Operations | Monitoring | ITSM | DevOps | Cloud

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.

Open Source & The New Era of AI Data Infrastructure - Aiven for Startups at Slush

There’s a foundational shift underway in the data infrastructure behind AI-native applications. In this fireside chat, we break down the technical requirements that make today’s data infrastructure dramatically different from what it used to be and what builders need to understand to keep up. In this talk, top founders defining the industry, Rebecka Storm, Co-Founder and CPO of Twirl (Acquired by Modal) and Heikki Linnakangas, Co-Founder of Neon (Acquired by Databricks) will dive deep into theme of AI data infrastructure and open source, moderated by Oded Valin (Aiven).

Kafka To ClickHouse in 6min

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.

Fork Your Database for Staging & Testing

Learn how to quickly create a fork of your Aiven for PostgreSQL instance to set up a safe staging or testing environment. In this demo, we walk you through selecting your project and service, navigating to the backups & forking section, naming your new instance, choosing the cloud provider and plan, and finalising the fork to replicate your original database. This approach allows you to test changes safely without affecting your production database, making development and QA workflows much more reliable.

Set Up a Read Replica for High Availability

Learn how to create a read replica for your Aiven for PostgreSQL instance using the Aiven Console. In this demo, we’ll show you step by step how to: 1) Ensure you’re on a Startup plan or higher 2) Select your service and name your replica 3) Choose the cloud provider, region, and plan 4) Create the read replica Once built, your replica can even be promoted to a primary instance, making it perfect for disaster recovery or scaling your workload.

Introducing Developer Tier for Aiven for PostgreSQL services

Aiven is introducing a new pricing plan for Aiven for PostgreSQL services. Starting at $8 USD per month, the Developer tier offers more storage, so you can scale up your free PostgreSQL service in a cost-effective way. Unlike the Free tier, services on the Developer tier are not automatically powered off if inactive. The Developer tier also automatically includes Basic support services. More information on the Developer tier is available in the Aiven docs.

Exploring why PostgreSQL 18 put asynchronous I/O in your database

For years, PostgreSQL relied on synchronous I/O, meaning that when the database needed data from disk, each read operation was a blocking system call. The database process would therefore pause and wait for the data retrieval before moving to the next task. Synchronous I/O works well for local storage, but our database needs have changed drastically since then, resulting in this architecture creating significant bottlenecks when storage has higher latency.