Modernizing data platforms is no longer just about moving to the cloud. Organizations are under pressure to build data environments that not only scale, but also support real-time analytics, governed data access, and AI-driven applications.
However, many teams get stuck between migration and real business impact. Legacy architectures, fragmented pipelines, and unclear roadmaps often slow progress and limit the value of AI initiatives.
DBTA recently held a webinar, Modernizing Data Platforms for AI & Cloud, with Jack Christie, product marketing at EDB and Kelly Kohlleffel, senior director, global partner sales engineering at Fivetran, who shared how organizations are successfully modernizing their data platforms from initial migration through optimization and into full AI enablement.
According to Christie, there are several trends driving modernization right now. This includes:
- Agents stuck in staging: Gartner predicts that 40% of agentic AI projects risk cancellation by 2027.
- Legacy blockers: Oracle licensing and Broadcom pricing stall modernization before the AI conversation even starts.
- Data sprawl: Transactions, analytics, and AI live in separate systems—more cost, more risk, more to govern.
- Cloud alone isn’t enough: Lift-and-shift adds cost and still leaves teams without the architecture or skills for AI.
EDB Postgres AI provides a sovereign and open-source AI and data platform for the agentic enterprise, Christie said. The platform can be deployed on cloud, on-prem, or fully air-gapped—and it’s the same platform. EDB is the leading Postgres contributor and now its solution is extended for agentic AI.
Data now powers AI agents, operations, and real-time systems, not just reporting, Kohlleffel explained. The Modern Data Stack cannot support continuous, scalable, AI workloads. There are two forces closing off data: SaaS applications are monetizing and controlling the data they generate on customers’ behalf. And major data platforms are building increasingly integrated, proprietary ecosystems.
The next evolution of the modern data stack built for an agentic AI era is Open Data Infrastructure, Kohlleffel said. The pillars of Open Data Infrastructure include:
- Automated, standards-based ingestion and transformation
- An open data lake foundation
- Unified activation, semantics, and AI consumption
Open Data Infrastructure is committed to open standards. Keep your data portable, and free from vendor lock-in. It is interoperable by design, evolve your stack without rebuilding. It is flexible and future-proof. Scale, adapt, and optimize without re-platforming. And it is built for AI agents. Enable a data strategy built to scale across AI.
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.