en.Wedoany.com Reported - Enterprise database service provider EnterpriseDB (EDB) has introduced converged analytics capabilities for its managed EDB Postgres AI database, aiming to help enterprises leverage AI agents to operate on new data in real time without relying on traditional data pipelines and copies. This move follows Databricks' launch of a Lakehouse Transaction and Analytical Processing (LTAP) product based on Neon Postgres, with both vendors working to more closely integrate operational processing (OLTP) and analytical processing (OLAP).

EDB's Chief Engineering Officer, Max Romanenko, stated that EDB's approach is built from a fundamentally different starting point than Databricks. Databricks expands outward from the lakehouse, attempting to introduce transactional capabilities through Lakebase; EDB, on the other hand, starts from Postgres's operational layer, where enterprises already run critical workloads. EDB uses Postgres as the operational source of truth and employs Apache Iceberg as a shared catalog layer to connect Postgres with ClickHouse, WarehousePG, and Spark compute engines. This allows operational data to remain in Postgres, while historical data storage resides in Iceberg-managed object storage. Analytics engines can query the same data through a common catalog without requiring separate copies or ETL pipelines. Romanenko emphasized that this architectural difference is crucial for target customers—enterprises that want AI and analytics capabilities but are unwilling to migrate sensitive data to cloud-managed platforms.

Stephanie Walter, Head of AI Stack Practice at HyperFrame Research, noted that EDB's emphasis on control will resonate with CIOs concerned about data sovereignty, regulated data, and hybrid deployments, enabling them to run AI and analytics closer to the data on enterprise-controlled infrastructure without creating another proprietary data asset. Ashish Chaturvedi, Executive Research Lead at HFS Research, believes that for CIOs managing analytics and AI budgets, EDB's converged analytics approach offers more predictable costs than Databricks's LTAP. EDB's per-core billing model makes costs easier to predict compared to consumption-based cloud data platforms, where bills can fluctuate due to query volume, AI workloads, and data processing demands. However, Igor Ikonnikov, Advisory Researcher at Info-Tech Research Group, warned that predictable billing does not necessarily mean lower costs, as the hardware required for high-speed operational data processing is more demanding and relatively more expensive. Devin Pratt, Research Director at IDC, stated that EDB's architecture could also simplify data governance by reducing the number of platforms enterprises need to manage.

EDB's converged analytics aims to simplify operations for developers and data engineering teams. Walter believes the architecture reduces the number of systems that must be integrated and maintained, while eliminating much of the pipeline work traditionally required to move data between transactional and analytical systems. Pratt stated that zero ETL means fewer pipelines to build and tear down, allowing engineers to spend time creating value. Beyond EDB and Databricks, Snowflake is also expanding operational workload support by embracing open table formats, while Microsoft combines transactional and analytical services through its Fabric platform.

Converged analytics is just one part of EDB's updates to its Postgres AI platform. EDB has also made generally available what it calls "agentic database" capabilities, designed to automate routine database management tasks. The company stated that the system continuously monitors hundreds of operational and performance metrics, detects anomalies, recommends corrective actions, and can automatically apply fixes where enterprise policies allow, helping to optimize and tune databases up to 10 times faster. Walter views this more as an evolution of the autonomous database concept rather than a new category, as Oracle and other vendors have offered similar features for years. EDB's differentiation lies in extending these autonomous capabilities through AI-driven reasoning, automated remediation, and governance controls, allowing enterprises to decide how much authority the system receives.

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