Live online · 120 hours · Beginner to Advanced
Databricks Course — 8-Week Advanced Lakehouse & Data Engineering Program
Eight structured weeks from Spark/PySpark foundations to a production Lakehouse you built yourself — Delta Lake, Lakeflow, Unity Catalog, streaming and CI/CD.
What you will be able to do
- Design a medallion (bronze/silver/gold) Lakehouse on Delta Lake that survives schema drift and late-arriving data
- Build incremental pipelines with Auto Loader, Structured Streaming and Delta Live Tables
- Govern data with Unity Catalog: catalogs, external locations, row/column masking and lineage
- Tune jobs with the Spark UI, Photon, liquid clustering, Z-ORDER and predictive optimisation
- Cut cluster spend with job compute, spot fleets, autoscaling policies and system-table chargeback
- Pass the Databricks Certified Data Engineer Associate and Professional exams
Curriculum — 8 weeks · 40 weekday sessions + 8 weekend masterclasses · 3 capstone projects
Sessions run live; every session is recorded. Labs are hands-on from week one.
- Databricks Lakehouse & Workspace Architecture
- Spark Architecture & Execution Fundamentals
- PySpark DataFrame API - Core Engineering
- Built-in Functions, Dates & Data Cleaning
- Window Functions & Practical Analytics
- Weekend lab: PySpark Deep-Dive Coding Lab
- Spark SQL for Data Engineering
- JSON, XML, Parquet, ORC & Open Formats
- Delta Lake Fundamentals
- Delta Lake Advanced Features
- Databricks Relational Objects & Table Design
- Weekend lab: Delta Lake Engineering Workshop
- Incremental Data Processing Concepts
- Databricks Auto Loader
- Spark Structured Streaming
- Watermarks, Stateful Streaming & Joins
- Medallion Architecture
- Weekend lab: Complete Bronze -> Silver -> Gold Project
- Lakeflow Pipelines (formerly Delta Live Tables / DLT)
- Data Quality Expectations
- CDC & Auto CDC / SCD
- Multi-Flow & Metadata-Driven Pipeline Patterns
- Pipeline Monitoring & Recovery
- Weekend lab: Declarative Pipeline Capstone Lab
- Lakeflow Jobs / Databricks Workflows
- Scheduling, Parameters, Retries & Repair
- Databricks SQL Warehouses
- AI/BI Dashboards, Alerts & Consumption
- Orchestration Architecture Decisions
- Weekend lab: Production Workflow Project
- Unity Catalog Architecture
- Storage Credentials & External Locations
- Permissions, Row Filters & Column Masks
- Lineage, Tags, Discovery & System Tables
- Delta Sharing & Lakehouse Federation
- Weekend lab: Enterprise Governance Lab
- Spark Performance Tuning Deep Dive
- Partitioning, Caching & File Optimization
- Photon, Serverless & Cost Optimization
- Testing & Observability
- Declarative Automation Bundles (formerly Databricks Asset Bundles / DAB)
- Weekend lab: CI/CD + Performance Engineering Workshop
- Databricks on AWS Integration
- Azure Databricks Integration
- Advanced Databricks Topics
- AI-Assisted Data Engineering
- Certification, Interview & Resume Preparation
- Weekend lab: Final Architecture, Capstone Review & Mock Interview
- Capstone 1 — Retail Analytics Medallion Platform: batch + incremental ingestion, SCD Type 2, quality expectations, governance, orchestration
- Capstone 2 — Real-Time Clickstream Pipeline: watermarking, checkpointing, late data, stateful aggregation, streaming observability
- Capstone 3 — CDC & SCD Type 2 Platform: insert/update/delete processing, sequence handling, SCD history, unit testing, governance
- Architecture walkthrough and code review with the trainer
- Mock interview and portfolio presentation practice
Hands-on projects
You leave with 3 portfolio projects you can demo in an interview — not toy notebooks.
Retail Analytics Medallion Platform
Batch and incremental ingestion into a Bronze/Silver/Gold Lakehouse with SCD Type 2, data-quality expectations, Unity Catalog governance and Lakeflow Jobs orchestration.
Real-Time Clickstream Pipeline
Structured Streaming clickstream pipeline covering watermarking, checkpointing, late-arriving data, stateful aggregation and streaming observability.
CDC & SCD Type 2 Platform
Change-data-capture platform handling insert/update/delete processing, sequence handling, full SCD Type 2 history, unit testing and governance.
Tools and technologies covered
Who this course is for
- Data engineers and ETL/Informatica developers moving to the Lakehouse
- SQL developers, DBAs and BI engineers modernising a warehouse
- Python/Java developers entering big data
- Architects preparing a Databricks migration
Prerequisites
- Basic SQL (SELECT, JOIN, GROUP BY)
- Any one programming language — Python and PySpark are taught from scratch in Week 1
- A laptop with 8 GB RAM; free Databricks Community Edition + trial cloud accounts are used in labs
Frequently asked questions
No. Week 1 builds Spark and PySpark fundamentals from scratch. If you already know Spark you can skip ahead — recordings are released from day one.
You can follow along on AWS, Azure or GCP. Labs are written to be cloud-neutral, with cloud-specific notes for storage and identity. Databricks Community Edition plus free cloud tiers cover most exercises.
Yes. The course maps to the Databricks Certified Data Engineer Associate and Professional exam guides, and includes 120 practice questions plus two timed mock exams. The exam fee itself is paid directly to Databricks.
Every session is recorded and posted the same day. You get lifetime access to those recordings and can rejoin any future batch at no cost.
Yes — resume review, LinkedIn optimisation, a mock interview and an interview question bank are included. We do not guarantee placement, and we will never sit in an interview on your behalf.
Venu Katragadda or a course advisor will call or WhatsApp you within one working day with the full syllabus, batch dates and fees. For anything urgent, WhatsApp +91-9247159150.
Free download · PDF
Download the full 120-hour syllabus
Every module, every hour, every lab and all three projects — the same document we hand to corporate clients. No email verification loop; the PDF downloads the moment you submit.
- Every week broken down topic by topic with hours
- All 3 portfolio projects in full
- Prerequisites, tools list and certification mapping
- Fees, EMI options, batch timings and the refund policy
What students say about Venu Katragadda
Verified Google reviews from Sreyobhilashi IT students. Read all 320+ reviews →
“Recently took Databricks classes with Venu to upskill in trending technologies, and the experience exceeded all expectations. While I initially sought guidance only on Databricks, Venu provided in-depth training across the entire ecosystem.”
Databricks · Cleared DE Professional Cert · Verified Google review
“This training has exceeded my expectations. Venu explains concepts clearly and uses hands-on examples that make the content easy to understand. I am learning a lot and would definitely recommend.”
Databricks Training · Verified Google review
“I recently completed the Data Engineering course on Databricks and AWS. Venu Sir delivers instruction at the next level, focusing on high-performance learning. He explains every concept clearly and thoroughly, accompanied by practical examples.”
Databricks & AWS Training · Verified Google review
Foundation course or masterclass?
We run two tiers. Most people should start with the foundation course on our sister site and step up later — this page is the advanced one.
Foundation · databrickstraining.in
Databricks Data Engineering Training
₹40,000₹25,000
- 80 hours of live instruction
- Covers the job-ready core of the stack
- Best if you are new to the platform or changing careers
- Same trainer, same teaching style
Masterclass · this page
Databricks Masterclass
₹40,000₹25,000
- 120 hours — roughly 25–30 extra hours of depth
- Internals, performance tuning and cost engineering modules
- Three reviewed portfolio projects instead of guided labs
- Architecture review and certification drill included
- Best if you already work with the stack and want senior-level depth
Not sure which fits? WhatsApp +91-9247159150 and Venu Katragadda will tell you straight — including when the cheaper one is the right answer.
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