The UK Government publishes a lot of its data as open feeds. One that I keep coming back to is the Environment Agency’s flood-monitoring API that gives access to an estate of sensors that provide information about data such as river levels and rainfall.

The data is well-structured and provided across three primary API endpoints. In this blog article I’m going to show you how I use Flink SQL to explore and wrangle these into the kind of form from which I am then going to build a streaming pipeline using them.

There was a useful question on the Apache Flink Slack recently about joining data in Flink SQL:

Let’s imagine we’ve got a source of data with a nested array of multiple values. The data is from an IoT device. Each device has multiple sensors, each sensor provides a reading.

I was exploring some new data, joining across multiple tables, and doing a simple SELECT * as I’d not worked out yet which columns I actually wanted. The issue was, the same field name existing in more than one table. This meant that in the results from the query, it wasn’t clear which field came from which table:

Here’s a bunch of interesting links and articles about data that I’ve come across recently.

I’m a HUGE fan of Docs as Code in general, and specifically tools like Vale that lint your prose for adherence to style rule.

One thing that had been bugging me though was how to selectively disable Vale for particular sections of a document. Usually linting issues should be addressed at root: either fix the prose, or update the style rule. Either it’s a rule, or it’s not, right?

Sometimes though I’ve found a need to make a particular exception to a rule, or simply needed to skip linting for a particular file. I was struggling with how to do this in Asciidoc. Despite the documentation showing how to, I could never get it to work reliably. Now I’ve taken some time to dig into it, I think I’ve finally understood :)

At Current 24 a few of us will be going for an early run (or walk) on Tuesday morning. Everyone is very welcome!

I do my best to try and keep, if not abreast of, then at least aware of what’s going on in the world of data. That includes RDBMS, Event streaming, stream processing, open source data projects, data engineering, object storage, and more. If you’re interested in the same, then you might find this blog useful, because I’m sharing my sources :)

Let’s not bury the lede: it was DNS. However, unlike the meme ("It’s not DNS, it’s never DNS. It was DNS"), I didn’t even have an inkling that DNS might be the problem.

I’m writing a new blog about streaming Apache Kafka data to Apache Iceberg and wanted to provision a local Kafka cluster to pull data from remotely. I got this working nicely just last year using ngrok to expose the broker to the interwebz, so figured I’d use this again. Simple, right?

Nope.