EDB发布Postgres AI平台自治数据库与治理功能，但属于非上市公司产品宣传，缺少可交易催化

이전 기사보기
다음 기사보기
EDB Adds Autonomous Database and Governance to Postgres AI Platform
Character Size Settings
가
The body content of the article will be changed to this letter size.
스크롤 이동 상태바
IT·Gaming
Unified data layer unifies AI, analytics, and control to slash TCO and accelerate tuning by 10x
Hyun-Seon, Park
Published 2026.07.06 08:36
Reply 0
이 기사를 공유합니다
Illustration of EDB Postgres AI's autonomous database management capabilities. (Source: EDB)
EnterpriseDB (EDB), a sovereign AI and data platform provider, has added Agentic Database, Converged Analytics, and governance capabilities to its open-source-based AI data platform, EDB Postgres AI (EDB PG AI).
The new capabilities unify AI, analytics, and governance at the data layer on a single Open Postgres platform that organizations can own and control. According to EDB, the platform enables relational, analytical, vector, and agentic workloads to be processed within a single environment.
EDB said the Agentic Database capability transforms traditional PostgreSQL from a manually managed database into an autonomous database. The system continuously monitors more than 200 operational and performance metrics, automatically analyzes areas requiring optimization, and applies improvements within the limits defined by enterprise policies. This automates database tuning, scaling, and troubleshooting while proactively identifying and addressing issues before they result in service disruptions.
Organizations can configure each task to run automatically, require human approval, or execute only during scheduled maintenance windows. All actions are recorded in an audit trail.
EDB PG AI supports unified management of relational, JSON, time-series, geospatial, and vector data through a single SQL interface, with access control and policy enforcement performed directly at the data layer.
EDB claims the platform can perform database optimization and tuning up to 10 times faster, identifying issues and recommending solutions within minutes that previously required an experienced database administrator (DBA) to spend 60 to 90 minutes analyzing. When permitted by policy, those optimizations can also be applied automatically. EDB said these improvements can increase application performance by up to eight times while enabling most performance issues to be detected before deployment into production environments.
The company also highlighted EDB PG AI's zero-ETL architecture, which eliminates the boundary between operational and analytical data. Without requiring data movement or replication, all data can be used for real-time analytics and large-scale data warehousing. The platform supports deployment across on-premises, hybrid cloud, and multi-cloud environments while using open standards for both data formats and analytics engines.
EDB also claims EDB PG AI delivers up to 30 times faster query performance on a single node, up to 99 times higher analytics performance when using GPU-accelerated Apache Spark, up to 52% greater scalability in high-concurrency environments, and up to 58% lower total cost of ownership (TCO) through a predictable core-based licensing model.
The company said the platform also enhances AI agent performance by providing high-performance data retrieval capabilities. Because EDB PG AI unifies vector search, structured data, unstructured data, and analytics within a single query layer, AI agents can retrieve the information they need without requiring separate vector databases or storage systems.
Citing an independent benchmark conducted by McKnight Consulting Group, EDB said EDB PG AI achieved up to 99.4% lower query latency than Databricks, up to 93% lower query latency than MongoDB, the highest retrieval accuracy with a Recall@10 score of 0.911, 17% higher retrieval accuracy than Databricks and 26% higher than MongoDB, query availability within 12 milliseconds (ms) after new data is stored, and 99.7% faster data freshness than Databricks, which required 3.8 seconds.
AI governance is also implemented directly at the data layer. Using PostgreSQL's role-based access control (RBAC) and row-level security (RLS), the platform governs AI agent access to data. According to EDB, agent identities, roles, purposes, permissions, and enterprise policies are enforced at the database level, allowing all activities to be managed under a unified governance framework.
In addition, all activity is tracked through session-level auditing, and because policies are enforced where the data resides, organizations do not need to deploy separate security engines or runtime environments. EDB said the governance capability is currently available as a preview release.
hspark@thelec.kr
View other news
Copyright © The Elec Inc. No unauthorized transfer and redistribution
Comments
Name
Password
Enter comments
0
/ 400
Submit
Sort the comments.
Newest
Recommended
Top Comments
Best comment
It is automatically exposed by adding up the number of replies and recommendations.
close
더보기
Delete comments
Deleted comments cannot be recovered.
Do you still want to delete it?
Delete
Modify the comments
You can modify comments only within 1 minute after writing.
Text
Modify
My comment
Most Read
1
Corning Highlights Glass Bridge for CPO and Glass-Core Packaging
2
Canada Set to Select Submarine Supplier as Hanwha Ocean Makes Final Push
3
Samsung, SK hynix Consider Massive Honam Semiconductor Investment, Raising Questions Over Yongin Plans
4
Apple to Begin Foldable iPhone Production in July After Resolving Hinge Issues
5
SK hynix Files ADR Registration Statement for Nasdaq Listing
6
Qualcomm Unveils Dragonfly Data Center Platform, Wins Meta Deal
7
Micron's Record Results Quiet AI Bubble Concerns as Operating Profit Hits $33.3 Billion
8
LG Display Says AI-Driven Manufacturing and R&D Save More Than 200 Billion Won Annually
9
High-Purity CO2 Supply Tightens for Advanced Semiconductor Cleaning
10
Samsung, SK to Invest 800 Trillion Won in Four Semiconductor Fabs in Southwest Korea
Editor’s Picks
Samsung SDI Supplies AI Data Center Battery Cells to Taiwan's Simplo Technology
​Samsung Reaffirms 1.4nm Mass Production in 2029, Sets SF1.4+ for 2030
Samsung Display to Expand 6th-Generation OLED Line in Asan
More Stories
KDCA Advances ITU International Standards for AI Agent Identity
Snet Group Restructures Organization for Second Half of 2026
Is Meta's Cloud Plan Really a Sign of an AI Semiconductor Investment Bubble?
TwelveLabs Raises $100 Million in Series B Led by NEA and NAVER Ventures
Nokia Highlights Network Evolution for the AI Supercycle