How would you design a data pipeline and Databricks that ingest data from REST APIs, relational databases, or streaming sources?

To design a data pipeline that ingests data from REST APIs, relational databases, and streaming sources using Databricks, I would first identify the source types and their ingestion patterns. For REST APIs, I would use Spark’s HTTP client or an external library like requests to pull data in batches, then write the payload to a staging Delta table. For relational databases, I would use JDBC connectors to read tables or incremental changes via CDC, loading them into Delta Lake. For streaming sources such as Kafka or event hubs, I would set up Structured Streaming jobs that write to Delta tables with schema evolution enabled. I would orchestrate the pipeline with Delta Live Tables or Airflow, ensuring data quality checks, schema validation, and lineage tracking. The data would be stored in Delta Lake, partitioned by business keys and time, enabling ACID transactions and fast reads. I would also set up monitoring with Unity Catalog, job alerts, and dashboards in Databricks SQL. This architecture provides a unified, scalable, and maintainable pipeline that handles batch and streaming ingestion, transformation, and storage in a single platform.