Skip to main content

Extract Measures and its Description, Source Information, and Relationships from Power BI template.

Project description

✔ Power BI Automation Using Open AI API

Problem Statement:

  • Power BI documentation is essential for maintaining the accuracy and integrity of data models, ensuring compliance with regulations, and improving collaboration and efficiency across teams.
  • It is a time-consuming task that requires more human intervention and is highly error prone.
  • Our model aims to automate this process using OpenAI's NLP capabilities.

Purpose:

  • The purpose of our Power BI model documentation automation is to provide a Time-saving and Accurate solution for fetching pbix details. Below are the purpose that our model is fulfilling:
    • Automating the Power BI model documenter using one of the most efficient GPT-3 architecture i.e. text-davinci-002.
    • Fetched the Measures, Source Information, and Model Relationships attributes from the Power BI report.
    • Wrote back the Measures and Modification Descriptions and displayed on hovering the respective properties in updated pbit files.
    • Presented the output into three directories, namely EXCEL, JSON and Updated PBIT.

Input/Output Deliverables:

- Input: 
    - Single File
    - Multiple Files
    - A Folder

- Output:
    - Excel Directory
    - JSON Directory
    - Updated PBIT Directory

Features:

  • Implemented following features in our model:
    1. Features in JSON Deliverables:

      • DataModelSchema Generation: Generated the datamodelschema file for the respective pbit file and stored it in JSON format.
    2. Features in EXCEL Deliverables:

      • Measure Sheet:
        • Measure Name
        • Measure Expression
        • Measure Data Type
        • Measure Description
      • Source Information Sheet:
        • Table No
        • Table Name
        • Table Mode
        • Table Type
        • Table Source
        • Original Table Name
        • Table Query
        • Modification
        • Modification Description
      • Relationships Sheet:
        • From Table
        • From Column
        • To Table
        • To Column
        • State
        • Direction
        • Cardinality
    3. Features in UPDATED PBIT Deliverables:

      • Dynamic Hover Description: Made the Measures and Modifications Description to hover on each respective properties.

Prerequisites:

  • In order to use this script, one need to ensure to met the following requirements:
    • A Power BI Desktop installation (version - 2.115.663.0 64-bit).
    • A valid API secret key for OpenAI's NLP capabilities.
    • Python 3.7 or later installed on your machine.
    • Access to the Power BI models that you want to document.


✔ Package Description

PyPbitExtractor

- Created a python package for the given python script and published it on https://pypi.org/

Package Usage

- from PyPbitExtractor import Extractor
    - This command will install all the uninstalled required libraries used in script.
- Extractor.api()
    - This command will prompt user for Open API Secret Key.
- Extractor.main()
    - This will prompt user for input of file selection and thereafter the repective file/folder.

Package Installation

pip install PyPbitExtractor

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pypbitextractor-1.2.0.tar.gz (9.8 kB view details)

Uploaded Source

Built Distribution

pypbitextractor-1.2.0-py3-none-any.whl (9.2 kB view details)

Uploaded Python 3

File details

Details for the file pypbitextractor-1.2.0.tar.gz.

File metadata

  • Download URL: pypbitextractor-1.2.0.tar.gz
  • Upload date:
  • Size: 9.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.2

File hashes

Hashes for pypbitextractor-1.2.0.tar.gz
Algorithm Hash digest
SHA256 4e64ff1f87e751bfe8a878c3ff654b7695720daf49a279505c664d20ad49d900
MD5 271877d0172af8b5af76191efe89b1f6
BLAKE2b-256 b1d0c88cc4964a2730b7a87b251cfbef74f03cf968aa5751ec3ea98993df2b04

See more details on using hashes here.

File details

Details for the file pypbitextractor-1.2.0-py3-none-any.whl.

File metadata

File hashes

Hashes for pypbitextractor-1.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 f52da12c2d31f521e309c6d6371836026981688b8f2ccc8f2159caa86dda43e0
MD5 7ed3996b77adfc0a38160f8cf7ba4777
BLAKE2b-256 bb94c91c75eb327660e84bd85f3f9765de99384e963e08a9c06564bda0c6acf1

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page