An often overlooked framework used by NASA among others is Kedro https://github.com/kedro-org/kedro. Kedro is probably the simplest set of abstractions for building pipelines but it doesn't attempt to kill Airflow. It even has an Airflow plugin that allows it to be used as a DSL for building Airflow pipelines or plug into whichever production orchestration system is needed.

Exactly - this release train metaphor that Spotify uses explains this visually:

https://medium.com/pm101/spotify-squad-framework-part-i-8f74...

One of the reasons smart contracts work so well is that they retain some of the properties of libraries yet are monetizable.

If you are feeling overwhelmed with yet another machine learning pipeline automation framework, you should check out Kedro (https://github.com/quantumblacklabs/kedro).

Kedro has the simplest, leanest, functional-programming inspired pipeline definition and also spits out AirFlow and other formats readily + comes in with an integrated visualisation framework which is stunning & effective.

Anyone else came here expecting a Haskell post?

Ditto.

You should try http://roamresearch.com/ and follow #RoamCult on Twitter - they are way ahead on note taking.

Looks nice but that is paid service. Unfortunately i can't afford.

If you use macOS and/or iOS check FSNotes out: https://fsnot.es/

It's really powerful if you go through all of it's capabilities, but can be used quite simply too.

consider Foam: https://github.com/foambubble/foam or TiddlyRoam: https://joekroese.github.io/tiddlyroam/

The author is forgetting that Google is a search monopoly.

AI was (and continues to be) the biggest threat to their success in search so they invested heavily in that (it was called ML back then).

Clearly they are still doing well even with subpar cloud software, so this has proven out.

This seems to lend more credit to ReasonML. Compile times are faster and it doesn't bring the associated complexity of object types while still retaining the benefits of static typing.