Operations | Monitoring | ITSM | DevOps | Cloud

As part of our big tent philosophy at Grafana Labs, we think you should be able to dig into your data and find meaningful insights — wherever that data happens to live. For many of our users, that data lives in Databricks, the open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale.

InfluxDB 3 is our latest time series database built for real-time analytics and high-volume data. Its Python Processing Engine lets developers run custom scripts known as plugins to process data, trigger alerts, or integrate with external systems via HTTP web requests. To demonstrate what’s possible, we’ve developed several plugins, all of which are available in the influxdb3_plugins GitHub repository. This public repo is open for anyone to use, modify, and contribute to.

Cribl recently introduced Lakehouse, a powerful new feature within Cribl Lake that enables fast queries on the freshest data. But it’s so much more than just speedy searches. Lakehouse redefines how organizations collect, store, manage, and analyze telemetry data at scale, ensuring a future-proofed, cost-efficient, and flexible approach to data management.

Managing your InfluxDB Cloud Dedicated environment just got easier. We’ve introduced an admin UI to streamline everyday tasks, so you can spend less time navigating settings and more time working with your data. The update is built for speed and usability. Whether you’re creating tables, managing tokens, or checking database status, the new UI helps you move faster with: This update is all about reducing friction for developers and teams managing time series infrastructure at scale.

Artificial Intelligence (AI) is revolutionizing businesses across industries. From personalized customer experiences to predictive analytics and process automation, there are hardly any sectors untouched by AI's impact. Its applications in data management aren't left behind. In fact, AI has the potential to transform traditional data management practices.

In this article, we will be continuing our series of deep dives into KIP-405. Previously, we covered: Now, we turn our attention to the internals of the read and delete paths. Just like we did for the write and metadata, here we will also be focusing on Aiven’s battle-tested Apache-licensed KIP-405 plugin. What makes the read path particularly interesting is how it delivers latency comparable to local disk or memory systems despite leveraging external object storage—let's dive in!