Description

Slow dashboards, runaway cloud costs, and broken KPIs aren’t usually tooling problems—they’re data modeling problems. In this episode, I break down the four most damaging data modeling mistakes that silently destroy performance, reliability, and trust at scale—and how to fix them with production-grade design patterns. If your analytics stack still hits raw events for daily KPIs, struggles with unstable joins, explodes rows across time ranges, or forces graph-shaped problems into relational tables, this episode will save you months of pain and thousands in wasted spend. 🔍 What You’ll Learn in This Episode

• Why slow dashboards are usually caused by bad data models—not slow warehouses
• How cumulative tables eliminate repeated heavy computation
• The importance of fact table grain, surrogate keys, and time-based partitioning
• Why row explosion from time modeling destroys performance
• When graph modeling beats relational joins for fraud, networks, and dependencies
• How to shift compute from query-time to design-time
• How proper modeling leads to:
  • Faster dashboards
  • Predictable cloud costs
  • Stable KPIs
  • Fewer data incidents

🛠 The 4 Data Modeling Mistakes Covered 1️⃣ Skipping Cumulative Tables Why daily KPIs should never be recomputed from raw events—and how pre-aggregation stabilizes performance, cost, and governance. 2️⃣ Broken Fact Table Design How unclear grain, missing surrogate keys, and lack of partitioning create duplicate revenue, unstable joins, and exploding cloud bills. 3️⃣ Time Modeling with Row Explosion Why expanding date ranges into one row per day destroys efficiency—and how period-based modeling with date arrays fixes it. 4️⃣ Forcing Graph Problems into Relational Tables Why fraud, recommendations, and network analysis break SQL—and when graph modeling is the right tool. 🎯 Who This Episode Is For

• Data Engineers
• Analytics Engineers
• Data Architects
• BI Engineers
• Machine Learning Engineers
• Platform & Infrastructure Teams
• Anyone scaling analytics beyond prototype stage

🚀 Why This Matters Most pipelines don’t fail because jobs crash—they fail because they’re:

• Slow
• Expensive
• Semantically inconsistent
• Impossible to trust at scale

This episode shows how modeling discipline—not tooling hype—is what actually keeps pipelines fast, cheap, and reliable. ✅ Core Takeaway Shift compute to design-time. Encode meaning into your data model. Remove repeated work from the hot path. That’s how you scale data without scaling chaos.

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