nifi-elastic-reporting VS ydata-profiling

Compare nifi-elastic-reporting vs ydata-profiling and see what are their differences.

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nifi-elastic-reporting ydata-profiling
1 43
1 12,101
- 1.3%
10.0 8.5
over 1 year ago 8 days ago
Java Python
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

nifi-elastic-reporting

Posts with mentions or reviews of nifi-elastic-reporting. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-26.

ydata-profiling

Posts with mentions or reviews of ydata-profiling. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-26.

What are some alternatives?

When comparing nifi-elastic-reporting and ydata-profiling you can also consider the following projects:

date-fns - ⏳ Modern JavaScript date utility library ⌛️

dtale - Visualizer for pandas data structures

direnv - unclutter your .profile

DataProfiler - What's in your data? Extract schema, statistics and entities from datasets

huh - Build terminal forms and prompts 🤷🏻‍♀️

dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

kubernetes-client - Java client for Kubernetes & OpenShift

lux - Automatically visualize your pandas dataframe via a single print! 📊 💡

FLiPStackWeekly - FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...

get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

zilla-examples - A collection of pre-canned Zilla feature demos. Deploy on K8s via Helm.

evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b