Webinars

Converged Analytics: Copy Once, Query Everywhere

Most attempts to unify analytics still move data to get the job done: faster pipelines, tighter syncs, more automated ETL. That's real progress, but it's still movement — and every copy created has a cost, whether it shows up as stale data, bloated bills, or lost control over where data lives and who can touch it.

Building Postgres High Availability That Holds Up Under Pressure

We dig into the scenarios teams most often overlook: replication lag during a failover, split-brain risks, fencing a demoted primary, and why automated failover can make an outage worse if not done carefully.

Multimodel Data Platforms in the Agentic Era

Join Lizzy Nguyen (EDB) and featured speaker Indranil Bandyopadhyay (Forrester), to dissect the multimodel data-platform market: where it's heading, what a converged approach unlocks, what enterprise buyers need to watch out for, and how to approach adoption.

From Live Transactions to In-Database Analytics and Machine Learning

Skip the CDC and ETL step between a transaction and your analysis.

Getting a live transaction in front of an analytical query or a machine learning workload usually means extra steps: a separate CDC tool captures the change, a custom ETL job transforms and loads it, and by the time the data lands in the warehouse, the moment to act on it has often passed.

Stop Managing Silos: A Modern Analytics Architecture Built on Postgres

We address the real trade-offs between converged and specialist systems, and what you need in place to make it perform.

Stateful on Kubernetes: Running Postgres the Right Way

We dig into the failure scenarios teams overlook, the trade-offs between self-managed and operator-managed approaches, and what it takes to build a Kubernetes-native Postgres setup you can actually trust in production.