cloud-carbon-footprint VS datastation

ThoughtWorks made an app that does this: https://github.com/cloud-carbon-footprint/cloud-carbon-footp... but mainly works with aws, azure, and gcp

You might be interested in the methodology page cloudcarbonfootprint.org/docs/methodology/

See also https://github.com/cloud-carbon-footprint/cloud-carbon-footp...

A - I think finding one specific area of focus can be helpful -- e.g. find one specific source of the carbon footprint of the company (or advocate for using something like watershed if they have not measured it already!) and find and advocate for ways to reduce that. Here’s one example -- use the Cloud Carbon Footprint tool to understand the energy consumption and carbon emissions of cloud resources for a particular project that you might be involved in, and find ways to reduce that (e.g. through optimization, switching to different services that require less compute resources, research what would switching to a cloud provider that runs on renewable energy would mean, etc.).

There are a lot of open-source projects where one can contribute; I like the list maintained at https://opensustain.tech/. It’s a long list, so might make sense to focus on a specific sector one might be interested in and projects that can use your existing experience (e.g. in a particular language). A few projects that I think are amazing are OpenAQ which maintains an open API and open data on air quality and levels of pollutants; the Linux Foundation Energy projects like Grid eXchange Fabric that has a lot of applications in green energy (e.g. solar microgrids, load management, etc.); and the Cloud Carbon Footprint tool mentioned in another question, used to estimate the carbon emissions of cloud applications.

There was a technique that existed already where you could use `go test -cover` and the `-o` flag to produce a binary from `go test` rather than actually running tests. So you could build a binary that had coverage enabled. Then when you ran

Here's an example: https://github.com/multiprocessio/datastation/blob/main/runn....

I can't remember where I found this technique but it's been around for a while.

This new option is the same thing but a way to `go build` with `-cover` instead of `go test -cover -o $out`? Do I have that right?

If my employer doesn't consider paying for dbt cloud, I will use u/eatonphil 's datastation, run the queries on a dev database then put them in dbt.

I build a somewhat similar app, DataStation [0], that is in JavaScript and Go. It supports scripting in Python, Julia, R, JavaScript, Ruby, etc.

The server version of it exists and I run it myself but that process is not documented yet. (Most people use it as a desktop app today.)

[0] https://github.com/multiprocessio/datastation

My biggest issue with Pyodide is the long wait times. I haven't figured out a way around a ~5 second load time where the entire UI hangs every single time you load the page.

My app (similar to Simon's, a lite mode of a data IDE): https://app.datastation.multiprocess.io.

My code: https://github.com/multiprocessio/datastation/blob/main/shar....

I use Go heavily cross-platform developing DataStation [0] and dsq [1]. I am not an expert. And I don't have proof for it but on some rudimentary benchmarks the Linux-specific file idioms in the Go standard library definitely don't seem to translate well to even macOS let alone Windows. For example some good streaming techniques for reading large files on Linux that work really well there seemed to be pretty bad on macOS.

I think Amos has presented more proof than I can on the topic of just how Linux-influenced Go is. And I think it is fine for the majority of Go users because the majority users of Go are building server apps or Linux CLIs.

Amos has spent some time building cross-platform desktop systems with Go for itch.io and I think I'm seeing some of the same things they are in that scenario.

I think this is a reasonable article. If Amos gets flame-y at any point I think it's worth ignoring because there does seem to be something up with Go in cross-platform applications.

I like Go a lot and for most things I'd keep using it still. Just sharing some observations.

[0] https://github.com/multiprocessio/datastation

[1] https://github.com/multiprocessio/dsq

I keep a page of good first projects for two big projects I work on. The only expectation is that you know Go. I've had a couple of people who've never contributed to OSS come in and get some meaningful features merged.

I've got some good first projects if you're interested in OSS data tools and have some Go experience.

Check out: https://github.com/multiprocessio/datastation/blob/main/GOOD...

Some example projects: DataStation (desktop GUI for querying every kind of database, scripting and graphing the results) and dsq (a CLI companion for running SQL queries on many kinds of files), and go-json (a library for fast JSON encoding of arrays of large objects).

I'm building a desktop-first (SaaS-eventual) data IDE for developers [0]. Making a living? Not yet.

It being desktop-first makes it as easy to try out in a corporate environment as Sublime. The data never leaves your machine. Desktop-first is a big deal in devtools for this reason.

[0] https://github.com/multiprocessio/datastation