SQL, Scripts, and Automation: Navigating ELT Strategies in the Lakehouse
Автор: Dremio
Загружено: 2026-01-14
Просмотров: 7
Traditional ETL pipelines can’t keep up with today’s lakehouse architectures, AI use cases, and real-time analytics demands. In this session, we explore why separating ingestion from transformation (EL + T), versioning SQL with Git, implementing CI/CD workflows, and applying DataOps principles are essential to building reliable, scalable, and fast lakehouse transformation pipelines. You’ll see why slow nightly batches, hidden scripts, and manual deployments block productivity—and how tools like dbt and modern SQL workflows make data transformation reproducible, testable, and automation-ready.
We also dive into how agentic AI accelerates development in the lakehouse: from generating SQL and dbt models to debugging queries, validating transformations, and improving documentation automatically. Combined with the Dremio MCP server, developers and analysts can streamline ELT, automate quality checks, and move faster while maintaining governance and accountability. If you’re modernizing your data platform or rethinking your ELT strategy, this talk gives you a practical blueprint for building scalable, automated, AI-ready transformation workflows in the lakehouse.
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