In this episode of Data Builders Club, Henry Clavo shares lessons from over a decade in data engineering across healthcare and government, exploring what it really takes to build reliable data systems in the age of AI. From ETL best practices and data quality to AI hallucinations, observability, and the future of data engineering careers, this conversation is packed with practical insights for modern data teams.

What exactly is a Catalog, and why has it become such a critical component of the modern Lakehouse architecture and AI workloads? In this episode, we break down the differences between technical catalogs (metastores) and business catalogs, explore how catalogs enable governance and interoperability, and explain why the Iceberg REST Catalog specification became the open standard for sharing Iceberg tables across platforms without vendor lock-in.

Ready to scale your corporate support from chaotic firefighting to structured flight control? In this comprehensive overview, we explore how Cloudera leverages its own technology stack to develop Agent TAM—a powerful suite of autonomous AI agents designed to unlock institutional knowledge, streamline customer workflows, and eliminate technical debt. Whether you want to build an automated Case Analyzer or an intelligent planning companion, this guide provides the exact architectural blueprint to transition your engineering teams from reactive firefighting to proactive, data-driven automation.

As Superhuman expanded its AI platform across Grammarly, Coda, Superhuman Mail, and Superhuman Go, more of the business began to rely on timely data from Salesforce, Outreach, Pardot, Stripe, Zendesk, Qualtrics, and other third-party systems. The challenge went far beyond moving data into Databricks. Go-to-market, finance, and customer teams needed faster, reliable access to trusted data without turning every new data request into weeks of custom engineering.

As AI accelerates the pace of change, demanding fresher data, diverse formats, and support across multiple engines, many teams discover their infrastructure was built for reporting, not real-time AI at scale. Open Data Infrastructure is redefining how organizations design for analytics, operations, and AI. By leveraging Fivetran as an interoperable data foundation, organizations can embrace open standards, separate storage from compute, and keep data portable across clouds and engines, preserving adaptability while scaling AI and operational workloads with Databricks.

Healthcare organizations operate some of the most complex data environments, spanning thousands of systems across clinical, financial, and operational domains. At Inova Health, this complexity created an opportunity to rethink how data could better support analytics and AI at scale.