How do you architect a data stack that's ready for AI?

Every IT leader is being asked to connect more systems, feed more AI agents, and justify the infrastructure spend behind both. Celigo's Head of Data, Ray, shares how to architect a data stack from scratch, when real-time infrastructure is worth the cost and when it's pure overhead, and why AI has quietly turned data quality from a hygiene problem into a governance risk. What you'll learn: ▸ A practical, sequenced framework for architecting a data stack from scratch: where to store data, how to get it in, and what's actually worth ingesting ▸ Why real-time vs. batch is a cost and governance decision, not a technical one ▸ The four real cost centers of real-time data: compute, storage, infrastructure complexity, and the most expensive one — data correctness at scale ▸ Why AI has raised the stakes on data quality ▸ ETL vs. ELT, explained CHAPTERS 00:00 Meet Ray, Celigo's Head of Data 02:59 Why AI quietly raised the stakes on data quality 04:17 Using AI day to day: inferring strategy vs. amplifying execution 06:28 How Celigo manages data quality 08:41 ETL vs. ELT, explained 11:27 Real time vs. batch 14:23 The actual cost of real-time data 16:39 How to architect a data stack from scratch, step by step 20:58 Proactive vs. reactive: how Celigo decides what data's worth ingesting The stack you build today decides whether your AI agents are working from good data tomorrow. 🚀 Subscribe to the podcast for a new episode every two weeks: celigo.com/podcast 👉 Get a demo: https://www.celigo.com/request-a-demo/ #Celigo #DataGovernance #DataArchitecture #ETL #ELT #Snowflake #EnterpriseAI #CIO #BuildersHubPodcast #AIagents