smart_open VS Streamz

We’re using smart_open, which is an amazing library that lets you open objects in S3 (and other cloud object stores) as if they’re files on your filesystem. It’s obviously critical that we’re able to seek to an arbitrary position in an S3 file without first downloading the whole thing. We’ll assume you’re using Poetry, but you should be able to follow along with any other package manager:

If you didn’t follow along with the first article in this series, you should be able to follow this article with your own dataset as long as you install smart_open and Meadowrun. smart_open is an amazing library that lets you open objects in S3 (and other cloud object stores) as if they’re files on your filesystem, and Meadowrun makes it easy to run your Python code on the cloud.

I see that you're primarily looking into Python work, so I'd recommend `smart_open` as a nice, compact way to get started.

https://github.com/RaRe-Technologies/smart_open

You want smart_open. It gives you a (more complete) file-like interface to many different storage systems, including s3. You can read and seek as needed.

See also smart_open: https://github.com/RaRe-Technologies/smart_open which might be more user-friendly? Never used it myself but it was on HN before. Discussion on their bugtracker: https://github.com/RaRe-Technologies/smart_open/issues/579

Hi everyone, I am looking into building data pipelines in a way that is as pythonic as possible, I found out about this Streamz project and it integrates well with Dask. Does anyone have any experience using this? My main use would be for fairly simple accumulator pipelines. https://github.com/python-streamz/streamz

The best candidate so far is 'streamz', which is simple and lays out a processor in a pipeline. But it doesn't quite have the same level as built in monitoring as apache camel, for better or worse.

I use https://github.com/python-streamz/streamz + Dask for 100% python distributed mini batch real time processing, so we can import any python libraries and less hustle to deploy the server in production. We processed average 120 GB everyday, CDC from Debezium dan Kafka Connect Oracle Big Data Golden Gate.