PhoenixAI (the original creator of StarRocks)

We’re kicking off our new newsletter this October 🎉 Your monthly hub for CelerData/StarRocks: feature releases, optimization tips, case studies, roadmap insights, and upcoming events. 💪 We’re starting strong with big news — StarRocks 4.0, our biggest and most capable release yet. And that’s just the start. The past few weeks have been packed with new tutorials, benchmarks, partnerships, and community meetups. Here’s a look at what’s new, what to watch, and where to meet us next 👇 #DataAnalytics #DataEngineering #DataLakeAnalytics #DataLake #DataLakeHouse #RealTimeAnalytics

If you’re building analytics for AdTech or MarTech, you’ve probably felt the pain of slow dashboards, heavy pipelines, and infra bills growing faster than your team. Demandbase has been there too. On their ClickHouse setup, that mix pushed them into extreme denormalization, heavy ETL, and 49 separate clusters just to keep up. So they stepped back and redesigned the architecture. The payoff: they cut hardware costs by 60%, slashed storage by 90%, saved millions, simplified operations for thousands of tenants, and still improved performance on complex, JOIN‑heavy workloads. Their engineering team walks through the concrete changes, trade‑offs, and architecture decisions in the case study. Check out how they pulled it off 👇

Demandbase cut hardware costs by 60%, slashed storage by 90%, and saved millions—all while boosting performance for complex, JOIN-heavy workloads. Before that, they were stuck with 49 clusters, overprovisioned hardware, and mounting storage waste just to keep up with scale. Their engineering team breaks down the exact changes, trade-offs, and architecture decisions behind those results. Check out how they did it in the full case study 👇

Demandbase didn’t just “optimize” infra — they saved millions. Once they moved from a sprawl of 49 clusters, heavily denormalized data, and expensive ETL workflows to a single 45‑node cluster with efficient on‑the‑fly JOINs and a much cleaner architecture, they were able to: ✅ Cut hardware needs by 60% ✅ Reduce storage costs by 90% ✅ Simplify ETL and free up their engineers If infra costs keep creeping up while performance stalls, this story will feel very familiar. Get the full breakdown of how they turned a cost problem into a long‑term advantage.

If your data team is stuck gluing together JOIN workarounds, denormalization jobs, and “just spin up another cluster,” you’ll recognize Demandbase’s story. It led to exactly what you’d expect: slow changes, ballooning storage, and infra bills nobody wanted to own. So they hit pause, redesigned the architecture, and ended up cutting hardware by 60%, reducing storage by 90%, and saving millions, all while making JOIN‑heavy workloads faster. Their engineers walk through the architecture, trade‑offs, and lessons learned. Grab the full breakdown 👇

If you’re building or maintaining data systems in AdTech or MarTech, you’ll probably relate to this one. Demandbase, a leading ABM and go-to-market platform, shares how they streamlined their stack, sped up product improvements, and made real-time analytics actually useful. Get the full deep dive!