Selected Work
Compare five hands-on case studies across Microsoft Fabric, Azure Data Factory and Azure Databricks, covering ingestion, transformation, lakehouse storage, validation and pipeline reliability.
Project Library
Showing 5 projects
Microsoft Fabric
Lakehouse and Data Quality
A Microsoft Fabric ingestion workflow that discovers incoming files, validates trusted office CSV files and loads approved records into a curated Lakehouse table while routing unsupported or schema-incompatible files to quarantine.
Azure Data Factory + Azure Databricks
Metadata-Driven Quarterly Processing
A completed quarterly source-to-Raw platform combining metadata-driven ADF orchestration, Databricks validation, Delta Lake processing, Unity Catalog registration and protected watermark updates.
Azure Data Factory
Metadata-Driven Ingestion
A configurable Azure Data Factory ingestion framework that reads source behaviour from metadata and processes SQL Server, HTTP or REST and file-based sources through reusable pipeline components.
Azure Databricks
PySpark and Data Quality
A retail data pipeline that ingests order and order-item data from Amazon S3, stages it in ADLS Gen2 and applies Databricks PySpark validation before loading approved records into Azure SQL Database.
Azure Data Factory
Mapping Data Flow and Conditional Routing
A parameterised Azure Data Factory pipeline that reads movie data from Azure Blob Storage, applies Mapping Data Flow transformations and routes validated outputs to Azure SQL Database or ADLS Gen2 according to release-year rules.
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