Data Integration using Python & Alteryx
Developed a digital reporting solution enabling descriptive analytics at scale—empowering multiple Adidas & Reebok business units with automated, timely, data-driven decisions.
Description
The objective was to replace an existing Syntasa clickstream data integration system with a new Adobe API-based pipeline using in-house enterprise toolsets. The solution needed to enhance scalability, reduce costs, and establish full ownership of data processing and reporting logic.
Key Challenges
- High processing and licensing costs of the legacy platform.
- Security and data governance concerns.
- Limited transparency in existing functionality.
- Operational dependency on the external vendor.
Solution
Designed and implemented an end-to-end automated data pipeline combining Python and Alteryx:
- Data Extraction: Leveraged Python scripts to fetch data from Adobe APIs.
- Data Transformation: Applied business rules and complex transformations using Alteryx workflows.
- Data Loading: Pushed analytics-ready datasets into the Exasol in-memory database for enterprise-wide reporting.
These components were orchestrated within a master Alteryx wrapper workflow to ensure scheduled, reliable execution. Additionally, a custom Slack bot was integrated to notify Adidas' Global DevOps team about daily job status and performance metrics.
Key Benefits
- Established self-sufficient, in-house integration—eliminating vendor dependency.
- Reduced recurring costs associated with the Syntasa platform.
- Unified multiple Adidas business streams (e.g., Football, Yeezy) into a single consolidated data repository.
- Achieved zero manual intervention through full process automation.
- Embedded data quality validation checks for error-free delivery.
Tech Stack
- Python (Adobe Analytics API extraction)
- Alteryx (wrapper & transformation workflows)
- Exasol (in-memory analytical data store)
- Slack bot (DevOps notifications)
- Automated validation & scheduling
Workflow Visual
