27 comprehensive notebooks including data modelling foundations across 5 weeks + advanced modules
Complete journey from fundamentals to production deployment
Hands-on practice with real-world patterns
Comprehensive learning experience
From beginners to advanced practitioners
Master the Databricks platform, Unity Catalog governance, cluster management, and Spark optimization techniques.
Platform architecture, runtime environments, workspace organization, and best practices for production data engineering.
Data governance fundamentals, three-level namespace (catalog.schema.table), permissions, and secure data sharing.
Autoscaling strategies, instance types, cost optimization, and cluster configuration for different workloads.
Distributed computing, DataFrame operations, RDD transformations, and performance tuning for large-scale data processing.
Delta Lake fundamentals, ACID properties, Delta transaction logs, and how ACID properties map to Delta log transactions.
Essential data modeling concepts for building maintainable, performant data architectures including medallion patterns, dimensional modeling, and slowly changing dimensions.
Fundamentals of data modeling: organizing data for consistency, accessibility, and performance. Learn why modeling matters and key design principles.
Bronze, Silver, Gold layers: progressive data refinement pattern for data lakes. Understand when to use each layer and how they work together.
Star and snowflake schemas, fact and dimension tables for analytics. Learn to design data warehouses optimized for business intelligence.
Slowly Changing Dimensions (SCD Types 1, 2, 3) and Delta Lake implementation patterns. Handle historical data changes in production systems.
Production-grade ingestion patterns from files, APIs, databases, and cloud storage with error handling and retry logic.
CSV, JSON, Parquet ingestion with explicit schemas, data quality validation, and Delta Lake integration.
REST API integration, authentication patterns, retry logic, rate limiting, and error handling for production systems.
JDBC connections, incremental loading, change data capture (CDC), and database integration patterns.
Cloud storage patterns, partitioning strategies, data lakehouse architecture, and efficient file organization.
Batch and streaming ingestion, inference and explicit schema handling, error handling patterns, idempotent ingestion, and incremental loading.
Complex Spark operations including window functions, advanced analytics, and medallion architecture transformations.
Data cleaning, type conversions, business logic implementation, and Bronze to Silver layer transformations.
Ranking functions, moving averages, lead/lag operations, and time-series analytics with window functions.
Complex grouping operations, CUBE/ROLLUP, statistical functions, and Silver to Gold layer transformations.
Lazy evaluation, narrow and wide transformations, partitioning and shuffling, Catalyst optimizer, and caching and persistence strategies.
Build complete production pipelines from data ingestion through transformations to final insights.
Complete ETL pipeline from file ingestion through all medallion layers (Bronze → Silver → Gold) with monitoring.
Real-time data processing pipeline from API ingestion to final insights with error handling and recovery.
Medallion Architecture, idempotency and exactly-once processing, data quality and validation patterns, monitoring and observability strategies, and checkpointing and recovery patterns.
Professional Python packaging with Poetry, job orchestration, and production deployment with real stock market data.
Comprehensive guide to DAG concepts, retry logic, scheduling, and job orchestration fundamentals with both UI and SDK approaches.
Multi-task job orchestration with parallel execution, dependency management, and real-time monitoring.
Professional Python packaging with Poetry, building reusable modules, testing, and deploying wheels to Databricks.
Production capstone project with real stock market data (AAPL, GOOGL, MSFT, AMZN, NVDA) using Yahoo Finance. Complete medallion architecture pipeline with financial calculations and deployment automation.
Build interactive data applications with Streamlit, including a production stock market analyzer querying gold layer tables.
Complete guide to building data applications on Databricks: architecture, Streamlit fundamentals, Unity Catalog integration, and deployment strategies.
Production Streamlit application with interactive stock market analysis: market overview, risk-return analysis, detailed stock analysis, and portfolio simulator using gold layer data.
Databricks Platform
Master workspace, Unity Catalog, and cluster management
Data Engineering
Build production pipelines with medallion architecture
ETL/ELT Patterns
Implement ingestion, transformation, and loading workflows
Production Deployment
Package, orchestrate, and deploy professional solutions
Python Packaging
Create reusable wheel packages with Poetry
Data Applications
Build interactive dashboards with Streamlit
Choose your path and begin your Databricks journey