Case study / Manufacturing
From months
to days.
A manufacturer that could not tell whether revenue moved on price, volume or product mix.
The difference it made
Value you can see.
Revenue reporting in days, not months.
Reporting
Reporting in days.
The reporting cycle moved from months to days.
Quality
Fewer data-quality issues.
Change data capture and preserved history catch problems before reports do.
Scale
Built for volume.
The lakehouse takes large daily sales volumes in its stride.
The solution, at a glance
Revenue changes.
Clearly explained.
The challengeWhat we changed
- Scattered sales dataOne lakehouse
- Changing recordsPreserved history
- Manual calculationsAutomated revenue analysis
- Sales dataSQL Server & files
LakehouseAWS · Iceberg
Curated dataSnowflake
Revenue driversPower BI
Raw → Refined → Curated · History preserved
Engineering details
The challenge
- Sales data split across SQL Server and manual uploads
- History had to stay accurate as records changed
- PVM logic was complex and hand-calculated
The build
- Raw, refined and curated layers on AWS with Apache Iceberg
- Change data capture with DMS and SCD Type 2 history
- Step Functions and EventBridge orchestration
- Curated tables in Snowflake, PVM in Power BI, all in Terraform
Technology
AWSSnowflakePower BI
TerraformAWS DMSGlueIcebergStep FunctionsSnowflakePower BITerraform
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