Azure Data Engineering Project

End-to-End Azure Data Engineering Pipeline ( On-Prem to Azure Cloud )

In my journey to strengthen my cloud and data engineering skills, I recently built an end-to-end Azure data pipeline that simulates how modern enterprises migrate, transform, monitor, and analyze on-premises data in the cloud for decision-making.

The workflow begins with an on-prem CSV source migrating into Azure using Azure Data Factory (ADF) with a Self-Hosted Integration Runtime, enabling secure access to internal systems that are not exposed publicly. ADF was connected to Git for enterprise-grade version control, supporting branching, code reviews, and collaborative development. The ingested data lands in Azure Data Lake Storage Gen2, which acts as a scalable and cost-efficient data repository for both raw and curated layers.

To improve reliability and observability, I integrated Azure Logic Apps for pipeline email alerts and Azure Monitor for metrics, logs, and diagnostics—similar to how real production environments track SLAs and error handling. Data cleaning and transformation was performed in Azure Databricks, where I applied schema standardization and quality checks before pushing refined data downstream. Sensitive secrets such as access keys and tokens were stored securely using Azure Key Vault and consumed through a Databricks Secret Scope, ensuring compliance with cloud security best practices.

Once processed, the data was analyzed in Azure Synapse Analytics using SQL for reporting queries and validation. To make the output usable for business teams, I created interactive dashboards in Power BI, allowing stakeholders to slice, filter, and visualize insights from the transformed dataset.

The final architecture followed a realistic enterprise pattern:
On-Prem Data → ADF (Git + SHIR) → Data Lake Gen2 → Logic Apps + Monitor → Databricks (Transform) + Key Vault → Synapse Analytics → Power BI.

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