Can you describe how you build ingestion logic and transformation, perform validation checks, leverage APIs, and trigger actions based on comparisons?

A robust ingestion pipeline starts with a source connector that pulls raw data from files, databases, or APIs. The data is then staged in a landing zone (e.g., S3 or a raw table). Transformation logic is applied using a processing engine such as Spark or dbt, where you clean, enrich, and shape the data into a target schema. Validation checks are embedded as unit tests or data quality rules—e.g., null checks, range checks, or referential integrity—executed after each transformation step. When APIs are involved, you authenticate (OAuth, API keys), make paginated requests, and handle retries or back‑off. Comparisons are performed by joining the new data with a snapshot of the previous state; differences trigger downstream actions such as sending alerts, updating downstream tables, or invoking a workflow orchestrator like Airflow. Error handling includes logging, alerting, and retry queues. Monitoring dashboards track ingestion latency, error rates, and data freshness, ensuring the pipeline remains reliable and scalable.