Webinars

Upcoming Webinars

(EDT)

Own Your Data Lifecycle: The Converged Analytics Series

Analytics is converging — Postgres, open lakehouses, and specialized engines are merging into one governed platform. This six-part series traces the full arc: why convergence is happening now, how Postgres data becomes a live analytical estate on Apache Iceberg, and how ClickHouse and WarehousePG query it together with zero duplication. From there, live demos show the model in action — real-time personalization, log and observability consolidation, and governed AI agent access — proving that moving less data, not moving it faster, is what makes an analytics stack defensible, auditable, and yours to control.

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(EDT)

Governing EDB Postgres® AI at Scale with Red Hat Ansible

Part 1 of this series closed the deployment gap: automated provisioning, sub-30-second failover, and validated HA/DR that took one customer from 10 shared Postgres clusters to more than 500 individually managed instances, with automated failover replacing all hands incident response.

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On-demand

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.

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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.

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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.

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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.

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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.

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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.

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