- _HµCourse Modules
Module 1: Introduction to Databricks & Modern Data Engineering
What is Databricks?
Lakehouse Architecture Overview
Difference between Data Lake, Data Warehouse, and Lakehouse
Setting up Databricks workspace
Brief tour of the Databricks UI
Hands-on: Launching your first Databricks Notebook
Module 2: Spark Essentialsfor Data Engineers
Spark architecture &execution model
DataFrames vs Datasets vs RDDs
Working with PySpark in Databricks
Hands-on: Reading and writing data using Spark
Transformations and Actions
Module 3: Delta Lake and Data Lakehouse
What is Delta Lake?
Features: ACID Transactions, Time Travel, Schema Enforcement
Creating & managing Delta Tables
Data versioning and rollback
Hands-on: Converting Parquet to Delta, using Time Travel
Module 4: Ingesting Data at Scale
Ingesting batch data (CSV, JSON, Parquet, externalsources)
Streaming data ingestion (Kafka, Auto Loader)
Using Databricks Auto Loader
Hands-on: Streaming data ingestion from cloud storage (S3/ADLS)
Best practicesforscalable ingestion
Module 5: Building ETL Pipelines
Writing modular ETL jobs in notebooks
Using Databricks Workflows(Jobs API)
Orchestrating pipelines using Task Dependencies
Scheduling jobs and alerts
Hands-on: Building a reusable ETL pipeline
Module 6: Data Quality and Validation
Data validation with Deequ or Great Expectations
Implementing quality checks in Delta
Handling bad records and nulls
Logging and alerting with MLflow or other tools
Hands-on: Quality checks with Delta table constraints
Module 7: Performance Optimization & Best Practices
Caching, partitioning, and Z-ordering
Query optimization and cost reduction
Choosing the right cluster type and size
Managing job performance metrics
Hands-on: Optimizing a slow-running query
Module 8: Real-Time Data Processing
Spark Structured Streaming on Databricks
Window functions and aggregations
Streaming joins and watermarks
Handling late data
Hands-on: Building a real-time dashboard pipeline
Module 9: CI/CD and Production Readiness
Version control with Git in Databricks
CI/CD using Databricks Repos & Workflows
Managing environments (dev, test, prod)
Logging, monitoring & alerting strategies
Hands-on: End-to-end pipeline deployment with Git
Module 10: Project
Project Ideas(Choose One):
Build a real-time data pipeline
ETL pipeline for a retail sales dataset with Delta Lake
End-to-end streaming + batch processing pipeline
⬛ Outcomes
By the end of this course, learners will be able to:
Build production-grade ETL pipelines in Databricks
Use Spark and Delta Lake effectively
Automate and schedule jobs using Workflows
Process batch and streaming data atscale
Implement CI/CD and deploy end-to-end data pipelines
