Скачать или смотреть 4 Data Modeling Mistakes That Break Data Pipelines at Scale

Are your dashboards painfully slow and costing your team valuable time? The answer might not be bigger tools or more storage, but smarter data models. Fix slow dashboards with smarter data models and learn how to shift computation upfront for faster, more predictable results.

In this episode, I show you four production-grade data modeling patterns that eliminate performance bottlenecks. From cumulative tables for high-use metrics to graph-shaped logic for complex relationships, I’ll break down practical strategies to keep your pipelines efficient and your dashboards lightning fast. No hype—just actionable design decisions to prevent your data systems from collapsing under their own weight.

Why does this matter? Slow dashboards and runaway cloud costs aren’t just frustrating—they’re signs of weak data models that impact business decisions. By optimizing your data pipeline, you’ll improve system trust, cut costs, and empower your team to focus on strategic analysis instead of firefighting.

I share real-world examples of how companies solved common modeling mistakes, reduced query times, and stabilized their budgets. Whether it’s pre-aggregating metrics, partitioning facts, or compactly modeling time ranges, these patterns deliver tangible results for data engineers, analysts, and decision-makers alike.

Ready to transform your approach to data modeling and unlock the full potential of your dashboards? Stay tuned—this is one episode you don’t want to miss. Subscribe now and take your data-driven vision to the next level!

#datamodels #slowdashboards #dataengineering #datagovernance #datapipeline

CHAPTERS:
00:00 - Intro
00:25 - Modeling Mistakes Breaking Data Pipelines
02:32 - Skipping Cumulative Tables in Modeling
08:39 - Issues with Fact Table Design
14:02 - Time Modeling: Row Explosion vs. Date Ranges
14:21 - Understanding Time Ranges in Data Modeling
17:37 - Normalizing Periods for Better Data Structure
18:25 - Relational Joins vs. Graph-Shaped Logic
23:59 - Synthesis: Common Patterns in Data Fixes
25:54 - Growth Path for Data Engineers
26:17 - Summary and Key Takeaways