How Startups Should Build Their Data Stack in 2026 скачать в хорошем качестве

How Startups Should Build Their Data Stack in 2026

If you're a founder or executive at a startup, how you set up your data stack determines whether you get answers in 30 minutes or 6 months. Most startups follow the enterprise playbook from 2020: data warehouse, ETL tool, transformation layer, BI tool. Four products, four bills, probably a data engineer to keep it running. But there's another way: start AI-native from day one and skip all of that. This video breaks down both approaches so you can decide what makes sense for your stage. 🔗 Links: Definite: https://www.definite.app/ Connectors: https://www.definite.app/connector-db Pricing: https://www.definite.app/pricing Fi (AI Agent): https://www.definite.app/fi ⏱️ Chapters: 0:00 The choice you need to make 0:45 How the traditional data stack works 1:44 The part that surprises most founders 1:56 Why this doesn't work for startups 2:42 The AI-native alternative 3:11 Definite demo 4:50 What AI-native unlocks 5:29 When to use traditional vs AI-native 6:03 The move for 2026 📊 What You'll Learn: The 4-tool traditional data stack (Snowflake, Fivetran, dbt, Looker) Why the traditional approach breaks down at startups What "AI-native" actually means for data infrastructure When to use each approach based on your stage 💡 Who Is This For: Founders at seed, Series A, or early Series B startups Technical co-founders evaluating data infrastructure Executives who need answers, not infrastructure #startup #datastack #analytics #ai #founder #dataengineering #snowflake #definite