EDB is proud to unveil our brand new custom-built chatbot, EDB Envoy. If you look to the bottom right of this page, you’ll see Envoy ready and waiting to deliver answers to your Postgres and database questions faster than ever before. It’s part of our mission to help our customers get the most out of their data in the AI generation. Thanks for being here with us on this digital journey ..read more

Migrating from legacy databases to Postgres is a complex process that extends beyond technical considerations to encompass a broad spectrum of change management activities. By carefully planning and executing each phase of the migration, organizations can minimize disruptions, engage stakeholders effectively, and realize the full benefits of Postgres ..read more

How we ended up with two JSON parsers in PostgreSQL ..read more

The world of data and AI is evolving faster than ever. In fact, it’s estimated that 90% of the world's data was generated in the last two years. Because of this seismic shift, it’s imperative for businesses to harness the power of their data, in order to stay competitive in the next generation of digital transformation. And because of our expertise in Postgres, EDB is perfectly positioned to help our customers make the critical shift to next generation operating models ..read more

A few weeks ago I covered the basics of tuning checkpoints, and in that post I also mentioned autovacuum as the second common source of performance issues (based on what we see on the mailing list and at our customers). Let me follow-up on that with this post about how to tune autovacuum, to minimize the risk of performance issues. In this post I'll briefly explain why we even need autovacuum (dead rows, bloat and how autovacuum deals with it), and then move to the main focus of this blog post - tuning. I’ll go over all the relevant configuration options, and some basic rules for tuning them ..read more

EDB Postgres solutions have long addressed the highest security standards demanded by federal, state, and local Government agencies, as well as highly regulated businesses like financial services, healthcare, and telecommunication ..read more

Enhancing performance, observability, stability, and security of the unified EDB Postgres AI platform This blog was co-authored by David Wagoner, Simon Notley, and Tim Boutin. As part of strategic planning leading to the EDB Postgres® AI announcement, our Product Management and R&D teams established a quarterly release cadence to drive the innovations that provide the foundation of this unified transactional, analytical, and AI workloads platform. Today’s EDB Postgres AI Cloud Service roadmap announcement extends features delivered in the initial EDB Postgres AI Q2 Release with upgrades th ..read more

EDB is proud to be part of the vibrant open source community. Earlier this month, EDB Chief Database Scientist, Robert Haas introduced a new mentorship program designed to support and nurture PostgreSQL code contributors. Robert's vision for this program is rooted in his conversations with experienced PostgreSQL developers. He discovered a common thread: support, encouragement, and meaningful, positive feedback played a crucial role in their perseverance and success. Whether it was validation of their work, support for a major patch, or an unexpected promotion to committer, these interactions ..read more

The recent launch of the unified EDB Postgres® AI intelligent platform brings an unparalleled approach for addressing challenges associated with managing transactional, analytical, and AI workloads across hybrid and multi-cloud environments. EDB Postgres AI solves the challenges involved in migrating from legacy data infrastructures to an open source database approach that fosters innovation and integration with modern cloud-native stacks. As part of our platform, the EDB Postgres AI Cloud Service enables organizations to deploy fully managed EDB Postgres on Amazon Web Services (AWS ..read more

This post continues from my report on Random Numbers. I have begun working on a random data generator so I want to run some tests to see whether different random number generators actually impact the overall performance of a data generator. Let’s say we want to create random data for a table with 17 columns, something that contains the details of a company’s order information: Order ID Line number Customer ID Part ID Supplier ID Order date Order priority Shipping priority Quantity Item price Total price Discount Revenue Cost Tax Commit date Shipping mode This is intended to illustrate how many ..read more