ETL vs ELT: Powering Data Pipelines for AI & Analytics

Learn more about ELT vs. ETL here → https://ibm.biz/BdnNJN Think of data integration like a water filtration system: ETL and ELT are two different ways to prepare data for analytics and AI. Caroline Garay explains ETL, ELT, and TETL, exploring data pipelines, cloud data platforms, transformation strategies, governance, compliance, performance, and cost tradeoffs. Learn how modern teams choose the right data integration architecture to deliver trusted data for analytics, reporting, and AI workloads. 🚀 00:00 - Understand Data Integration for AI and Analytics 00:26 - Explore How ETL Transforms Data Before Loading 01:34 - Discover When ETL Is the Better Choice 02:43 - Learn How ELT Uses Modern Cloud Platforms 03:30 - See Where ELT Excels for Analytics Workloads 04:24 - Compare ETL vs ELT for Cost, Performance, and Compliance 05:36 - Uncover the Role of TETL in Data Pipelines 06:18 - Choose the Right Data Integration Strategy AI was used in the creation of the transcript and metadata for this video. AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdnNJW #ai #etl #dataanalytics #dataengineering #dataintegration --------------------------------------------------------------------------------------------------------- Find us on YouTube: 🔵 IBM Technology:    / @ibmtechnology   🔵 IBM:    / @ibm   🔵 IBM Developer:    / @ibmdeveloperadvocates