Case Study
Sports
Ongoing
Building a scalable data pipeline : Multi-Source API & Web Scraping into Snowflake
Multi-source
Match, tracking & scouting data
Snowflake
Central data warehouse
Analysts
Monitoring & manual controls
Executive Summary
We partnered with the data science team at Wolverhampton Wanderers F.C. to modernize their data orchestration framework and address critical limitations affecting analytics productivity and decision-making speed.
The challenges
Wolves needed to feed match, recruitment, and performance models with timely, trustworthy data from multiple sources. Legacy pipelines couldn't keep up.
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Slow data refresh 5–6 day refresh cycles meant post-match and in-season models were always behind. The team needed sub-12-hour refresh for match-day and weekly planning.
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Limited data quality controls Little automated validation or monitoring. Bad or late data could slip through, undermining trust in dashboards and forcing manual checks.
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Vendor lock-in & high operational cost Proprietary orchestration platform drove rising costs and limited flexibility to scale or add new data sources.
Our approach
Modernized data orchestration
We designed and implemented a modernized data orchestration architecture that streamlined the pipeline execution. automated quality checks, and better observability—reducing dependency on the incumbent vendor and cutting costs while improving speed and reliability.
Key outcomes
- Data refresh reduced from 5–6 days to under 12 hours
- Automated data quality validation and monitoring
- Improved pipeline reliability and operational transparency
- Reduced platform dependency and vendor lock-in
- Cloud-native, serverless architecture with lower operational cost
- Faster, more reliable insights for the data science team
Architecture
Scalable, modular design for reliability, performance, and extensibility-from ingestion and processing through to monitoring and execution.
Monitoring dashboard
A purpose-built control surface for the data platform—designed with stakeholders, then engineered for clarity and everyday use by analysts and ops.
Design before build
Before development began, the monitoring dashboard was shaped in collaboration with the client using Figma prototypes. That let us validate workflows, confirm the right data points, and gather early stakeholder feedback—so the shipped product matched how analysts actually work.
The result is a dashboard focused on ease of use: clear states, sensible defaults, and actions that reduce back-and-forth with engineering for routine checks.
Impact
Better operational visibility and streamlined data management—less reliance on engineering for day-to-day pipeline tasks.
- Faster answers when something breaks or data is late
- Self-serve actions instead of ticket queues
Pipeline health
See ingestion status, gaps, and failures at a glance—no database access required.
Jobs & scheduling
Retrigger batches and schedule ingestion without waiting on engineering.
Logs & audit
Review operational logs in one place instead of hunting across systems.
Self-service
Built so non-technical staff can operate day-to-day workflows without SQL.
Scale & Complexity
Over a year, AptlyLabs delivered 400+ JIRA-tracked enhancements, bug fixes, and workflow improvements so the system kept pace with evolving data and operational needs. Active development across repositories produced ~800 code commits, reflecting continuous innovation and commitment to the platform.
JIRA items
Commits
Team & Effort
12+ months
Continuous engagement
6
Primary technologies
5+
Data sources integrated
Delivery Squad
Solutions Architect
System design and cloud strategy.
Project Management
Requirements and stakeholder alignment, manage the project.
UI/UX Designer
Monitoring dashboard and user experience.
DevOps / API Engineer
Pipeline and infrastructure reliability
Python Developer
Data extraction and ETL workflows
Frontend Developer
Develop Dashboard Frontend and UI Logic.
Conclusion
From fragmented, manual ingestion to a future-ready data platform.
AptlyLabs transformed a fragmented, manual data ingestion process into a centralized, scalable, and reliable data pipeline. By integrating multiple providers, automating ingestion workflows, and introducing a monitoring dashboard with manual and scheduled triggers, Wolves now receives football data with far greater reliability and operational transparency.
The new architecture improved data reliability and visibility, reduced operational overhead and infrastructure costs, and delivered a future-ready platform that evolves with the team's growing data needs.
Client testimonial
What Wolves said
Edward Maw
Lead Data Scientist at Wolves Football Club
Our recent collaboration with Aptlylabs has been nothing short of exceptional. The entire team went above and beyond to understand exactly what we needed and worked diligently to deliver on that, staying in close contact throughout and always responding promptly to requests.
Their deep technical expertise and thoughtful approach helped us build a robust AWS-based solution that has significantly accelerated our data ingestion pipelines while lowering our operational costs - all wrapped in a clean, easy-to-use UI that they designed for us.
We're thrilled with the final product and how well it's already scaled with our growing data demands, and we're looking forward to continuing our work with them in the near future.