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Apache Flink Sliding windows allow you to aggregate events on partially-overlapping, fixed width time windows. For Flink Sliding windows you need to define: Find out how you can create sliding windows with Flink SQL.

Apache Flink Tumbling windows allow you to aggregate events on non-overlapping, fixed width time windows. Check out how you can create tumbling windows with Flink SQL.

Learn the windowing options available in Apache Flink.

A recording from the first Open Source Data Infrastructure meetup in Amsterdam, co-hosted by Aiven and PingCAP on April 19, 2023. The video features talks by Daniël Van Eeden, Technical Support Engineer at PingCAP, Sébastien Blanc, Developer Advocate at Aiven, and Kewei Shang, Staff Software Engineer (Data Platform) at Bolt.

Monitor a PostgreSQL database with a prebuilt Grafana dashboard, by storing the metrics in a dedicated time-series InfluxDB database.

Wondering how you can have a GitOps approach with your data infrastructure? In this video, we show you how you can combine Terraform and GitHub Actions.

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.

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.

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.

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.