Master
End-to-End
Azure Data
Engineering.
Build production-grade pipelines from ingestion to insight. Learn SQL, Azure Data Factory, Databricks and PySpark by shipping a real retail lakehouse — guided by Atchyut Kumar.
- Modules
- 33Modules
- Phases
- 13Phases
- Capstone Project
- 1Capstone Project
- Ingest
- Store
- Transform
- Orchestrate
- Serve
- SQL
- Data Factory
- Databricks
- PySpark
- Delta Lake
- Unity Catalog
/ What you'll learn
Follow the data — from raw source to real decisions.
The 13 phases map onto five stages of a production pipeline. You'll build each stage yourself, then connect them into a single retail lakehouse.
Data Factory · Auto Loader
Ingest
Pull data from databases, files and streams. Build a metadata-driven framework that handles full and incremental loads from one pipeline.
- Batch & streaming sources
- Watermarking & CDC
- Metadata-driven framework
ADLS Gen2 · Lakehouse
Store
Design a medallion lakehouse on ADLS Gen2. Model bronze, silver and gold layers on Delta, with ACID guarantees and a real transaction log.
- Delta Lake & the _delta_log
- Bronze/Silver/Gold
- Z-ordering & liquid clustering
Databricks · PySpark
Transform
Clean, join and reshape at scale. Handle schema drift, deduplication and business logic with tested Spark jobs.
- PySpark & SQL
- Schema evolution
- Unit-tested transforms
ADF Pipelines · Databricks Workflows
Orchestrate
Schedule, monitor and retry. Wire the stages into multi-task workflows with dependencies, automated retries and an enterprise audit log.
- Task dependencies
- Retries & scheduling
- Logging & audit framework
Unity Catalog · Gold Layer
Serve
Turn curated data into decisions. Publish business-ready gold marts governed by Unity Catalog, with lineage and fine-grained access control.
- Business-ready gold marts
- Unity Catalog lineage
- Row & column-level security
/ The path
Thirteen phases. Thirty-three modules. One production platform.
Built for data engineers with 3–8 years of experience. The programme moves in order — foundations, then the Azure stack, then the enterprise patterns a senior engineer is expected to own. Nearly every phase ends in a hands-on build.
- PHASE 01Module 1
Data Engineering Foundations
The mental model first — OLTP vs OLAP, warehouse vs lake vs lakehouse, ETL vs ELT, and the medallion architecture everything later builds on.
- PHASE 02Modules 2–3
Azure Fundamentals
Get the platform under you: subscriptions, resource groups and regions, then ADLS Gen2 with a Bronze/Silver/Gold layout and secured access paths.
- PHASE 03Modules 4–6
SQL for Data Engineers
From joins and set operators through window functions, recursive CTEs, views and stored procedures — the SQL depth interviews actually probe.
- PHASE 04Modules 7–14
Azure Data Factory
The longest phase. Linked services and activities, then dynamic parameterised pipelines, a metadata-driven framework, incremental loads, logging and SHIR.
- PHASE 05Modules 15–18
Databricks & PySpark
How Spark genuinely executes — driver, executors, DAG, Catalyst, AQE — then DataFrames, schemas, complex types, joins and vectorised UDFs.
- PHASE 06Modules 19–21
Delta Lake & Lakehouse
ACID on the lake: the transaction log, MERGE, time travel, Z-ordering and change data feed, assembled into a working Bronze → Silver → Gold flow.
- PHASE 07Modules 22–23
Streaming Data Engineering
Structured Streaming with checkpointing, watermarks and stateful aggregation, plus Auto Loader for event-driven incremental file discovery.
- PHASE 08Modules 24–25
Performance Tuning
Why pipelines are slow and how to prove it — partitioning, broadcast joins, skew and salting, Photon, caching and cluster right-sizing.
- PHASE 09Modules 26–28
Enterprise Databricks
Unity Catalog governance and lineage, row and column-level security, secret scopes, and multi-task Workflows with retries and alerting.
- PHASE 10Modules 29–31
DevOps & CI/CD
Treat pipelines as software: Git-backed Repos, branching and promotion, Databricks Asset Bundles, and tested releases via GitHub Actions or Azure DevOps.
- PHASE 11Modules 32–33
Data Warehousing
Dimensional modelling done properly — facts, dimensions, star vs snowflake — and SCD Types 1, 2 and 3 implemented with Delta MERGE.
- PHASE 12Capstone
End-to-End Industry Project
The retail lakehouse build: metadata-driven ingestion, full and incremental loads, audit logging, SCD 1 and 2, and a business-ready gold layer.
- PHASE 13
Interview & Certification Preparation
400+ scenario questions, Spark tuning drills, system design for data engineers and mock interview sessions — plus a direct path to the Databricks Certified Data Engineer Associate and Professional exams.
- SQL questions
- 150+SQL
- PySpark questions
- 100+PySpark
- ADF scenarios questions
- 50+ADF scenarios
- Delta Lake questions
- 50+Delta Lake
- Architecture questions
- 50+Architecture
/ Free resources
Try the teaching before you pay for it.
66 free lessons across five playlists on the EduFulness channel — same instructor, same approach. Start with Data Factory, then work through PySpark and SQL.
- 39 videosData Factory
Azure Data Factory (ADF) Tutorials
Watch free on YouTube - 9 videosScenarios
Real-Time Scenarios: Azure Data Factory
Watch free on YouTube - 4 videosDatabricks
Azure Databricks — PySpark Tutorials
Watch free on YouTube - 4 videosSQL
SQL — Interview Questions
Watch free on YouTube - 10 videosPySpark
PySpark Shorts
Watch free on YouTube - ChannelEverything else
EduFulness on YouTube
Visit the channel
/ Live classes
Sit in on the next live session.
Recorded lessons show you the material. A live class lets you ask the awkward question halfway through — which is usually where the learning actually happens.
Building a Metadata-Driven Ingestion Framework in ADF
A working session on the pattern that separates senior data engineers from everyone else: one pipeline, driven by metadata tables, handling full and incremental loads across any number of sources.
- Saturday, 22 August 2026
- 10:00 AM IST · 90 minutes
- Online · free to attend
- Design the source and target metadata tables
- Drive a single pipeline with ForEach and dynamic content
- Live Q&A with Atchyut at the end
No payment required
/ The program
One course. The entire Azure data stack.
Azure Data Engineering with SQL, Data Factory, Databricks & PySpark
An industry-standard curriculum for professionals with 5+ years of experience. From raw ingestion to a governed gold layer, ending in a full retail lakehouse build.
- 70–80 hours · 3–4 months
- 33 modules across 13 phases
- Databricks certification prep
Full programme
70–80Hours · 3–4 months · weekday & weekend batches
Request the syllabusJoin WhatsApp channel for updatesEnroll Now in UDEMY
Atchyut Kumar
Lead Instructor · M.Tech, NIT Calicut
/ Your instructor
Learn from someone who has mentored 50,000 students.
Atchyut Kumar holds an M.Tech from NIT Calicut and placed in the 99.97 percentile of GATE CS/IT (AIR 440). Across 15+ years in teaching, research and industry he has mentored students into roles at Amazon, Google, Oracle, Samsung and Adobe — and brings 9+ years of hands-on data engineering to every module.
Data Engineering — 9+ years of hands-on data integration, transformation and schema design.
GATE CS/IT Faculty — 7+ years teaching GATE aspirants, with a track record of top ranks.
Algorithms — Competitive programming, optimisation and problem-solving technique.
- Years experience
- 15+Years experience
- Students mentored
- 50k+Students mentored
- GATE percentile
- 99.97GATE percentile
/ Ready to build?
Start your data engineering journey.
Leave your details and we'll send the full 33-module syllabus and the next batch dates — or join the WhatsApp channel for announcements.
Join the WhatsApp channel