Operations | Monitoring | ITSM | DevOps | Cloud

Benchmarking Diskless Topics: Part 1

We benchmarked Diskless Kafka (KIP-1150) with 1 GiB/s in, 3 GiB/s out workload across three AZs. The cluster ran on just six m8g.4xlarge machines, sitting at <30% CPU, delivering ~1.6 seconds P99 end-to-end latency - all while cutting infra spend from ≈$3.32 M a year to under $288k a year. That’s a >94% cloud cost reduction. Extending Apache Kafka does come with an explicit tax.

Aiven for ClickHouse

Aiven for ClickHouse is back - better, faster, and stronger than ever. Explore how its new capabilities make it easier to build real-time analytics pipelines, handle massive query workloads, and extract insights at scale. Whether you’re streaming events, powering dashboards, or running analytics applications, Aiven for ClickHouse helps you move from data to decisions in record time.

Different ways to Search Text in PostgreSQL

When it comes to text search, PostgreSQL offers a surprisingly rich set of tools. Initially, text search capabilities were quite basic, often relying on the LIKE operator. This is inefficient for large amounts of text and lacks the nuance that comes with language. A major breakthrough came with the introduction of the built-in tsquery and tsvector data types, along with the associated functions, as part of PostgreSQL's core distribution. But tsquery is not the only option.

Kafka to ClickHouse in 6 Minutes

Learn how to stream data from Kafka into ClickHouse using Aiven’s integration wizard. In this demo, we show you how to generate sample logistics data in Kafka, configure the integration to map Avro-formatted fields, and connect to ClickHouse to view and query the ingested data. We also demonstrate how to create a materialised view in ClickHouse to store and query streamed data efficiently, making real-time analytics fast and easy.

Replacing cron jobs and dbt pipelines with ClickHouse Refreshable Materialized Views

For ClickHouse to achieve the speed it's known for, it has historically relied on a trigger-based and incremental architecture for materialized views. This architecture is known to be very efficient but lacks flexibility, such as when working with data that needs to be backfilled rather than append-only.

Elephant in the Room, Episode 1: Working with Long Form Text in PostgreSQL and Django

Welcome to “Elephant in the Room – Presented by Aiven. In this new live series, we dig into the real-world challenges developers face when building modern applications on PostgreSQL, the elephant everyone depends on but few truly understand. Episode 1: Join Jay Miller, Staff Product Advocate at Aiven, and special guest Jeff Triplett, Partner & Engineer at REVSYS and long-time Django community leader, as they explore how developers can harness PostgreSQL’s native text-search capabilities to build faster, smarter, and more efficient applications.

Get Kafka-Nated Episode 3

Join us for Episode 3 of Get Kafka-Nated, where host Hugh Evans sits down with Greg Harris, Staff Software Engineer and the driving force behind KIP-1150, to explore the future of cloud-native Apache Kafka. In this episode, we dive into diskless Kafka — a bold reimagining of Kafka’s architecture that moves data from broker disks to object storage. Greg explains the technical challenges, the operational benefits, and the broader implications for Kafka users.