I recently spent some time configuring #airbyte for to allow some of my clients to do data science stuff on up-to-date transactional data. I wrote about my experience and provided some tips for others who might consider using Airbyte as their #ELT tool https://brainsteam.co.uk/2023/8/14/stable-airbyte-sync/

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I see going down a rabbit hole of JSON Schema is coming my way.

When tools like #AirByte use it and their example usage, docs, etc are all over the place, that is kinda difficult to handle for any non absolut trivial cases without doing one's own research. Now just have to shorten the feedback cycle.

Currently trying to describe a field for AirByte that the original API can return either as a boolean or an integer, and trying to guess how that will end up after the transformations in BigQuery.

Again and again having the urge to work on #OpenSource in various settings (and not just to have a good #GitHub contribution run, that's an outcome rather than a metric)

Today' I've been attempting adding a new, pretty simple connector to #AirByte, an ETL tool that we are using at $dayjob as well. Since I've run into some issues, I've gone back and opened an issue/request instead of sending the results just yet https://github.com/airbytehq/airbyte/issues/20789

Again and again having the urge to work on #OpenSource in various settings (and not just to have a good #GitHub contribution run, that's an outcome rather than a metric)

Today' I've been attempting adding a new, pretty simple connector to #AirByte, an ETL tool that we are using at $dayjob as well. Since I've run into some issues, I've gone back and opened an issue/request instead of sending the results just yet https://github.com/airbytehq/airbyte/issues/20789

@pbarker ah, excellent! Now this is something to wire up with #AirByte as well, just for kicks!

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