Things to Consider When Optimizing Cloud Infrastructure Costs
Cloud cost optimization is a topic that comes up frequently in client interactions. Read on to learn how to cut your infrastructure costs with Aiven.
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
Redis Strings
Redis is a powerful in-memory data store that servers for a variety of use cases such as: caching, real time analytics or even fraud detection. One of the most commonly used data type is the String, let’s discover in this video how this data type works.
What's new at Aiven: Q1/2023
Our product and engineering teams at Aiven have been busy! Find out what was released in the first quarter of 2023.
Redis Geospatial
Redis is a powerful in-memory data store that servers for a variety of use cases such as: caching, real time analytics or even fraud detection. Among the wide range of features, Redis has excellent support for geospatial indexes, let’s discover this in this video.
Aiven Terraform Provider v4: What's new
Aiven Terraform Provider 4.x is an important step on our move to the new Terraform Plugin Framework, providing better type checking. Read on for details.
Aiven Expands Data Platform on AWS Marketplace
Customers now have access to the complete Aiven portfolio via self-service from AWS Marketplace.
5 Redis data types in 5 minutes
Redis is a powerful in-memory data store that servers for a variety of use cases such as: caching, real time analytics or even fraud detection. It comes with a set of different data types and in this video we will learn about the 5 more common data types used in Redis. Sébastien Blanc's social media: CHAPTERS ABOUT AIVEN Aiven’s cloud data platform helps your business reach its highest potential by making your data work for you.
End to end managed development pipelines with Aiven and Qovery
Deploy your environments in a repeatable, secure way with Aiven and Qovery!
Streaming conversion of Apache Kafka topics from JSON to Avro with Apache Flink
Pushing data in JSON format to an Apache Kafka topic is very common. However, dealing with messages not having a predefined structure can create some problems, specifically when trying to sink the data via connectors, like the JDBC sink, which require the knowledge of the message structure. Transforming the messages from JSON to AVRO can enforce a schema on messages and allow the usage of a bigger variety of connectors.
Does Apache Kafka really preserve message ordering?
We’re told that Apache Kafka® preserves message ordering per topic/partition, but how true is that, and how close can we come to making it true?