AWS And Databricks Lakehouse
Storage and compute separation for governed analytical layers
The challenge
Modern data platforms need to scale without turning every batch flow into a custom script. Teams want governed analytical layers, lower operational friction, and a credible path from raw events to reusable business datasets.
How we solved it
- - Provision AWS resources with Terraform
- - Land raw events in S3 with a clear storage strategy
- - Process silver and gold layers in Databricks with PySpark
- - Use Delta Lake patterns to support reliable analytical serving
Execution story
Terraform sets up the base platform, event data lands in S3, and Databricks jobs transform raw records into cleaner and more governed layers.
Business framing
This case is designed for conversations about platform modernization, not only Spark syntax. It makes the business value visible: reusable analytical layers, clearer ownership, and lower friction for downstream teams.
Why it matters in the content platform
The same project can be linked to industry news about lakehouse evolution, then repackaged into site content and LinkedIn distribution without rewriting the narrative from scratch.