EDB integrates analytical and transactional workloads on Postgres
EnterpriseDB (EDB) has unveiled a converged analytics capability for its managed EDB Postgres AI database service, designed to bring operational (OLTP) and analytical (OLAP) processing closer together. The move comes weeks after Databricks introduced its own Lakehouse Transaction and Analytical Processing (LTAP) offering, based on Neon Postgres, with a similar goal of enabling AI agents to reason over fresh data without delays.
EDB’s approach is distinct, however, because it keeps Postgres as the operational source of truth and uses Apache Iceberg as a shared catalog layer. This allows analytical engines such as ClickHouse, WarehousePG, and Spark to query the same data through a common catalog without requiring separate copies or ETL pipelines. Operational data remains in Postgres while historical and tiered data is stored in Iceberg-managed object storage.
“Databricks is building from the lakehouse outward, trying to pull transactional capability in through Lakebase,” said Max Romanenko, chief engineering officer at EDB. “We’re building from the operational layer with Postgres, which is where enterprises already run their most critical workloads, and expanding from there.”
Data sovereignty and predictable economics take center stage
EDB’s architecture targets enterprises that want AI and analytics capabilities without moving sensitive data into a cloud-managed platform. Romanenko emphasized that “for us, it’s always been about the data sitting on infrastructure the customer owns and controls.” This stance resonates with CIOs focused on sovereignty, regulated data, and hybrid deployment models.
Industry analysts noted that EDB’s promotion of control could appeal to organizations with strict compliance requirements. The per-core pricing model offers more predictable costs than consumption-based cloud data platforms, where query volumes and AI workloads can cause bills to fluctuate. However, predictable bills are not necessarily lower. “The hardware requirements for high-speed operational data processing are higher and relatively more expensive compared to cheap lakehouse storage,” warned Igor Ikonnikov, advisory fellow at Info-Tech Research Group.
EDB’s architecture also simplifies data governance by reducing the number of platforms needing management. Since operational, analytical, and AI workloads can access data through a common Postgres-Iceberg foundation, enterprises avoid deploying and governing multiple specialized data stores. This results in fewer systems to license and secure, according to Devin Pratt, research director at IDC.
Reducing architectural complexity for engineering teams
Converged analytics cuts down the number of systems developers must integrate and maintain, while eliminating much of the pipeline work traditionally required to move data between transactional and analytical systems. “Zero-ETL means far less plumbing to build and break, so engineers spend their time creating value,” Pratt said.
EDB and Databricks are not alone in pursuing this convergence. Snowflake has been expanding support for operational workloads by embracing open table formats, and Microsoft has combined transactional and analytical services under its Fabric platform. The trend reflects a broader industry shift toward enabling agentic AI systems that need immediate access to both operational and historical data.
Autonomous database capabilities evolve
In addition to converged analytics, EDB made generally available what it calls an “agentic database” feature that automates routine administration tasks. The system continuously monitors hundreds of operational and performance metrics, detects anomalies, recommends corrective actions, and can automatically apply fixes where policies permit. EDB claims these automated agents can help optimize and tune databases up to ten times faster.
While some analysts see this as an evolution of autonomous database concepts rather than a wholly new category, EDB differentiates by extending autonomous capabilities with AI-driven reasoning, automated remediation, and governance controls that allow enterprises to determine how much authority the system receives. The feature builds on years of work from vendors like Oracle while aiming to deliver more proactive management for Postgres environments.
As enterprises increasingly deploy AI agents across critical business processes, the demand for converged analytics will likely intensify. EDB’s Postgres-first strategy, combined with a focus on data sovereignty and predictable costs, positions it as a viable option for organizations seeking to reduce architectural complexity while maintaining control over their data infrastructure.
Source: InfoWorld News