Databricks Lake House

This project outlines a cloud-native Data Lakehouse platform built on Databricks. The architecture combines the reliability and ACID transaction support of data warehouses with the flexibility and cost-effectiveness of raw cloud storage.

1. Medallion Architecture

Below is the standard Medallion architecture showing the three processing stages:

graph LR
    source["Cloud Landing Zone (S3 / ADLS)"] -->|Auto Loader| bronze["Bronze Layer (Raw Delta)"]
    bronze -->|Enrich & Deduplicate| silver["Silver Layer (Clean Delta)"]
    silver -->|Aggregate & Optimize| gold["Gold Layer (Analytical Delta)"]
    gold -->|SQL Warehouse| bi["BI Tools & Analytics"]

    classDef stage fill:#cc295f,stroke:#ffffff,fill-opacity:0.25,stroke-opacity:0.9;
    classDef output fill:#ff7e47,stroke:#ffffff,fill-opacity:0.25,stroke-opacity:0.9;
    class bronze,silver,gold stage;
    class source,bi output;

2. Platform Capabilities

• ACID Transactions: Implemented via Delta Lake storage format, enabling concurrent reads/writes and historical versioning (Time Travel).
• Databricks Auto Loader: Automates ingestion of high-frequency streaming files from cloud storage with schema evolution tracking.
• PySpark & Structured Streaming: Powers transformations and data enrichment workflows at scale.
• Databricks SQL & Unity Catalog: Provides centralized governance, row/column-level access control, and a dedicated SQL execution warehouse.

3. Implementation Blueprint

Phase 1: Workspace & Cloud Storage Setup

Provision cloud infrastructure and configure:

• Storage buckets (AWS S3 or Azure ADLS Gen2) to serve as the storage backend.
• Identity and Access Management (IAM) roles for secure Databricks workspace mounts.
• Unity Catalog metastore configuration for centralized governance.

Phase 2: Raw Ingestion (Bronze Layer)

Implement Databricks Notebooks to ingest incoming source files:

• Use PySpark Auto Loader (cloudFiles) to capture raw JSON/CSV dumps dynamically.
• Store the ingested stream as append-only Delta tables in the bronze schema without modifying structure.

Phase 3: Data Quality & Enrichment (Silver Layer)

Transform the bronze raw data to silver tables:

• Parse nested JSON elements and enforce strict schemas.
• Clean invalid inputs, filter nulls, and deduplicate primary keys.
• Perform joins against lookup dimensions (e.g., product or location tables).

Phase 4: Business Aggregations (Gold Layer)

Compute key metrics for business consumption:

• Build summary aggregates (e.g., daily sales, active user counts).
• Apply data organization optimizations such as Z-ORDER BY on query dimensions to improve query speeds.
• Save the results as read-optimized Gold Delta tables.

Phase 5: SQL Warehouse Serving

Set up a Databricks SQL Serverless Warehouse:

• Connect the Gold Delta tables to Databricks SQL Dashboards or external BI tools.
• Write analytical queries to evaluate key business indicators.