Have you built pipelines there?

Building pipelines involves designing a sequence of data ingestion, transformation, and loading steps that move data from source systems to target data stores or analytics platforms. I typically start by identifying the data sources, the required transformations, and the destination. I then choose an orchestration tool such as Apache Airflow, Prefect, or Azure Data Factory, and a processing engine like Spark or Flink. I design the pipeline to be modular, with clear input and output schemas, and implement error handling and retry logic. I also incorporate monitoring and logging to track pipeline health and performance. For example, in a recent project I built an Airflow DAG that extracted data from a REST API, transformed it using PySpark, and loaded it into a Delta Lake table on Databricks. I scheduled the DAG to run hourly, added SLA checks, and set up alerts for failures. The pipeline was scalable, fault‑tolerant, and produced clean, query‑ready data for downstream analytics.