datastation VS react-redux-api-tools

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

Software boutique with experts in Python/Django, Javascript/React, and UI/UX design.

Hi I’m Artur, from Labcodes. We’re a software boutique based in Brazil. We’re results-driven and internationally recognized for our 7+ years of partnership with US companies.

We’re experts in Python/Django, Javascript/React, and UI/UX design. Our team is comfortable working together and has a strong engineering culture, which is hard and expensive to build on an internal team.

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Talks we gave in major Python/Django conferences https://lab.codes/talks

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