Dataenforce Alternatives

Similar projects and alternatives to dataenforce

julia

350 44,469 10.0 Julia dataenforce VS julia

The Julia Programming Language
Vapor

57 23,775 8.3 Swift dataenforce VS Vapor

💧 A server-side Swift HTTP web framework.
InfluxDB

www.influxdata.com sponsored

Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
PythonNet

37 4,333 7.2 C# dataenforce VS PythonNet

Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.
py2many

29 590 8.1 Python dataenforce VS py2many

Transpiler of Python to many other languages
swift

16 6,052 0.0 Jupyter Notebook dataenforce VS swift

Discontinued Swift for TensorFlow (by tensorflow)
Enzyme.jl

10 400 9.5 Julia dataenforce VS Enzyme.jl

Julia bindings for the Enzyme automatic differentiator
MLJ.jl

6 1,717 8.8 Julia dataenforce VS MLJ.jl

A Julia machine learning framework
WorkOS

workos.com sponsored

The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
YOLOv4

1 12 3.6 C# dataenforce VS YOLOv4

Port of YOLOv4 to C# + TensorFlow
smoke-framework

4 1,424 6.5 Swift dataenforce VS smoke-framework

A light-weight server-side service framework written in the Swift programming language.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better dataenforce alternative or higher similarity.

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dataenforce reviews and mentions

Posts with mentions or reviews of dataenforce. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-12.

Swift for TensorFlow Shuts Down
13 projects | news.ycombinator.com | 12 Feb 2021

The dependence on library authors is always a challenge in any language. You might have one author using `[a]` where another uses `PositiveNumeric a, Fin n => NonEmptyList n a` for the same thing. You can always just annotate whatever the library author used (e.g. they return a list of strings, so you use List[str]).
There are some interesting further add ons that seem very python, allowing you to go further. For example, with a pandas dataframe you can just say your type is a dataframe which isn't so useful, but it's possible to hack your own types onto it in the vein of https://github.com/CedricFR/dataenforce, or use things like https://smarie.github.io/python-vtypes/ to get smarter typing on things the authors didn't type. I expect that trend will continue.
What fascinates me about python's types is actually the very fact that they are bolted on. You have a language that lets you do crazy things and a type system trying to catch up and make it convenient to verify those crazy things. It's a nice complement to the usual developments of verifying all of the things and slowly extending the set of things you can do.
[D] Question: Do you enforce a data format in Pandas? When collecting data over a long period of time, wouldn't it be useful to use a system with versioned schemas that specify how various data entries must be formatted? Give me feedback on this Open Source idea:
1 project | /r/MachineLearning | 5 Feb 2021

https://github.com/CedricFR/dataenforce enforces column names and types, no versioning though. My first instinct is that important data should be stored in databases which enforce schemas, and that should be separate from the python code that reads it.