What is the difference between the bronze, silver, and gold layers in a data lake architecture, and how do you handle data once it reaches the silver layer? Also, how do you consolidate multiple data sources into a single table?

Bronze, silver, and gold layers are a common pattern in modern data lakehouse architectures. Bronze is the raw ingestion layer where data arrives in its native format, often with minimal transformation. Silver is the cleaned and enriched layer where schema is applied, duplicates removed, and data is transformed into a common format. Gold is the curated layer that delivers business‑ready tables for analytics, reporting, or downstream applications. Once data lands in silver, you typically perform data quality checks, enrich with reference data, and de‑duplicate. You then materialize the cleaned data into gold tables that are optimized for query performance (e.g., partitioned, compressed, and indexed). Consolidating multiple data sources into a single table is usually done in the silver layer by joining or unioning source streams, applying a common schema, and writing the result to a unified silver table. This table can then be used as the source for gold tables or for downstream analytics.