Announcing Inkless clusters: Cloud Kafka done right
Inkless clusters upgrade classic topics with 90% faster recovery and 10x faster scaling with infinite storage, while adding diskless topics.
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The "Now" Problem: Why BESS Operations Demand Last Value Caching
Battery Energy Storage Systems (BESS) represent one of the most unforgiving environments for real-time data. Unlike a passive asset, a battery is a complex electrochemical system where safety and revenue are determined by split-second decisions. In this context, “average” latency can become a serious problem. Performance depends entirely on one key question.
Stop Choosing Tech Just Because It's Popular
Giannis Polyzos joins Hugh Evans to challenge the idea of choosing technology based on popularity.
Log Management for your Homelab with Fluentd and Aiven for PostgreSQL
Tired of not knowing why your home lab containers are crashing? In this video, we’ll walk through how to set up a consolidated logging pipeline using Aiven for PostgreSQL (with TimescaleDB), Fluentd and Grafana — all running on my upgraded Mac Mini home lab. The key insight: never keep your troubleshooting tools on the same machine that's giving you problems!
What Is Predictive Analytics? A Complete Guide for 2026
In simple terms, predictive analytics is a form of analytics that tries to predict future events, trends, or behaviors based on historical and present data. You can achieve this goal in different ways, each involving trade-offs between accuracy and cost.
Get Kafka-Nated S2E2: Viktor Kessler on Apache Iceberg, OSS, and Community
In this episode, we sit down with Viktor Kessler, co-founder of Vakamo, major contributor to Lakekeeper, and organiser of Apache Iceberg Meetup Europe, to explore the evolving world of Apache Iceberg. From architectural deep dives to open-source governance, Viktor shares insights from building an Iceberg REST catalog in Rust, launching a company around open data governance and growing the European Iceberg community from Dublin to Vilnius.
New high availability architecture for Aiven for PostgreSQL in limited availability
At Aiven, we use and contribute to open source to build our managed data platform. In the early days of Aiven for PostgreSQL, robust cloud-native tools for high availability and streaming backups did not exist at the level we needed. So we built our own. We created pglookout to monitor replication and handle failovers, and we built pghoard for backup and restore using object storage. These tools served us and thousands of our customers well for years. But the open-source PostgreSQL ecosystem has grown.
How Coralogix's Data Pipeline Turns Obscure Data into Clear Business Value
Observability data arrives as a flood of signals, full of potential, but rarely consistent. Error messages and debug logs can reveal what businesses care about: reliability, customer experience, and revenue. The challenge is turning raw technical events into information the whole organization can act on. Many observability systems store data first and structure it later, forcing teams to rebuild context in dashboards and queries, often duplicating logic across services.
White-Label Loyalty Platform Features Checklist
White-label loyalty platforms sound great on paper. You launch your own branded rewards program without building everything from scratch. No heavy dev work is required. Just plug it in and go. In reality, though, choosing the wrong platform can lock you into limited features, poor customization, and endless workarounds. If you are evaluating vendors right now, this checklist will help you focus on what actually matters. So, what features should a solid white label loyalty platform have?
Elephant in the Room, Episode 4: Protecting Customer Data with PostgreSQL Anonymizer & Django
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.