After processing data from your source, you store it in three S3 buckets and then write it back to another S3 layer. Who uses this data and how do they use it?

In a typical data lake architecture, the processed data in S3 is consumed by several downstream stakeholders. Business analysts use the curated datasets in Amazon Athena or Redshift Spectrum to run ad‑hoc queries and build dashboards in QuickSight. Data scientists pull the same data into SageMaker notebooks or Databricks clusters to train machine learning models. ETL teams schedule incremental loads from the S3 layer into a data warehouse such as Snowflake or BigQuery for reporting. Operational teams might expose the data via API Gateway or Lambda functions for real‑time applications. The key is to version the data, maintain a catalog (Glue Data Catalog or AWS Lake Formation), and enforce access controls so each group can retrieve the appropriate granularity. By writing the data back to a separate S3 layer, you create a clear separation between raw, processed, and curated data, enabling each downstream consumer to work with the right schema and quality level.