4 highlights from EDB Postgres AI

Posted by on 13 June, 2024

This post was originally published on this site

35% of enterprise leaders will consider Postgres for their next project, based on this research conducted by EDB, which also revealed that out of this group, the great majority believe that AI is going mainstream in their organization. Add to this, for the first time ever, analytical workloads have begun to surpass transactional workloads.

Enterprises see the potential of Postgres to fundamentally transform the way they use and manage data, and they see AI as a huge opportunity and advantage. But the diverse data teams within these organizations face increasing fragmentation and complexity when it comes to their data. To operationalize data for AI apps, they demand better observability and control across the data estate, not to mention a solution that works seamlessly across clouds.

It’s clear that Postgres has the right to play and deliver for the AI generation of apps, and EDB has taken recent strides to do just this with the release of EDB Postgres AI, an intelligent platform for transactional, analytical, and AI workloads.

The new platform product offers a unified approach to data management and is designed to streamline operations across hybrid cloud and multi-cloud environments, meeting enterprises wherever they are in their digital transformation journey.

EDB Postgres AI helps elevate data infrastructure to a strategic technology asset, by bringing analytical and AI systems closer to customers’ core operational and transactional data—all managed through the popular open source database, Postgres.

Let’s take a look at the key features and advantages of EDB Postgres AI.

Rapid analytics for transactional data

Analysts and data scientists need to launch critical new projects, and they need access to up-to-the-second transactional and operational data within their core Postgres databases. Yet these teams are often forced to default to clunky ETL or ELT processes that result in latency, data inconsistency, and quality issues that hamper efficiency-extracting insights.

EDB Postgres AI introduces a simple platform for deploying new analytics and data science projects rapidly, without the need for operationally expensive data pipelines and multiple platforms. EDB Postgres AI’s Lakehouse capabilities allow for the rapid execution of analytical queries on transactional data without impacting performance, all using the same intuitive interface. By storing operational data in a columnar format, EDB Postgres AI boosts query speeds by up to 30x faster compared to standard Postgres and reduces storage costs, making real-time analytics more accessible.

Enterprise observability and data estate management

Even if data teams have made Postgres their primary database, chances are their data estate is still sprawled across a diverse mix of fully-managed and self-managed Postgres deployments. Managing these systems becomes increasingly difficult and costly, particularly when it comes to ensuring uptime, security and compliance.

The new capabilities of the recent EDB release will help customers create and deliver value greater than the sum of all the data parts, no matter where it is. EDB Postgres AI provides comprehensive observability tools that offer a unified view of Postgres deployments across different environments. This means that users can monitor and tune their databases, with automatic suggestions on improving query performance, AI-driven event detection and log analysis, and smart alerting when metrics exceed configurable thresholds.

edb data plane diagram EDB

Support for vector databases

With the surge in AI advancements, EDB sees a significant opportunity to enhance data management for our customers through AI integration. The strategy of the new platforms is twofold: integrate AI capabilities into Postgres, and simultaneously, optimize Postgres for AI workloads.

Firstly, this release includes an AI-driven migration copilot, which is trained on EDB documentation and knowledge bases and helps answer common questions about migration errors including command line and schema issues, with instant error resolution and guidance tailored to database needs.

In addition, EDB remains focused on optimizing Postgres for AI workloads through support for vector databases and AI workloads. With capabilities like the pgvector extension and EDB’s pgai extension, the platform enables the storage and querying of vector embeddings, crucial for AI applications. This support allows developers to build sophisticated AI models directly within the Postgres ecosystem.

In addition, EDB remains focused on optimizing Postgres for AI workloads through support for vector databases and AI workloads. The EDB Postgres AI platform streamlines capabilities by providing a single place for storing vector embeddings and doing similarity search with both pgai and pgvector, which simplifies the AI application pipeline for builders. This support allows developers to build sophisticated AI models directly within the Postgres ecosystem. The platform also enables users to leverage the mature data management features of PostgreSQL such as reliability with high availability, security with Transparent Data Encryption (TDE), and scalability with on-premises, hybrid, and cloud deployments.

EDB Postgres AI transforms unstructured data management with its new powerful “retriever” functionality that enables similarity search across vector data. The auto embedding feature automatically generates AI embeddings for data in Postgres tables, keeping them up-to-date via triggers. Coupled with the retriever’s ability to create embeddings for Amazon S3 data on demand, pgai provides a seamless solution to making unstructured sources searchable by similarity. Users can also leverage a broad list of state-of-the-art encoder models like Hugging Face and OpenAI. With just pgai.create_retriever() and pgai.retrieve(), developers gain vector similarity capabilities within their trusted Postgres database.

This dual approach ensures that Postgres becomes a comprehensive solution for both traditional and AI-driven data management needs.

Continuous high availability and legacy modernization

EDB Postgres AI maintains the critical, enterprise-grade capabilities that EDB is known for. This includes the comprehensive Oracle Compatibility Mode, which helps customers break free from legacy systems while lowering TCO by up to 80% compared to legacy commercial databases. The product also supports EDB’s geo-distributed high-availability solutions, meaning customers can deploy multi-region clusters with five-nines availability to guarantee that data is consistent, timely, and complete—even during disruptions.

The release of EDB Postgres AI marks EDB’s 20th year as a leader of enterprise-grade Postgres and introduces the next evolution of the company—one even more proudly associated with Postgres. Why? Because we know that the flexibility and extensibility make Postgres uniquely positioned to solve for the most complex and critical data challenges. Learn more about how EDB can help you use EDB Postgres AI for your most demanding applications.

Aislinn Shea Wright is VP of product management at EDB.

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

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