{"id":15959,"date":"2017-01-19T18:02:00","date_gmt":"2017-01-20T02:02:00","guid":{"rendered":"https:\/\/devwww.3cloudsolutions.com\/post\/demo-day-deriving-dimensions-in-power-query-2\/"},"modified":"2024-01-03T16:02:29","modified_gmt":"2024-01-04T00:02:29","slug":"demo-day-deriving-dimensions-in-power-query","status":"publish","type":"post","link":"https:\/\/3cloudsolutions.com\/resources\/demo-day-deriving-dimensions-in-power-query\/","title":{"rendered":"Demo Day: Deriving Dimensions in Power Query"},"content":{"rendered":"

In today’s self-serve Bl world, it’s common to need to analyze or mash up data from a huge variety of sources. Sometimes data arrives in a nice, clean and structured form, but it may also be delivered in a very basic flat file extract or single database tables. Power Query (also known as Get and Transform<\/a> in Excel 2016) is a great way to shape data into a clear and more convenient form for analysis.<\/p>\n

<\/p>\n

\"iStock-508901890edited.png\"
\n<\/strong><\/p>\n

You may have to take a flat file data extract, perhaps pulled from some source database or a public data set, and shape it into a small star composed of a fact table surrounded by related dimension tables. In a Demo Day video, I’ve taken a slice of Microsoft’s new World Wide Importers SQL Server 2016 sample data<\/a> and made a mock data extract, stored in a single CSV file, to emulate this scenario.<\/p>\n

Making Dimensions and Facts<\/h2>\n

After downloading the sample data<\/a> or finding a data sample of your own, follow along in Power BI<\/a> with the short video below to learn how to:<\/p>\n

    \n
  1. \n
      \n
    1. Import your original data and identify columns to make into dimensions.<\/li>\n
    2. For each of those dimensions, make a copy of the table that isolates the distinct values from the column(s), and numbers them with Add Index.<\/li>\n
    3. Join the original data to the dimensions using the text columns for matching.<\/li>\n
    4. Expand the matching ID values, and remove the corresponding text columns, to make a neat, compact fact table.<\/li>\n
    5. Analyze!<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      For more information or to learn more about analyzing data, contact 3Cloud<\/a> today!<\/p>\n","protected":false},"excerpt":{"rendered":"

      In today\\’s self-serve Bl world, it\\’s common to need to analyze data from a huge variety of sources. Check out the demo video to learn how to derive dimensions.<\/p>\n","protected":false},"author":21,"featured_media":14730,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[260],"tags":[273],"class_list":["post-15959","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai","tag-power-bi","topics-blog"],"acf":[],"_links":{"self":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/15959","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/comments?post=15959"}],"version-history":[{"count":0,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/15959\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media\/14730"}],"wp:attachment":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media?parent=15959"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/categories?post=15959"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/tags?post=15959"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}