My Design Process
I follow a structured Azure data engineering process that starts with understanding business and data requirements, designing scalable pipelines using ADF, Databricks, and Synapse, transforming and curating data in Delta Lake, ensuring governance and performance optimization, and finally delivering analyst-ready datasets while continuously monitoring and iterating for efficiency and reliability.
Requirements & Data Understanding
I start by understanding the business goals, reporting needs, and data sources, assessing data quality, formats, and accessibility. This ensures that the pipeline design aligns with both business objectives and technical constraints, whether the data comes from APIs, databases, CSVs, or external datasets.
Architect & Plan Pipelines
I design the data flow architecture using ADF, Databricks, and Synapse, Fabric typically following a medallion architecture (Bronze → Silver → Gold). Decisions about batch processing, incremental loading and Idempotency procedures with file formats like Delta, Parquet, or CSV are made to optimize performance and maintainability.
Build & Transform
I develop ETL/ELT pipelines in ADF and Databricks, applying transformations, aggregations, and data cleaning using PySpark or SQL. Delta Lake is used to provide ACID-compliant, versioned storage, ensuring reliable and reproducible datasets.
Governance & Data Management
I implement data governance with Unity Catalog and RBAC, ensuring secure and compliant access. While validating data quality through incremental loads and checks.
Delivery & Consumption
Curated datasets are delivered to Synapse, SQL warehouses, Fabric Warehouse or Power BI, making them ready for analytics and reporting. I create reusable views or tables to simplify access for analysts and business users.