AWS Data Wrangler VS ga-extractor

I had no problem with awswrangler (https://github.com/aws/aws-sdk-pandas) and it supports reading and writing partitions which was really helpful and a few other optimizations that made it a great tool

awslabs has developed their own package for this and given it's for their product, seem likely to maintain it. https://github.com/awslabs/aws-data-wrangler

AWS data wrangler works well. it's a wrapper on pandas: https://github.com/awslabs/aws-data-wrangler

you'll find pre-made zips here: https://github.com/awslabs/aws-data-wrangler/releases

Don't forget about https://aws-data-wrangler.readthedocs.io/

Yep, agreed. Go is a great language for AWS Lambda type workflows.

Python isn't as great (Python Lambda Layers built on Macs don't always work). AWS Data Wrangler (https://github.com/awslabs/aws-data-wrangler) provides pre-built layers, which is a work around, but something that's as portable as Go would be the best solution.

ga-extractor – Tool for extracting Google Analytics data suitable for migrating to other platforms/databases

There's also no need to be scared of self-hosting the analytics engine yourself. Many of the open-source solutions can be spun up in matter of minutes and require very little resources to run. Even data migration can be quite simple as you've seen earlier in this article. If that's the route you want to go, but the extractor tool presented here doesn't support the target platform you'd like to migrate to, or you have some feedback to share, then feel free to create and an issue in GitHub repository and I will definitely try to help out.