Systems | Development | Analytics | API | Testing

Semantic layers and ontologies have moved from nice-to-have data modeling tools to the foundational engine required for enterprise AI. In this episode, Raman Tallamraju, Senior Director and Head of Enterprise Data Architecture and Engineering at Vanguard, breaks down how Vanguard is architecting its AI semantic layer to turn scattered institutional knowledge into reliable, agent-ready context. He shares why autonomous agents expose decades of hidden data debt, how to bridge domain-specific definitions like clients versus prospects, and how to balance building a unified semantic layer with a pragmatic, federated data operating model.

The challenge for most data teams isn’t a lack of ideas—it’s the time it takes to turn those ideas into something usable. In this session, Steffen Bischoff, Chief Architect Data at Qlik, follows a single dataset from a core system through its entire journey to becoming a governed data product. You’ll see pipelines created by describing intent instead of writing code, versioned in Git, then curated, quality-checked, and documented with the help of specialized agents. From there, the data product is made available to the AI tool of your choice through the Qlik MCP Server.

Use Qlik Answers to manage and create your Data Products!

Now that AI coding tools have put development capabilities into more hands, prototypes are becoming business-critical applications almost overnight. Shanea Leven sees an opportunity for a new generation of builders, provided the infrastructure around their applications keeps pace. Shanea explains how organizations can give developers and new technical employees room to build while maintaining the standards required for enterprise software..

Fivetran vs Talend in 2026: Which ETL Tool Is Better? Fivetran and Talend follow different approaches to data integration. Fivetran focuses on managed, warehouse-first ELT with automatic connector maintenance and schema handling. Talend supports ETL, ELT, data quality, governance, APIs and hybrid deployment across cloud and on-premises environments.

Fully managed Confluent Cloud for Apache Flink combines Flink SQL for data engineers with programmatic capabilities like the Table API, User-Defined Functions (UDFs), and Process Table Functions (PTFs) for developers. You can build mission-critical use cases with programmatic capabilities while using familiar dbt- and SQL-based workflows.