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Creating Scalable Automations With Your Data
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🎙️ Why Point-to-Point Automation Is Broken—and What to Do Instead | Data Fuel Podcast
🔔 Subscribe for smarter systems, cleaner data & automation that actually scales
📢 Your Zaps and automations are breaking—and it’s not your fault. Tools like Zapier and Make are powerful, but when used in point-to-point setups, they create fragile, error-prone spaghetti systems. In this episode of Data Fuel, we break down:
✔️ Why point-to-point automation falls apart at scale
✔️ How a centralized data warehouse solves 80% of your issues
✔️ Step-by-step plan to reroute your automations through a warehouse
✔️ Real-world use cases and tools to get started
⏱️ Chapters
00:00 – The Problem with Point-to-Point Automation (Zapier, Make, Airtable, etc.)
01:30 – How a Centralized Data Warehouse Becomes Your Automation Hub
02:10 – The Hub-and-Spoke Model: Replace Chaos with Clean Data
03:00 – Benefits: Data Normalization, Less Reliance on Fragile APIs
05:20 – Data Modeling 101: Linking IDs Across Tools (HubSpot, Billing, PM)
06:00 – Why You Should Push from Warehouse to Apps (Not the Other Way Around)
06:45 – Automation Use Case 1: Invoicing from a Ready-to-Bill Table
07:30 – Automation Use Case 2: Weekly Reporting Without Breaks
08:25 – Getting Started: The 80/20 Rule for Automation Refactoring
09:35 – Final Thoughts: Automate Smarter, Not Harder
✅ Key Takeaways
✔️ Point-to-point automations break when data or tools change—even slightly
✔️ A centralized data warehouse (Snowflake, BigQuery, Postgres) creates structure and trust
✔️ Run automations from your warehouse using Zapier or Make, not directly from source tools
✔️ Fix 80% of your problems by starting with one high-friction automation
✔️ Warehouse-driven automation = better data integrity, scalability, and maintainability
💡 Tools Mentioned:
- Zapier / Make
- Snowflake, BigQuery, Postgres
- Stitch, Fivetran, Airbyte
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