Will you explain the architecture of the Enterprise ETL pipeline you have designed?

An enterprise ETL pipeline typically consists of four layers: ingestion, staging, transformation, and delivery. The ingestion layer pulls data from heterogeneous sources (databases, APIs, files) using connectors or message queues. Data is then moved to a staging area where it is stored in raw form, often in a data lake or temporary tables, allowing for decoupling from source systems. The transformation layer applies business logic, data cleansing, enrichment, and schema mapping. This layer may use batch jobs, stream processors, or a data integration platform. Metadata management tracks lineage, quality metrics, and schema changes. Orchestration tools (Airflow, Prefect, or cloud services) schedule and monitor jobs, handling dependencies and retries. The delivery layer loads the transformed data into a target warehouse or data mart, using bulk loads or incremental merges. Key decisions include choosing between batch vs. real‑time processing, selecting storage formats (Parquet, ORC), ensuring data quality with validation rules, and implementing security controls such as encryption and role‑based access. Monitoring dashboards and alerting complete the architecture, providing visibility into performance and failures.