El Toro + Starburst: Query Performance at Scale

While I was CTO at El Toro, the company's data team ran into a wall that every data-heavy organization eventually hits: the queries that used to be fine were getting slow, and the data was getting bigger. Starburst profiled what we did about it in a customer case study — "Optimizing query performance to power data lake analytics."

The Scale

On any given day, El Toro's machine learning algorithms digest over 350 billion data points. That's not a number I throw around casually — it's the daily reality of an IP-targeting platform matching physical and IP addresses at scale. The analytics engine had to run fast, live, interactive queries against a massive data lake, and it had to do it for ad-hoc analytics, not just pre-baked reports.

The Results

The case study documents what we achieved with Starburst Enterprise: a 300% improvement in query performance and $5 million in savings, supporting thousands of campaigns launched per day. The quote they pulled from me says it plainly: "The needs we were struggling with were exactly why we ended up with Trino, and Starburst was a very natural next step for us as an Enterprise company — it makes data access easier, better, more supported, more stable, and more developed without needing to put the resources in place."

The full case study is on the Starburst site. It's a good look at what happens when you stop fighting your data infrastructure and let it scale with you.

Think this argument fits your event? Tell me about the room — the calendar is selective.