Real-Time Data Engineering Platform

Real-Time Data Engineering Platform – Azure Databricks

Client

Personal

Timeframe

June 01 - June 15

Services

Azure Databricks, PySpark, Delta Lake, Delta Live Tables, Spark Streaming, Unity Catalog, Power BI, Tableau

Source Code

Project Overview

This project delivered a robust, enterprise-grade data engineering platform on Azure, built around Azure Databricks. It ingests, processes, and serves both streaming and batch data—integrating Spark Streaming, Delta Lake, Delta Live Tables, Unity Catalog, and BI tools like Power BI and Tableau. The result is a scalable, governed data pipeline supporting real-time analytics and reporting.

Implemented Components

Real-Time Data Ingestion: Employed Spark Streaming and Databricks Autoloader to ingest streaming data with low latency and auto schema evolution
ETL Pipelines with DLT: Leveraged Delta Live Tables for declarative, error-tolerant ETL workflows with built-in data quality and lineage controls
Data Modeling: Designed and implemented a star schema, handling Slowly Changing Dimensions (SCDs) for dimension tables, and optimized fact tables for analytics.
Data Governance: Managed access controls and lineage via Unity Catalog, ensuring secure, auditable data in a collaborative environment.
Delta Lake for Storage: Integral use of Delta Lake features like ACID transactions, time travel, schema enforcement, and vacuuming.
Workflow Orchestration: Orchestrated end-to-end pipelines (Autoloader → Bronze → Silver → Gold) using Databricks Workflows.

Tech Stack & Infrastructure

Cloud Platform: Microsoft Azure
Compute & Processing: Azure Databricks with PySpark, Spark Streaming, Delta Lake, DLT
Data Governance: Unity Catalog for access control and metadata management
Storage: ADLS Gen2 with Bronze–Silver–Gold medallion architecture
BI Integration: Connected to Power BI and Tableau via Databricks SQL endpoints

Key Innovations & Learnings

Streamlined streaming ingestion: Implemented Autoloader with trigger-based pipelines for efficient micro-batching.
SCD logic within DLT: Streamlined slowly changing dimensions through Delta Live Tables declarative constructs.
Schema design excellence: Star schema optimized for performance, ensuring clean separation of facts/dimensions.
Governance-first mindset: Unity Catalog was leveraged for fine-grained data access and centralized schema management.
Production-grade reliability: Established automated pipelines and error handling via Databricks Workflows, enabling retries and clean fault recovery.

Outcome & Impact

The final solution stands as a fully operational, real-time data platform suited for:

Streaming analytics: Nearly real-time ingestion, processing, and visualization (via Power BI / Tableau).
Reliable data pipelines: With Delta Live Tables managing quality, lineage, and schema consistency.
Secure, governed data access: Unity Catalog ensures enterprise-ready compliance and collaboration.
Scalable architecture: Easily extendable to additional sources, complex transforms, or ML integration.