Module 1: Introduction to Azure Databricks
Lesson 1: Overview of Azure Databricks
- What is Databricks? (Unified Data Analytics Platform)
- Key components: Workspace, Clusters, Notebooks, Jobs
- Integration with Azure services (Blob Storage, Synapse, ADF)
Lesson 2: Architecture & Key Concepts
- Databricks Runtime (Optimized Spark)
- Workspace organization (Folders, Repos, Teams)
- Cluster types (All-purpose, Job, High-Concurrency)
Lesson 3: Pricing & Cost Optimization
- DBU (Databricks Unit) pricing model
- Cluster auto-scaling & termination policies
- Spot instances & cost-saving best practices
Module 2: Setting Up Azure Databricks
Lesson 1: Deployment & Configuration
- Creating a Databricks workspace in Azure
- Azure Active Directory (AAD) integration
- Network security (VNet, Private Link)
Lesson 2: Workspace Navigation
- Databricks UI overview
- Notebooks, Repos, and Workspace organization
- Managing users and permissions
Lesson 3: Hands-on Lab
- Deploy a Databricks workspace
- Create your first notebook
- Run a simple PySpark query
Module 3: Data Ingestion & Processing
Lesson 1: Reading & Writing Data
- Connecting to Azure Data Lake (ADLS Gen2)
- Delta Lake format (ACID transactions, schema enforcement)
- Supported data sources (CSV, JSON, Parquet, SQL DB)
Lesson 2: ETL with Spark
- Transformations (filter, join, aggregate)
- Structured Streaming for real-time data
- Optimizing Spark jobs (partitioning, caching)
Lesson 3: Hands-on Lab
- Ingest data from Azure Blob Storage
- Clean and transform data using PySpark
- Write processed data to Delta Lake
Module 4: Delta Lake & Advanced Data Engineering
Lesson 1: Delta Lake Deep Dive
- Time travel (querying historical data)
- MERGE, UPDATE, DELETE operations
- Optimizations (Z-ordering, compaction)
Lesson 2: Databricks SQL & BI Integration
- SQL Warehouses in Databricks
- Creating dashboards with Databricks SQL
- Connecting Power BI to Databricks
Lesson 3: Hands-on Lab
- Implement SCD (Slowly Changing Dimensions) in Delta
- Optimize a Delta table for fast queries
- Build a Databricks SQL dashboard
Module 5: Machine Learning & AI in Databricks
Lesson 1: MLflow for Model Tracking
- Experiment tracking
- Model registry & deployment
- Hyperparameter tuning
Lesson 2: Distributed ML with Spark
- MLlib for scalable machine learning
- Feature engineering at scale
- Integrating with Azure ML
Lesson 3: Hands-on Lab
- Train a machine learning model using MLflow
- Deploy a model for batch inference
- Monitor model performance
Module 6: Job Automation & Orchestration
Lesson 1: Databricks Jobs
- Scheduling notebooks & workflows
- Job clusters vs. interactive clusters
- Error handling & retries
Lesson 2: Integration with Azure Data Factory
- Triggering Databricks jobs from ADF
- Parameterized notebooks
- Monitoring job performance
Lesson 3: Hands-on Lab
- Schedule a daily ETL job
- Set up alerts for job failures
- Orchestrate a multi-step pipeline
Module 7: Security & Governance
Lesson 1: Access Control & Security
- Role-based access control (RBAC)
- Secret management (Databricks Secrets)
- Encryption & compliance
Lesson 2: Performance Tuning & Optimization
- Cluster configuration best practices
- Spark UI & debugging
- Delta Lake performance tuning
Lesson 3: Hands-on Lab
- Configure table access control
- Optimize a slow-running Spark job
- Implement data masking
Module 8: Real-World Use Cases & Capstone Project
Lesson 1: Industry Applications
- Real-time analytics (IoT, clickstream)
- Data warehousing with Databricks SQL
- AI/ML use cases (fraud detection, recommendation engines)
Lesson 2: Capstone Project
- End-to-end data pipeline: Ingest → Process → Analyze → Visualize
- Example: Retail sales forecasting