Operations | Monitoring | ITSM | DevOps | Cloud

In Episode 4 of Elephant in the Room, we tackle a question many teams struggle with: how do you protect sensitive customer data from your own developers while still running and improving your web application? Join Jay Miller (Aiven) and Tim Schilling of Djangonaut Space as they explore a real-world privacy challenge. Djangonaut Space’s application process requires collecting personal information from applicants, but reviewers and contributors should not have access to identifying details. The goal: enable fair review and active development without exposing sensitive data.

Giannis Polyzos sits down with Hugh Evans to discuss whether Paimon is really just Iceberg, only better.

Welcome to Elephant in the Room – presented by Aiven, the live series where we explore the real-world challenges developers face when working with PostgreSQL, the database that everyone depends on but few fully leverage. In Episode 2, Jay Miller, Staff Product Advocate at Aiven, is joined by Alexis Roberson, Senior Developer Educator and feature flag enthusiast, to break down how feature flags help teams ship faster, reduce risk and improve user experiences in PostgreSQL-backed applications.

Giannis Polyzos chats with Hugh Evans about whether batch data processing is truly “good enough".

Season 2 of Get Kafka-Nated kicks off with Giannis Polyzos, member of the Fluss PMC, for a deep dive into one of data infrastructure’s longest-running debates: streaming vs. batch, or both? What we’ll cover: Timestamps: Useful links.

Data catalogs are no longer just for compliance - they are the essential context layer for AI. While LLMs are powerful, they fail without the "tribal knowledge" found in metadata. This session explores how to transform your catalog from a static repository into an active nervous system for AI agents.

Some queries are hard to solve with "basic" RAG. When questions require multi-step reasoning, full-document understanding (not just chunks), or aggregating many results that match specific criteria, simple retrieve-and-generate pipelines break down, we need agentic RAG. But this added capability comes at a cost: as agents plan, search, read, and iterate, they quickly use up a lot of context, which both degrades answer quality and increases costs and latency.

In this video, Senior Developer Advocate Cole Bowden walks you through the key similarities and differences that exist in InfluxDB 3 Core and InfluxDB 3 Enterprise. As an open source offering, Core thrives at data collection on the edge and providing real-time insights into fresh data, while Enterprise includes support, compaction for performant historical analysis over wide windows, better scaling and security for enterprise-scale operations.