Databricks Data Engineering
An intensive 8-week (40-lecture) Databricks & PySpark masterclass taking you from Python programming foundations (datatypes, loops, functions, collections, OOP, exception handling) to scalable Lakehouse data engineering. Master Apache Spark architecture, Spark SQL, Databricks workspaces and clusters, Databricks File System (DBFS), Databricks Utilities (dbutils: fs, secrets, notebook, widgets), external cloud mounts (ADLS Gen2, Azure SQL, Synapse), Delta Lake ACID transactions, MERGE INTO (UPSERTs), Slowly Changing Dimensions (SCD Type 1 & 2), Time Travel, and Catalyst performance optimization.
Created by
Support Me Techs Team
Certification Ready
Graduate fully prepared for the Databricks Certified Data Engineer Associate exam with practice tests included.
Guaranteed Outcomes
People don’t buy lectures — they buy results.
Certification Ready
Graduate fully prepared for the Databricks Certified Data Engineer Associate exam with practice tests included.
Lakehouse Architecture
Master the medallion architecture pattern used by Fortune 500 companies to build their modern data platforms.
Real-Time Pipeline
Build a fully functioning Kafka-to-Delta-Lake streaming pipeline as your capstone project.
Specific Skills You'll Master:
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How You'll Learn
Live Sessions
Join expert-led classes via Zoom / Meet. Real-time doubt clearing.
Session Recordings
HD recordings of every session.
Hands-on Work
Weekly assignments and industry-grade portfolio projects.
Weekly Roadmap
Structured learning cadence to ensure consistent progress.
Meet Your Instructor
Support Me Techs Team
Senior Technical Mentor
Learners
300+
Rating
4.8
Courses
3
Professional Journey
8+ Years in Big Data & Data Engineering
Tech Stack Expertise
Major Milestones
- Enterprise Lakehouse Ingestion Pipeline (ADLS Gen2 to Delta Lake Medallion Architecture)
- Production SCD Type 2 Dimension Pipeline (Delta Lake MERGE INTO Historical Audit Trails)
- Parameterized Multi-Notebook Orchestration & Query Optimization Engine with DBUtils
Course Content & Outcomes
12 lectures • 8 Weeks total length
Python & Spark Prerequisites: Functional programming, lambda functions, collections, file I/O & OOP
Apache Spark Core Architecture: Drivers, executors, cluster managers, partitions & lazy evaluation
Databricks Unified Platform: Workspace navigation, multi-node compute clusters & Databricks runtime
Databricks File System (DBFS) & DBUtils: Mount points, cloud storage access & secrets management
PySpark DataFrame Operations: Reading multi-format data (CSV, JSON, Parquet), schema definition & transformations
Advanced PySpark Transformations: Complex expressions, window functions, joins & aggregations
Delta Lake Architecture: ACID transactions, transaction log, time travel & metadata management
Medallion Lakehouse Pattern: Architecting Bronze (raw), Silver (cleansed) & Gold (aggregated) data layers
Delta Lake DML Operations: High-performance UPSERTs using MERGE INTO & optimizing table storage with OPTIMIZE & Z-ORDER
Slowly Changing Dimensions (SCD): Implementing SCD Type 1 and Type 2 historical dimension pipelines
Workflow Orchestration: Multi-notebook Databricks Jobs, parameterized widget pipelines & automated triggers
Performance Tuning & Optimization: Catalyst optimizer, Adaptive Query Execution (AQE), caching & exam readiness
Requirements
- Basic understanding of programming concepts
- A computer with internet access
- No prior professional experience required
- Passion for learning and building!
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Databricks Data Engineering
Key Outcomes
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Databricks Data Engineering
Key Outcomes