Operations | Monitoring | ITSM | DevOps | Cloud

The following serves as a practical guide for those looking to simplify their architecture by migrating to a Rust monolith. Earlier this year, the platform team at InfluxData undertook a major rewrite of our core account and resource management APIs, moving from Go to Rust and from a microservices architecture to a single monolith. This change supported a new administrative UI for InfluxDB Cloud Dedicated and aligns with our broader effort to rewrite the InfluxDB database engine in Rust.

Modern data centers generate enormous volumes of telemetry from servers, switches, cooling systems, power infrastructure, and environmental sensors. Operations engineers must capture, store, and analyze this data in real-time to monitor uptime, maintain energy efficiency, and perform predictive maintenance using AI. Legacy monitoring systems struggle to meet today’s volume, cardinality, and latency demands.

In this blog series we’ve been examining the Five Myths of Kubernetes Resource Optimization. The fourth myth we’re considering relates to a common misunderstanding held by many Kubernetes practitioners: manual application tuning can increase resource utilization in my applications. Let’s dive into it.

Data plays a critical role in shaping operational decisions. From sensor streams in factories to API response times in cloud environments, organizations rely on time-stamped metrics to understand what’s happening and determine what to do next. But when that data is inaccurate or incomplete, systems make the wrong call. Teams waste time chasing false alerts, miss critical anomalies, and make high-stakes decisions based on flawed assumptions.

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%!

Aiven is proud to introduce our new pricing plans for Aiven for ClickHouse! Depending on your chosen region and plan, you can now double your compute power for the same price. On the other hand, if reducing costs is your priority, it's now possible to lower your total cost by up to 30% for the same compute power, depending on how you use the service. But how did we achieve this? This article explains how, and why Aiven’s nodes are the best for your ClickHouse workloads.