fusionauth-jwt VS ploomber

FusionAuth is one of the most decorated and successful open-source IAM solutions. It has been downloaded by more than 10 million customers and hosts over 1 million digital identities.

Ideally, I would recommend not handling and storing passwords yourself. It is preferable to use an identity provider (IdP), such as Azure AD B2C, Auth0, or FusionAuth. These systems are designed to manage your users' identity (including their passwords) so you don't have to. You could also use Single Sign-On with cloud providers.

Check out https://fusionauth.io/

Concur.

Another data point. Our open source JWT library[0] and Java client library[1] both target Java 8 because that is widely used.

0: https://github.com/FusionAuth/fusionauth-jwt

1: https://github.com/FusionAuth/fusionauth-java-client

FusionAuth | Senior Java Engineer, Senior TechOps Engineer, Senior Support Engineer | Full-time | Denver CO, USA | https://fusionauth.io

FusionAuth is the authentication and authorization platform built for devs, by devs. Our software solves the problem of integrating essential user security without adding risk or distracting from the primary application.

Ya know, the one with the features your users want to pay you for.

FusionAuth comes without the risk and complexity that typical identity systems have. With self-hosted or cloud installation, extensive documentation, free options, fast deployment and a no-hassle process, we eliminates hurdles that make it hard for developers to meet their authentication and authorization requirements.

We aren't VC funded, but we are profitable, growing fast, and need your help. We are currently hiring for the following roles:

Senior Java Engineer

Senior TechOps Engineer

Senior Support Engineer

Learn more, including salary info, what makes us tick, and role expectations, here: https://fusionauth.io/jobs

But I rarely see a comprehensive comparison that is current for any of them. And then there are others out there like Teleport and FusionAuth.

JWT processing for java. Fast: https://github.com/skjolber/java-jwt-benchmark

This article is based upon a presentation from Dan Moore of FusionAuth.

FusionAuth is a powerful identity and access management platform that makes it easy to add authentication and authorization to your applications. In this blog post, we'll explore how to integrate FusionAuth with a Python Flask application using OAuth 2.0, covering features such as login, displaying user profile information, and logout functionality.

- One-click sharing powered by Ploomber Cloud: https://ploomber.io

Documentation: https://jupysql.ploomber.io

Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).

We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!

For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel

And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber

Develop notebook-based pipelines

Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.

We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.

I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.

Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.

If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!

It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2

That's what we do for lineage with https://ploomber.io/