Is it just me or does this look like flowered up jupyter? Idk, I'm a bit sceptical-JetBrains have a few really solid products but all the ones that come with a web interface are... I can't think of a word that can truly describe them. You get the idea.
I greatly enjoy using pycharm, but the jupyter integration is and extremely mediocre way of using notebooks... which themselves have issues for serious development work.
I'm not really getting excited over this, sadly.
It's an enterprise-branded product, but nothing in this product stops people from writing the same old spaghetti. Then it's up to the engineers to figure out a way to get the spaghetti to behave.
I'd rather see some dev platform that forces an analyst or data scientist to think about integrating with other infrastructure in a sane way. Your code, conformed and embedded into some CI/CD pipeline. easy to set up data pipelines. unit/integration/data integrity test boilerplate generators geared towards data science.
(I know I'm probably describing something that's impossible... one can dream)
> Is it just me or does this look like flowered up Jupyter?
Must be JetBrains’ headline writers too, since the headline is “Announcing Datalore Enterprise – The Smart and Secure Jupyter Environment for Data Science Teams” making it sound rather like flowered up Jupyter.
I feel the supply of managed pimped out jupyter lab instances is plenty right now. What is really not clear is the path from there on to pipelining and workflow building with other components in a distributed/cloud architecture. There are also a lot of open source options like KubeFlow, prefect, ludwig etc. all of which have their problems and quirks, as well as the managed offerings like SageMaker.
I've used the cloud version of Datalore a little bit, and it's been excellent. Very nice jupyter environment, and it had very reasonable resource constraints on the free version. Version control works very well, which is a nice upgrade on some similar solutions I've tried to use.
Everything I've ever tried to use that plugged into Hub has been less excellent. I don't think I have the problems that's meant to solve. Our on-premise TeamCity server works just fine without it, and we never laid down the entire suite of other tools that would use Hub.
This is enterprise edition.
There is a community edition[1], and it is 3 yeas old[2]. I wasn't aware of that:
Other cloud offerings (besides Coalb) are more like a vanilla Jupyter. This one seems more customized. Not sure it's a good thing, but it's understandable as a JetBrains product.
I think Colab is indeed more comparable here. Overall Colab seems a bit better on available resources, but Datalore UI seems more powerful. Also, it seems to have R support. That can be a killer for would-be Colab users who aren't opted to Python.
On a tangent, I'm building an open-source data IDE focused on developers/engineering managers rather than data scientists. The goal is to be able to easily make SQL queries, HTTP requests, load files, script in Python, and visualize results all in a single place.
I think compared to Jupyter Notebooks this can more directly solve a problem for anyone who wants to do analysis on customer data, historic log data, JIRA tickets, incidents, etc. The long-term goal for a commercial version would be to have high level connections to all the APIs developers use; to make cross-datasource analysis easier.
Make the REPL ergonomic enough and it will crush any notebook interface. I think that's one reason why R users barely use Jupyter at all - RStudio's REPL is better than most of the out-of-the-box Python REPLs so R users don't have to resort to Jupyter (and there's rmarkdown for the rare cases when notebooks actually make sense).
Average dev salary at my org is north of 100k, but I do work in the US so lets say we have a developer earning half that. At $125 a month, it works out to be around 3% of monthly compensation (not including taxes and benefits). This only has to improve productivity by a tiny amount to be worth it.
It's the enterprise version. They can afford it. Besides it's not like everyone in the org will be having a seat. Only the people that are doing data science.
On-Prem (even in private cloud) solutions like this seem to always be pricier, includes dedicated support too. They have a cheaper $19/m and free tier on https://datalore.jetbrains.com/
To be fair, having it for individual use comes at a fair price[0]. The real money[1] is when the grad students go into their jobs and demand jetbrains IDEs since, as you said, they don't want to learn a new IDE.
[+] [-] axegon_|4 years ago|reply
[+] [-] dudus|4 years ago|reply
https://cloud.google.com/notebooks
https://notebooks.azure.com/
https://aws.amazon.com/emr/features/notebooks
https://colab.research.google.com/
https://www.kaggle.com/code
https://gradient.paperspace.com/
[+] [-] isoprophlex|4 years ago|reply
I'm not really getting excited over this, sadly.
It's an enterprise-branded product, but nothing in this product stops people from writing the same old spaghetti. Then it's up to the engineers to figure out a way to get the spaghetti to behave.
I'd rather see some dev platform that forces an analyst or data scientist to think about integrating with other infrastructure in a sane way. Your code, conformed and embedded into some CI/CD pipeline. easy to set up data pipelines. unit/integration/data integrity test boilerplate generators geared towards data science.
(I know I'm probably describing something that's impossible... one can dream)
[+] [-] proverbialbunny|4 years ago|reply
What makes Jetbrain's iteration far better than any of the other competitors? So far nothing?
[+] [-] Terretta|4 years ago|reply
Must be JetBrains’ headline writers too, since the headline is “Announcing Datalore Enterprise – The Smart and Secure Jupyter Environment for Data Science Teams” making it sound rather like flowered up Jupyter.
[+] [-] 0x008|4 years ago|reply
[+] [-] miohtama|4 years ago|reply
https://starboard.gg/
[+] [-] thrower123|4 years ago|reply
Everything I've ever tried to use that plugged into Hub has been less excellent. I don't think I have the problems that's meant to solve. Our on-premise TeamCity server works just fine without it, and we never laid down the entire suite of other tools that would use Hub.
[+] [-] flakiness|4 years ago|reply
Other cloud offerings (besides Coalb) are more like a vanilla Jupyter. This one seems more customized. Not sure it's a good thing, but it's understandable as a JetBrains product.
I think Colab is indeed more comparable here. Overall Colab seems a bit better on available resources, but Datalore UI seems more powerful. Also, it seems to have R support. That can be a killer for would-be Colab users who aren't opted to Python.
- [1] https://datalore.jetbrains.com/
- [2] https://blog.jetbrains.com/blog/2018/10/17/datalore-1-0-inte...
[+] [-] eatonphil|4 years ago|reply
I think compared to Jupyter Notebooks this can more directly solve a problem for anyone who wants to do analysis on customer data, historic log data, JIRA tickets, incidents, etc. The long-term goal for a commercial version would be to have high level connections to all the APIs developers use; to make cross-datasource analysis easier.
https://datastation.multiprocess.io/
[+] [-] jstx1|4 years ago|reply
[+] [-] bluehark|4 years ago|reply
[+] [-] xnyan|4 years ago|reply
[+] [-] codetrotter|4 years ago|reply
[+] [-] pininja|4 years ago|reply
[+] [-] SkyPuncher|4 years ago|reply
[+] [-] deregulateMed|4 years ago|reply
They infiltrated my college and got all of our students to use phpstorm for free. Upon graduation they smack you with a huge cost to continue.
I imagine quite a few grads didn't want to learn a new IDE.
[+] [-] judge2020|4 years ago|reply
0: https://www.jetbrains.com/phpstorm/buy/#personal?billing=yea...
1: https://www.jetbrains.com/phpstorm/buy/#commercial?billing=y...
[+] [-] spaetzleesser|4 years ago|reply
[+] [-] bitwize|4 years ago|reply