WORKLOAD: RAPID ANALYTICS FOR POSTGRES
Unleash an enterprise-grade Postgres® Lakehouse for analytical workloads
Run high-performance analytical queries on operational data and consolidate OLAP and OLTP workloads directly within your trusted Postgres environment, harnessing open source Apache projects and cost-effective object storage for faster insights and fewer ETL pipelines.
EDB Postgres AI
EDB Postgres AI is an intelligent platform for transactional, analytical, and AI workloads that meets you wherever you are — on premises or on any cloud, anywhere, and on appliances of choice. Data is stored in a columnar format, so you can go straight to the data source and spin up on-demand analytics clusters.
Explainer Video
Explainer Video
EDB Postgres AI: Rapid Analytics for Postgres
EDB Postgres AI: Rapid Analytics for Postgres
For data-driven enterprises, extracting timely insights from complex data remains a constant challenge.
Complex management
Organizations have to deal with intricacies and fragmentation when moving data from transactional databases to specialized analytical engines for running analytical queries and making business decisions.
Scalable analytics capabilities
Postgres by itself is not well-suited for running analytical queries. Executing them on a transactional instance is slow and can impact actual production workloads, leading operators to either leverage extensions and tools such as materialized views or analytical capabilities, or extract, transform, and load (ETL) the data into a separate, purpose-built database for OLAP workloads.
Lack of modern data management tools
Developing ETL data pipelines is difficult and adds complexity to the technology stack, as well as operational overhead that takes away from building solutions for customers. The advent of NoSQL data stores and the current craze around vector databases for generative AI use cases have further exacerbated the complexity of big data movement.
Overhead management
Postgres adoption has skyrocketed, and organizations now have Postgres databases running all over the place, making it complicated for DBA and IT teams to account for and manage these distributed data systems.
Accelerate analytical query performance on Postgres without impacting your transactional workloads.
Easy-to-manage lakehouse
EDB Postgres AI abstracts away the underlying technologies (object storage, columnar formats, DataFusion engine) so customers can continue using the standard Postgres interface for both transactional and analytical workloads. It also reduces fragmentation and dependencies on other external systems.
Fast analytical queries
Instead of using the regular Postgres query engine, the EDB Postgres AI Analytics Node uses a vectorized query engine that is optimized for the columnar data formats. This allows analytical queries to run 30x faster compared to Postgres.
Single data platform
The Analytic Node replicates tables to Lakehouse Tables stored in object storage (like Amazon S3) using columnar formats with Delta Lakes. This provides better performance for analytical queries against operational or transactional data directly within Postgres, eliminating the need for separate analytical engines and complex ETL pipelines.
Rapid analytics: The challenges you’ll face and the outcomes you can deliver with EDB
Rapid analytics for Postgres workloads architecture
Key features of the EDB solution
AI Lakehouse Node
A new resource that can be launched from the EDB Postgres AI UI, allowing users to run analytical queries on data stored in open table formats in object storage at much faster speeds compared to traditional transactional Postgres.
Managed Storage Location (MSL)
A storage location defined by a region, cloud service provider, and prefix, which can be created in either EDB Postgres AI or bring-your-own-account (BYOA) environments, and is used to store Lakehouse Tables, logs, backups of transactional databases, and eventually other data types such as AI and unstructured data.
EDB Postgres AI Analytics Sync
Enables users to snapshot tables from an EDB Postgres database and copy them into Lakehouse Tables, initially in the Delta Lake format, within an MSL.
Integration with Object Storage Services
EDB Postgres AI integrates with object storage services such as Amazon S3 and open table formats such as Delta Lake, with support for Apache Iceberg coming soon, to enable storing and querying large volumes of data in columnar formats.
Fast Execution of Queries
Apache Arrow, an in-memory columnar format, and Apache DataFusion, a vectorized SQL query engine designed to work with data stored in Arrow format, are utilized to enable fast execution of analytical queries on the data stored in object storage.
Single Pane of Glass
A unified interface helps to monitor and manage all your Postgres databases, including EDB's managed cloud database-as-a-service, on-premises Postgres instances, and third-party managed Postgres offerings such as Amazon RDS and Microsoft Azure Database for PostgreSQL.
