Introduction: In this project, I built a highly scalable data pipeline for processing and analyzing Netflix dataset using Azure Data Engineering Stack. The goal was to build an end-to-end ETL solution that efficiently ingests, transforms, and visualizes data in Databricks, Delta Live Tables (DLT), Azure Synapse, and Power BI.
🔧 Tech Stack & Tools Used: ✅ Databricks: Used for data processing, transformation, and orchestration ✅ Delta Live Tables (DLT): Implemented incremental data processing with autoloader ✅ Azure Data Factory (ADF): For data ingestion & workflow automation ✅ Azure Data Lake Gen2: Storage layer for Bronze, Silver, and Gold tables ✅ Azure Synapse Analytics: Warehouse for querying structured data ✅ Power BI: For interactive dashboards and visualizations ✅ GitHub: Version control & collaboration ✅ Azure Key Vault: Secure storage of credentials ✅ dbutils: Databricks Utilities for handling widgets, secrets, and storage
📂 Data Pipeline Architecture: 📌 Ingestion Layer: Data is loaded incrementally using Databricks Autoloader from Azure Data Lake. 📌 Bronze Layer (Raw Data Store): Stores raw ingested data in Delta format. 📌 Silver Layer (Transformations & Cleansing): Applied validations, deduplication, and aggregations. 📌 Gold Layer (Star Schema): Data is structured for analytics & reporting. 📌 Orchestration: Used Databricks Workflows and Azure Data Factory for scheduling jobs. 📌 Visualization: Power BI connected to Azure Synapse for interactive reporting.
🔹 ⚡ Key Implementations & Optimization Techniques 🔹 Incremental Loading: ✅ Used Databricks Autoloader for continuous ingestion with checkpointing. ✅ Ensured idempotency to avoid duplicate data loads.
🔹 Delta Live Tables for Streaming & Batch Processing ✅ Implemented Change Data Capture (CDC) using Delta format. ✅ Used @dlt.table and @dlt.expect_all_or_drop() to enforce data quality checks.
🔹 Orchestration with Conditional Execution ✅ Implemented ForEach & If-Else Conditions to execute specific jobs based on day of execution. ✅ Leveraged dbutils.jobs.taskValues.set() for cross-task communication.
🔹 Optimizations using Databricks SQL ✅ Used OPTIMIZE & ZORDER BY to speed up queries & partition pruning. ✅ Data Skipping to reduce scan times on large datasets.
🔹 Security & Access Control ✅ Configured Azure Key Vault to store secrets & credentials securely. ✅ Used dbutils.secrets.get() to retrieve authentication tokens securely.
🔹 Outcome & Impact 🔥 End-to-end ETL Pipeline successfully processes Netflix dataset with high efficiency & scalability. 🔥 Automated workflow execution with Databricks Workflows & ADF reduces manual effort. 🔥 Optimized queries & data warehouse design lead to faster analytics in Power BI. 🔥 Fully secure environment with Azure Key Vault integration.