Meagan Longoria

Meagan Longoria is a consultant with Denny Cherry & Associates in Denver, CO where she shares her expertise in Microsoft BI and data warehousing through solution envisioning, architecture design, solution development, training, and blogging. She is a speaker at user groups and conferences across the country.

Website: http://DataSavvy.me

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Why We Don’t Truncate Dimensions and Facts During a Data Load

by Meagan Longoria

Data Savvy

Every once in a while, I come across a data warehouse where the data load uses a full truncate and reload pattern to populate a fact or dimension. While...

2019-07-25

810 reads

What You Need to Know About Data Classifications in Azure SQL Data Warehouse

by Meagan Longoria

Data Savvy

Data classifications in Azure SQL DW entered public preview in March 2019. They allow you to label columns in your data warehouse with their information type and sensitivity level....

2019-06-18 (first published: 2019-05-30)

424 reads

Turning a Corporate Color Palette into a Data Visualization Color Palette

by Meagan Longoria

Data Savvy

Last week, I had a conversation on twitter about dealing with corporate color palettes that don’t work well for data visualization. Usually, this happens because corporate palettes are designed...

2019-06-03 (first published: 2019-05-16)

1,414 reads

Storytelling Without Data?

by Meagan Longoria

Data Savvy

There are many great resources out there for data visualization. Some of my favorite data viz people are Storytelling With Data (b|t), Alberto Cairo (b|t), and Andy Kirk (b|t)....

2019-04-23 (first published: 2019-04-11)

504 reads

Power BI Now Has Keyboard Accessible Visual Interactions

by Meagan Longoria

Data Savvy

The March 2019 release of Power BI Desktop has brought us keyboard accessible visual interactions. One of Power BI’s natural...

2019-04-10 (first published: 2019-03-21)

1,878 reads

Power BI: Where Should My Data Live? Webcast

by Meagan Longoria

Data Savvy

When you start a Power BI project, you need to decide how and where you should store the data in your dataset. There are three “traditional” options: Imported Model:...

2019-03-28

38 reads