You mentioned building scalable data pipelines on Azure Data Factory or Azure Synapse. Can you describe a pipeline you built, including its sources, transformations, and overall purpose?

I built a scalable data pipeline in Azure Synapse Pipelines to consolidate customer data from multiple SaaS applications (Salesforce, Dynamics 365, and a custom CRM) into a unified data warehouse. The pipeline started with a Synapse pipeline activity that used the built‑in connectors to pull data via REST and OData APIs. I scheduled the pipeline to run nightly, using a trigger that ensured incremental loads by leveraging each source’s change‑tracking or timestamp fields. After ingestion, I used Data Flow to perform data quality checks, such as null‑value handling and duplicate removal, and to enrich the data by joining with a reference table stored in Azure SQL. The transformed data was then written to a Synapse dedicated SQL pool using PolyBase for high‑throughput bulk loading. Finally, I created a set of dimension tables (Customer, Product, Time) and a fact table (CustomerActivity) in the SQL pool, and set up incremental refresh logic. The pipeline was monitored via Synapse Studio’s monitoring hub, and alerts were configured for failures. This architecture leveraged Synapse’s serverless SQL for ad‑hoc queries, the scalability of Data Flow for transformations, and the cost‑effectiveness of PolyBase for loading large volumes.