verified-sources VS AWS Data Wrangler

- https://github.com/dlt-hub/verified-sources

Please look at the issues, README and CONTRIBUTE guides - those are the tasks you’ll be working on. We will ensure you are onboarded and will give you comprehensive reviews of your code. We expect that you can work with us a minimum 20 hours/week and ideally you will be flexible to do some more upon request.

dlt is an open-source library that automatically creates datasets out of messy, unstructured data sources. You can use the library to move data from about anywhere into most of well-known SQL and vector stores, data lakes, storage buckets, or local engines like DuckDB. It automates many cumbersome data engineering tasks and can be handled by anyone who knows Python. You can read more about us here: https://news.ycombinator.com/item?id=37999527

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Your Task and Responsibilities:

- get data from any storage bucket:https://github.com/dlt-hub/verified-sources/tree/master/sour...

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.