Can you explain how to add data to the silver layer in a lakehouse architecture?

In a lakehouse architecture, data is typically staged in three layers: Bronze (raw), Silver (cleaned and enriched), and Gold (ready for analytics). The Silver layer is where you perform transformations, deduplication, and schema enforcement. To add data to the Silver layer, you usually ingest raw data from a source (e.g., S3, Kafka), run a Spark or Flink job that cleans and enriches the data, and write the result to a Delta Lake or Parquet table in the Silver location. You also maintain a schema registry or use Delta Lake’s schema evolution to handle changes. The job can be scheduled via Airflow or a cloud scheduler, and you can use Glue Crawlers to catalog the Silver tables. This approach ensures downstream analytics can rely on a consistent, high‑quality dataset. If you’re using a different stack, the same principles apply: ingest, transform, write to a curated layer, and register the schema.