Whilst Apache Kafka is an event streaming platform designed for, well, streams of events, it’s perfectly valid to use it as a store of data which perhaps changes only occasionally (or even never). I’m thinking here of reference data (lookup data) that’s used to enrich regular streams of events.

You might well get your reference data from a database where it resides and do so effectively using CDC - but sometimes it comes down to those pesky CSV files that we all know and love/hate. Simple, awful, but effective. I wrote previously about loading CSV data into Kafka from files that are updated frequently, but here I want to look at CSV files that are not changing. Kafka Connect simplifies getting data in to (and out of) Kafka but even Kafka Connect becomes a bit of an overhead when you just have a single file that you want to load into a topic and then never deal with again. I spent this afternoon wrangling with a couple of CSV-ish files, and building on my previous article about neat tricks you can do in bash with data, I have some more to share with you here :)

I’ve been playing around with the new SerDes (serialisers/deserialisers) that shipped with Confluent Platform 5.5 - Protobuf, and JSON Schema (these were added to the existing support for Avro). The serialisers (and associated Kafka Connect converters) take a payload and serialise it into bytes for sending to Kafka, and I was interested in what those bytes look like. For that I used my favourite Kafka swiss-army knife: kafkacat.

A script I’d batch-run on my Markdown files had inserted a UTF-8 non-breaking-space between Markdown heading indicator and the text, which meant that # My title actually got rendered as that, instead of an H3 title.

Looking at the file contents, I could see it wasn’t just a space between the # and the text, but a non-breaking space.