How would you design an AWS Glue job to process data flowing through multiple S3 buckets in a banking domain, when historical data storage is not required?

In a banking scenario where you need to ingest, transform, and load data from multiple S3 buckets without retaining historical snapshots, you can adopt a layered data lake approach. First, create three logical layers in S3: raw, processed, and curated. The raw layer holds the original files as they arrive. A Glue crawler runs on the raw bucket to populate the Glue Data Catalog with the schema. For incremental processing, enable Glue job bookmarks so the job only reads new or changed files. The Glue ETL job reads from the raw layer, applies business rules (e.g., masking sensitive fields, normalizing formats), and writes the cleaned data to the processed layer. If you need a curated view for downstream analytics, another Glue job can aggregate or join the processed data and write to the curated bucket. Security is critical: enable S3 bucket policies, server‑side encryption (SSE‑S3 or SSE‑KMS), and IAM roles with least privilege. For compliance, audit logs via CloudTrail and enable S3 access logs. Finally, schedule the Glue jobs using EventBridge or Glue triggers to run on a daily or hourly cadence, ensuring near‑real‑time availability without storing historical copies.