Systems | Development | Analytics | API | Testing

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.

Unlock the full potential of your data fabric and accelerate your AI journey with Cloudera Data in Motion. Many organizations struggle with massive amounts of diverse data spread across different formats, vendors, and locations—whether in the cloud or on-premises data centers. Cloudera provides the scalable, performant data services needed to move and process this information in real-time.

Hey, did you know your AI agents could be making decisions based on data they were never meant to see? When enterprise data governance is fragmented across separate tools, it creates severe blind spots. Rogue AI agents can over-index, modify, or even accidentally delete production databases simply because proper data guardrails weren't uniformly enforced. In this video, we tackle the root cause of why 79% of enterprise AI initiatives stall and show you how to build a unified data fabric that secures your hybrid estate.

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.

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.

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.

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.