Operations | Monitoring | ITSM | DevOps | Cloud

Diskless topics are proposed in KIP-1150, which is currently under community review. The examples in this article use "Inkless", Aiven's implementation of KIP-1150 that lets you run it in production. I joined Aiven as a Developer Advocate in May, shortly after the Kafka Improvement Proposal KIP-1150: Diskless Topics was announced, which reduces the total cost of ownership of Kafka by up to 80%!

Here at Grafana Labs, our commitment to our “big tent” philosophy runs deep. We prioritize interoperability and flexibility within our observability solutions, and believe you should be able to connect to and visualize data from a wide range of sources, including both open source and commercial technologies. Our rich ecosystem of Grafana data sources directly reflects these values — and today, we’re excited to share a recent milestone related to that ecosystem.

Time series data, such as IoT sensor readings or stock market ticks, flow in fast, often at a rate of millions of points per second. Querying this data, especially years of historical records, can be slow and painful if using a nonspecialized database rather than a time series database like InfluxDB.

Data isn’t just a record of what happened—it shapes what happens next. Across industries, connected devices continuously stream time-stamped data that reflects the current state of machines, environments, and systems. This steady flow gives businesses a live view of their operations and the opportunity to catch issues early, adjust quickly, and operate more efficiently.

Top Tips is a weekly column where we highlight what’s trending in the tech world and list practical ways to explore these trends. This week, we're going over how you can eliminate data silos in your organization to enable smoother data flows. The free flow of data is one of the clearest signs of organizational health. When data is locked away—isolated in disparate systems that don’t communicate with each other—you’re dealing with a data silo.

In this blog series we are examining the Five Myths of Kubernetes Resource Optimization. So far we’ve looked at Myth 1: Observability and Monitoring and Myth 2: Cluster Autoscaling. Stay tuned for the entire series! The third myth addresses another common assumption of many Kubernetes practitioners: Choosing the right instances will eliminate waste in a cluster.

Diskless Kafka splits storage from compute, delegating replication to cheap object storage and turning Apache Kafka Brokers into a stateless compute layer. It’s 100% Kafka, and 80% cheaper. But in the cloud, a cheaper underlying technology does not always mean you pay less. The cost varies significantly depending on the deployment model - SaaS or BYOC. In this article, we will learn why.

Everyone agrees security matters – yet when it comes to big data analytics with Apache Spark, it’s not just another checkbox. Spark’s open source Java architecture introduces special security concerns that, if neglected, can quietly reveal sensitive information and interrupt vital functions.