01
Network modules
Source-specific Python modules collect Awin, Rakuten, and TradeTracker-style data.
Affiliate Network Data Pipelines
FeaturedCase Study
Python pipelines for normalizing Awin, Rakuten, and TradeTracker-style affiliate data into reporting-ready transaction tables.
Affiliate Marketing TeamClient engagement - Selected workSolo
System architecture
Architecture / Flow
The practical path from source data to reliable reporting output.
02
Normalization
Shared utilities standardize dates, currencies, transaction IDs, commission statuses, and schemas.
03
Unified output
Network-specific records are shaped into one reporting-ready affiliate transaction dataset.
04
Validation
Duplicate IDs, status changes, network totals, and date windows are checked before reporting.
Project Overview
Built affiliate network pipeline modules for multiple partner marketing sources. Each network had different schemas, date formats, currencies, commission statuses, and export/API behavior, so the work focused on source-specific extractors and shared normalization logic.
Key Challenges
- Affiliate networks used different schemas, statuses, currencies, and attribution windows
- Network-specific exports had to be normalized into one reporting shape
- Transaction IDs and commission statuses needed duplicate and status validation
- API settings and merchant/order identifiers needed to stay controlled across the workflow
Results & Impact
- Built separate Python source modules for multiple affiliate networks
- Created shared normalization utilities for network-specific outputs
- Defined validation checks for network totals, transaction IDs, dates, and statuses
- Mapped a reusable pattern for partner marketing transaction reporting
Technology Stack
Project Details
Industry:Affiliate & Partner Marketing
Duration:Client engagement
Team Size:Solo
Completed:Selected work
Tags
affiliate-marketingpythonetlawinrakutentradetrackernormalization
Have a similar data workflow?
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