Operations | Monitoring | ITSM | DevOps | Cloud

From BigQuery to ClickHouse: How we made our analytics 5× faster

‍For years, ilert has given our customers extensive analytics across their alerts, notifications, and on-call activity, a comprehensive overview of how their teams and services respond to incidents. These capabilities were backed by a separate analytical database running on Google BigQuery. It held the numbers behind every reporting dashboard in ilert, and for a long stretch it was perfectly fine. Then three problems grew too big to ignore.

OpenSearch 3.6: Agentic Applications Meet Long-Term Support

TL;DR OpenSearch 3.6 makes agentic search production-ready, with the AI-powered Launchpad provisioning full search apps in minutes and faster default vector search, and it's the first LTS release, bringing 18+ months of guaranteed support, SBOMs, and an upstream-first commitment (every fix goes back to the main project) so teams get fast-moving open source and a stable, supported platform at once.

What I got wrong about ClickHouse as a Kafka Person

Kafka is brilliant at moving events around, but sooner or later someone wants to actually query those events, perhaps aggregations, dashboards, or ad-hoc analytics over billions of rows. That is where ClickHouse comes in. It's the option for when stream processing is more than you need, but warehouse query latency is more than you'll tolerate.

The Aiven MCP in Practice: From Dev Environment to App Deploy

I spend a good amount of my time deploying Aiven services for demos and examples. Traditionally the tools I reach for are: If I’m writing a program, I may also look to the Aiven API, perhaps using curl at the command line or in a shell script, or perhaps with direct HTTP requests in a Python program. The API is how the console and the CLI tool talk to Aiven, but I generally find that too low level to be comfortable, and I always have to look up how to pass in the Aiven user token.

From Data Warehouses to AI: How Enterprise Data Quality Has Changed Over the Last 20 Years

An interview with Marcin Chudeusz, co-founder and CEO of digna Two decades ago, enterprise data quality looked very different. Organizations were building centralized data warehouses, business intelligence projects revolved around structured reporting, and most data quality initiatives relied on thousands of manually created validation rules. The objective was simple: ensure the data entering reports was accurate enough for decision-making.

If Kafka Is Down, Everything Is Down | Wolt + Aiven

Wolt operates in 30+ countries. Every order, every restaurant, every courier runs on the same infrastructure. When it breaks, it doesn't slow things down, it stops everything. Wolt's VP of Engineering shares why Apache Kafka sits at the heart of their platform, what seven years of partnership with Aiven looks like in practice, and how they're building the data foundation needed to get AI working at scale.

Kafka MCP: Manage Apache Kafka From Your AI Assistant

The Aiven MCP connects Claude, Cursor, and VS Code to Apache Kafka. Inspect topics, track consumer lag, stream a database in with CDC, and manage your cluster. 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.

Why Strategic IT Planning Is Essential for Business Success

Technology now plays a central role in nearly every aspect of business operations. From communication and customer service to data management and workflow automation, organizations rely on digital systems to maintain efficiency and support growth. However, simply adopting new technologies is not enough. Businesses need a clear strategy that aligns technology investments with their long-term objectives.

Kafka MCP: Manage Apache Kafka From Your AI Assistant

You're building with Claude or Cursor, and you need to know what's actually happening on your Kafka cluster. Your AI assistant knows Apache Kafka in the abstract, but not your topics, your retention, or that a consumer group has been slipping since this morning. So you leave the editor and go digging through logs, a CLI, and a few dashboards, correlating by hand to answer questions like: The Aiven MCP (EA) turns each of those into a sentence you type where you already work.