Case Study · Data Engineering
Turfsport Data Lakehouse
Turfsport processes 4+ billion bets a year with several large-ticket betting houses and outlets. The sheer volume of data started running out from underneath them. Symbyte rebuilt the entire data foundation — enabling billions of rows to be queried in seconds.
The Challenge
4+ Billion Bets a Year. The Data Was Winning.
Turfsport processes one of the largest volumes of wagering data in Africa. The infrastructure that had served them well began failing under the weight of their own success — and the business couldn’t query its own data fast enough to run.
Turfsport processes 4+ billion bets a year with several large-ticket betting houses and outlets across South Africa. However, the sheer volume of data started running out from underneath them. No longer could bookkeepers or users query their data in a timely and user-friendly manner.
The existing reporting infrastructure was built for a fraction of the current data volume. As the business grew, queries that should take seconds began taking minutes. Operational reporting became unreliable. And as customers started demanding more data access through Turfsport’s own product, the gap between data reality and data capability widened further.
- 4+ billion bets per year
- Slow query performance
- Infrastructure ceiling hit
- Customer data access needed
- Reporting at breaking point
Challenge
Solutions Built
A New Foundation. Built to Last.
Symbyte designed and implemented a complete data lakehouse architecture for Turfsport — combining scalable cloud storage with a semantic reporting layer that made the data usable for both engineers and business stakeholders.
The solution was built on AWS with Pulumi for infrastructure-as-code, Cube.js for the semantic layer, PostgreSQL for structured data, and Kubernetes and Docker for containerised, resilient deployment.
Data Lakehouse Implementation
Utilising the power of cloud computing, Symbyte implemented a sub-30min ETL pipeline into a data lakehouse for hundreds of millions of rows. Advanced technologies now enable answers from billions of rows in seconds, not minutes.
Semantic Reporting Layer
With a scalable and efficient lakehouse in place, demand for reporting grew. Symbyte implemented a semantic data layer enabling Turfsport devs and analysts to deliver insights to customers more easily, while taking compute load off the core lakehouse.
Kubernetes-Backed Deployment
The entire infrastructure was containerised with Docker and orchestrated with Kubernetes — ensuring high availability, horizontal scalability, and zero-downtime deployments as data volumes continue to grow.
Sub-30 Minute ETL
Hundreds of millions of rows loaded into the data lakehouse in under 30 minutes — from a process that previously took hours or simply couldn't complete reliably.
Queries in Seconds
Billions of rows now queryable in seconds — bookkeepers and business users get the answers they need at the moment they need them, without engineering intervention.
Self-Service Reporting Unlocked
The semantic reporting layer enabled Turfsport's own development team and analysts to deliver insights directly to customers — without putting load back on the core data lakehouse.
Infrastructure Built to Scale
Kubernetes orchestration and containerised deployment means the system scales horizontally as bet volumes grow — without manual infrastructure intervention or performance degradation.
"Symbyte delivered an immediate positive change due to their incredible depth of knowledge and responsiveness in dealing with queries and problems"
Before Symbyte only
30%
of ML models are deployed to production.
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