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Azure Data Warehouse Architecture

Jul 22, 2025

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Overview

This lecture provides a comprehensive, practical walkthrough of building an end-to-end real-world data warehousing project using Azure Databricks, covering modern data engineering concepts, tools, and implementation details to help you master Databricks for interviews and job readiness.

Project Architecture and Setup

  • The architecture follows a medallion design with Bronze, Silver, and Gold layers for data processing.
  • Source data (orders, customers, products, regions) is stored in Parquet format, not CSV, to support columnar storage and schema management.
  • Azure Data Lake is used as storage, and Databricks is set up with associated resource groups, storage, and access connectors in Azure.
  • Unity Catalog is configured for data governance and permissions, with external data locations set up for each data layer.
  • Notebooks are organized for each processing stage (bronze, silver, gold) and configured to run on appropriate Databricks clusters.

Bronze Layer: Data Ingestion

  • Incremental data loading uses Spark Structured Streaming with Databricks Auto Loader for exactly-once processing and idempotency.
  • Checkpoint locations and schema evolution are managed automatically to ensure only new files are processed.
  • No-code ingestion features are demonstrated for static mapping files (e.g., regions).
  • Dynamic, parameterized notebooks are created to handle multiple tables using loops and workflows.

Silver Layer: Data Transformation

  • Data is transformed and enriched using PySpark functions – including date parsing, aggregation, and text manipulation.
  • Object-Oriented Programming (OOP) principles are demonstrated by using Python classes to encapsulate reusable transformations.
  • User-defined functions are created within Unity Catalog for consistent business logic.
  • Data is written in Delta Lake format for ACID compliance and versioning.

Gold Layer: Data Modeling

  • Star schema is implemented with dimension (dim_customer, dim_product) and fact tables (fact_orders).
  • Slowly Changing Dimension (SCD) Type 1 is implemented using upsert (update/insert) logic with surrogate keys.
  • SCD Type 2 and other complex data warehouse features are automated using Delta Live Tables (DLT), which manage history and change tracking.
  • End-to-end orchestration is established using Databricks Workflows, enabling parallel and sequential execution.

Advanced Features and Analytics

  • SQL Warehousing capabilities are leveraged for running optimized queries and sharing endpoints with tools like Power BI for visualization.
  • Partner integrations enable quick connections for BI/reporting without manual configuration.
  • Visualization features are available in Databricks SQL Editor and can be shared with data analysts.

Key Terms & Definitions

  • Bronze/Silver/Gold Layers — Stages in medallion architecture for raw, cleansed, and curated data storage.
  • Unity Catalog — Centralized data governance solution in Databricks for managing access and metadata.
  • Spark Structured Streaming — Framework for processing real-time data streams in Spark.
  • Auto Loader — Databricks feature for efficient, incremental data ingestion from cloud storage.
  • Delta Lake — Storage layer enabling ACID transactions, schema enforcement, and versioning on big data.
  • Slowly Changing Dimension (SCD) Type 1/2 — Methods for managing dimension data changes in a warehouse.
  • Delta Live Tables (DLT) — Declarative ETL framework in Databricks to automate pipelines and data quality.
  • Upsert — Combination of update and insert operations, critical for SCD1.
  • Parameterization — Making notebooks dynamic by using inputs and loops to process different data sets.

Action Items / Next Steps

  • Download provided sample data in Parquet format and set up Azure Databricks and Data Lake accounts.
  • Configure resource groups, storage accounts, and Unity Catalog with correct access.
  • Implement ingestion, transformation, and data modeling layers step-by-step in assigned notebooks.
  • Practice running and debugging workflows and Delta Live Table pipelines.
  • Explore SQL Warehousing features and build sample BI integrations.
  • Review Python OOP concepts and PySpark functions to strengthen technical understanding.

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