really happy to take these courses which gave me a good understanding and solid foundation on AI-ML and Data Engineering. The data engineering course gave me a real good exposure to how data preperation is done in AWS. But becuase i didnt have any knowledge on AWS so i couldnt run the code properly on LAMBDa, Glue and Atehna , got some errors which i could not fix.... I had to take a detailed course on AWS and understood how AWS work. I guess there will be many students who will face the same problem when they will landup in this course...so my humble request is in this Data engineering course please add some more tutorial videos on AWS at the begining, even you start the actual Data pipeline....Rest all good. And please make a detailed course on Docker and Kubernetes and how to run ML app on them in AWS. Thanks a lot.... This course provides a comprehensive introduction to the Python programming language. The course is well structured, starting with the basics and gradually building up to more advanced concepts. The lessons are taught through clear and concise video tutorials, accompanied by interactive coding exercises that reinforce the concepts covered. The course covers topics such as data types, functions, object-oriented programming, and more. The instructor is knowledgeable and passionate about Python, and the course is well-paced, making it easy to follow along and absorb the material. Overall, the Code Basics Python course is an excellent resource for anyone looking to learn Python, from beginners to those with some programming experience. Landed a Job Hello CodeBasics team and fellow students! I’ve just completed the Python course from the GEN-AI bootcamp, and I really want to thank Mr. Patel for conducting such engaging lectures and practical sessions. I had the chance to apply what I learned on two real-life projects (I’m saying two because some projects were excluded in GEN-AI, but these two were highly relevant). The exercises after each unit were a great way to reinforce learning. My advice: take this course and practice consistently. Once you finish the course, keep practicing to truly master the concepts. Thank you, CodeBasics! I’m proud to be part of this family and to have completed the first step in my GEN-AI/Data Science journey. Landed a Job [View All](https://codebasics.io/testimonials) Overview ## What you'll learn in this Live Data Engineering for Data Analyst Bootcamp ### [Week-1: Advanced SQL Engineering Phase 01 · Foundations ](#) - Session 1 - SQL for Modern Data Engineering Advanced joins & query optimization · window functions deep dive · recursive CTEs · MERGE statements · incremental loading patterns · CDC concepts · query execution plans · warehouse optimization Output: Hands-on: Optimize enterprise-scale SQL workloads · build incremental transformation logic * * * - Session 2 - Data Modeling & Warehouse Engineering OLTP vs OLAP · star schema · snowflake schema · fact vs dimension tables · SCD Type 1 & 2 · partitioning strategies · medallion architecture · data contracts Output: Hands-on: Design retail analytics warehouse · implement SCD Type 2 logic ### [Week-2: Advanced Python Engineering Phase 01 · Foundations ](#) - Session 3 - Production Python for Data Engineers Modular Python architecture · OOP for pipelines · config-driven frameworks · logging · exception handling · retry mechanisms · environment management · secrets handling Output: Hands-on: Build a reusable ingestion framework * * * - Session 4 - Advanced Python Data Processing APIs & ingestion patterns · async processing · parallel execution · file streaming · memory optimization · testing with pytest · packaging basics Output: Hands-on: Build an API ingestion pipeline ### [Week-3: PySpark & Distributed Engineering Phase 02 · Platforms & Cloud ](#) - Session 5 - PySpark Deep Dive Spark architecture · executors & DAGs · lazy evaluation · partitioning · broadcast joins · shuffle optimization · Spark UI analysis · caching strategies Output: Hands-on: Optimize large-scale Spark workloads * * * - Session 6 - Delta Lake & Lakehouse Engineering Delta internals · ACID transactions · OPTIMIZE & ZORDER · time travel · schema evolution · Change Data Feed · incremental ETL · Bronze / Silver / Gold architecture Output: Hands-on: Build a medallion architecture pipeline ### [Week-4: Cloud & Modern Data Platforms Phase 02 · Platforms & Cloud ](#) - Session 7 - Azure Data Engineering Stack ADLS Gen2 · Event Hubs · Key Vault · managed identities · Integration Runtime · networking basics · Synapse vs Databricks vs Fabric Output: Hands-on: Build a secure cloud ingestion architecture * * * - Session 8 - Microsoft Fabric Engineering OneLake · Lakehouse · Warehouse · Fabric Data Factory · Eventstream · Real-Time Intelligence · DirectLake · Fabric governance Output: Hands-on: End-to-end Fabric implementation ### [Week-5: Analytics Engineering & dbt Phase 03 · Transformation & Orchestration ](#) - Session 9 - dbt Core Fundamentals Models · sources · refs() · materializations · snapshots · incremental models · tests · documentation Output: Hands-on: Build a modular dbt transformation project * * * - Session 10 - Advanced Analytics Engineering Macros & Jinja · semantic layer · MetricFlow · SQLFluff · lineage · data quality frameworks · governance · reusable transformation patterns Output: Hands-on: Enterprise dbt framework implementation ### [Week-6: Orchestration & Pipeline Engineering Phase 03 · Transformation & Orchestration ](#) - Session 11 - Apache Airflow Engineering DAG architecture · dynamic DAGs · sensors · XCom · scheduling · monitoring · retry patterns · failure handling Output: Hands-on: Build orchestrated ETL workflows * * * - Session 12 - Enterprise Data Pipelines Azure Data Factory · Fabric Pipelines · Databricks Workflows · metadata-driven pipelines · config-based orchestration · parameterization · reusable frameworks Output: Hands-on: Build a metadata-driven orchestration framework ### [Week-7: Streaming, CI/CD & Reliability Phase 04 · Production & Launch ](#) - Session 13 - Streaming Data Engineering Kafka fundamentals · event-driven architecture · Structured Streaming · watermarking · windowing · event-time processing · CDC streaming · Event Hub integration Output: Hands-on: Real-time streaming pipeline * * * - Session 14 - CI/CD & Reliability Engineering Git branching strategies · GitHub Actions · automated testing · deployment pipelines · monitoring · freshness checks · cost optimization · incident management Output: Hands-on: CI/CD pipeline for data engineering workloads ### [Week-8: Architecture, Capstone & Career Phase 04 · Production & Launch ](#) - Session 15 - End-to-End Capstone Project API ingestion · lakehouse architecture · PySpark transformations · dbt modeling · Airflow orchestration · CI/CD · Power BI reporting · monitoring layer Output: Hands-on: Enterprise-grade end-to-end implementation * * * - Session 16 - Interview Preparation & System Design SQL interview rounds · PySpark interview questions · data modelling rounds · system design · resume transformation · LinkedIn optimization · mock interviews Output: Hands-on: Mock interview + architecture discussion sessions 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 ![](https://images.codebasics.io/v3/images-webp/bootcamp/faqs.webp) ### [ Q.3 What is the Inner Circle, and how is it different from regular enrollment? ](#) The Inner Circle is early enrollment, open until 16 June 2026. Inner Circle members enroll at a reduced price and get a dedicated live session a few days before the bootcamp launches on 20 June to help shape the curriculum through their feedback. You are not just enrolling early, you are influencing what gets built. ### [ 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.8 What happens in the Inner Circle curriculum session? ](#) All Inner Circle members join a live session with the core team a few days before 20 June 2026. You bring real analyst-to-engineer problems, the stacks your team is moving to, and the gaps you want filled. What you share directly shapes the deep-dives, labs, and capstone tracks. The exact date is communicated to Inner Circle members after enrollment. ### [ 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.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.3 What is the refund policy? ](#) Full refund, no questions asked, if you request it on or before 22 June 2026. That is after the first two live sessions (20 and 21 June), so you can see exactly how the bootcamp runs before deciding. ![](https://images.codebasics.io/v3/images-webp/bootcamp/faqs-mob.webp) ### [ Q.3 What is the Inner Circle, and how is it different from regular enrollment? ](#) The Inner Circle is early enrollment, open until 16 June 2026. Inner Circle members enroll at a reduced price and get a dedicated live session a few days before the bootcamp launches on 20 June to help shape the curriculum through their feedback. You are not just enrolling early, you are influencing what gets built. ### [ 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.8 What happens in the Inner Circle curriculum session? ](#) All Inner Circle members join a live session with the core team a few days before 20 June 2026. You bring real analyst-to-engineer problems, the stacks your team is moving to, and the gaps you want filled. What you share directly shapes the deep-dives, labs, and capstone tracks. The exact date is communicated to Inner Circle members after enrollment. ### [ 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.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.3 What is the refund policy? ](#) Full refund, no questions asked, if you request it on or before 22 June 2026. That is after the first two live sessions (20 and 21 June), so you can see exactly how the bootcamp runs before deciding.