How to prevent MOST Power BI errors - FOREVER using Microsoft Fabric

This text discusses the importance of data validation in data analysis, business intelligence, and data science workflows. It highlights how errors in data or dashboards can lead to loss of trust. The text presents the usage of Great Expectations, a Python library for data testing and validation. The document showcases a video walkthrough split in two parts: initial setup and configuration of Great Expectations within Microsoft Fabric, and a notebook to run validation on new datasets. There is an emphasis on implementation within a medallion architecture.

• Microsoft Fabric is the platform to manage the Great Expectations library.
• The tutorial lays out an end-to-end project including initial setup and data validation on new datasets.
• The goal is to prevent Power BI errors using Microsoft Fabric.
• Apart from data validation, data sources and assets are set up in the Great expectations context.
• The test scenarios aka Expectations are defined followed by defining and running a checkpoint.
• The output of the results is then discussed.
• Configuration is copied to Lakehouse Files.
• The second notebook begins with re-initializing the data context from Files and running the validation on fresh datasets.

Delving Deeper into Data Validation in Power BI using Microsoft Fabric & Great Expectations.

Data validation plays an integral role in data analysis, business intelligence, and data science. Errors in data can lead to misinformation and loss of credibility. Hence, libraries such as Great Expectations, that provide data testing and validation, are important. It can be used in combination with platforms like Microsoft Fabric to implement systematic data validation, thereby preventing most Power BI errors. Understanding the setup and configuration as well as running validation exercises on the platform can help users trust the data and ensure accuracy.

Learn about How to prevent MOST Power BI errors - FOREVER - using Microsoft Fabric

This video provides an introduction to using Microsoft Fabric and Great Expectations to prevent errors in Power BI. It covers setting up and configuring Great Expectations within Microsoft Fabric, as well as running validation on new datasets. It also looks at setting up a Data Context, adding data sources and assets, defining tests and expectations, running checkpoints, and handling the results. Finally, the video provides a link to the LinkedIn page of LearnMicrosoftFabric and introduces the presenter, Will.

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Keywords

Power BI errors prevention, Microsoft Fabric End-to-end project, Great Expectations, Data Validation, Data Analysis, Business Intelligence, Data Science, Data Testing, Python library, Medallion architecture, Data Engineering, Data Factory, LinkedIn page, LearnMicrosoftFabric