Mensor v0.3.1 documentation¶
Welcome! If this is the first time that you have stumbled across this documentation, there is a very good chance you have some questions about this project. That’s fantastic! Hopefully, these resources will go some way toward answering them. If you find it lacking in any way, please do not hesitate to file an issue on the GitHub issue tracker.
What is Mensor?¶
Mensor is a graph-based computation engine for computing measures and metrics. It:
- defines a new grammar for extracting measures and metrics that is designed to be intuitive and capable (it can do almost(?) anything that makes sense to do with metrics and measures).
- makes measure and metric definitions explicit and shareable, and their computations transparent and reproducible.
- allows multiple data sources to be stitched together on the fly without users having to explicitly write the code / query required to join the data sources.
- is agnostic as to how data is stored or accessed, and new data backends are relatively simple to write.
- allows for local ad-hoc definitions of additional data sources for exploration by data scientists or other technically minded folk, decoupling it from deployment into production services.
Why does Mensor exist?¶
In short, the author (Matthew Wardrop) became frustrated with some (perceived?) operational inefficiencies endemic to the data science industry. In particular, he observed that substantial portions of data science work hours were spent reproducing statistics shown in dashboards, defining ad-hoc segmentations in SQL, and then endlessly debugging them. To make matters worse, despite these efforts taking a significant amount of time, there was little persistence of their efforts beyond their particular analyses, meaning that very similar analyses being done on opposite sides of the company (or done a few months later) all started from scratch. Mensor was created to solve these problems.
How do I use Mensor?¶
I like where you are going with this line of inquiry! If you are new to Mensor, check out the Concepts documentation, and then proceed with the Installation instructions. Once installed, you can kickstart your efforts using the Quickstart documentation. If you are looking to deploy Mensor as part of a Python package for a team or for production environments, consider exploring the Deployment material.