What is the typical sequence of steps when building a data architecture that starts with a data lake, then processes data, and finally makes it available for BI reporting?

The typical flow is: 1) Ingest raw data into a bronze layer of the data lake using ingestion tools (Kafka, Flink, or scheduled scripts). 2) Clean and enrich the data in a silver layer, applying transformations, schema enforcement, and data quality checks. 3) Aggregate and materialize business metrics in a gold layer, often stored in a data warehouse or lakehouse format. 4) Expose the gold layer to BI tools via a semantic layer or direct SQL access. 5) Provide dashboards and reports in Power BI, Tableau, or Looker. Throughout, I use metadata management, lineage tracking, and automated testing to ensure reliability. This layered approach keeps raw data safe, allows iterative refinement, and delivers clean, ready‑to‑use data for analysts.