Operations | Monitoring | ITSM | DevOps | Cloud

Myth #5 of Kubernetes Resource Optimization: Spark Dynamic Allocation

In this blog series we’re examining the Five Myths of Kubernetes Resource Optimization. The fifth and final myth in this series relates to another common assumption of many Kubernetes users: Dynamic Allocation for Apache Spark applications automatically prevents Spark from overprovisioning resources while improving workload utilization levels.

Get Kafka-Nated - Episode 1: Apache Kafka's Evolution: 14 Years of Streaming

Grab your cup and prepare to get Kafka-Nated! In this series we’ll be digging into everything from Apache Kafka, from the pre history of Kafka to the latest Kafka Improvement Proposal (KIP). We’ll be joined by a range of guests who have been at the forefront of Kafka for many years. And to ensure you don’t kip (I know - we’re sorry) we’re offering every attendee a free coffee voucher. What We’ll Cover in Episode 1.

Microservices to Monolith, Rebuilding Our Backend in Rust

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.

Pepperdata Resource Optimization for Data Workloads on Kubernetes

Struggling with underutilized Kubernetes resources or rising cloud costs? Learn how Pepperdata Capacity Optimizer delivers real-time, automated resource optimization for Kubernetes and Amazon EMR workloads—helping teams reduce costs and boost performance without manual tuning. In this video, discover how Pepperdata helps DevOps, platform engineers, and FinOps teams.

Data Center Ops with InfluxDB 3: From Raw Metrics to Actionable Insights with Ease

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.

Myth #4 of Kubernetes Resource Optimization: Manual Tuning

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.

Benchmarking Hyperscalers: Aiven for ClickHouse Delivers More for Less

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.

Flavors of PostgreSQL: Choosing the Right Database for Your Next Build

In this deep dive, we’ll explore the modern PostgreSQL landscape—from core extensions to cloud-native forks—and help you confidently choose the right implementation based on your technical and business needs. AIVEN DATA PLATFORM The Aiven Platform is more than a collection of open source services for streaming, storing and analyzing data. The platform ensures that all services run reliably and securely in the clouds of your choice, are observable, and can easily be integrated with each other and with external 3rd party tools.

The Cost of Bad Data: Why Time Series Integrity Matters More Than You Think

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.