Name: Data Intelligence 4 of 21 - Glossaries & Relationships
Uploaded: 2023-9-6
Duration: 392 s

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<div><div>In this video series, we'll introduce some of the applications when using Data Intelligence. In this video, we'll go through using the Metadata Explorer to create business glossaries, which provide a central and shared repository for defining terms and describing how and where they are used in the business. We'll also look at the concept of relationships, which is linking terms to other terms, published datasets, rules, rulebooks, or columns.<br><br>0:00 - Introduction<br>0:50 - Relationships, rules, ratings and comments<br>2:10 - Glossary<br>3:30 - Adding terms<br><br><br>Please note:<br>                     This video was originally published on the SAP HANA Academy YouTube Channel                      and therefore may contain references to SAP HANA Academy and YouTube.                      The tech world is always evolving and the SAP HANA Academy YouTube channel                      is set to close on December 31, 2023.                      At that time, references will no longer be valid                      and will gradually be removed when the videos are updated.<br>Also, this video includes automatically generated captions                      and therefore might misrepresent the spoken content or display incorrect product names.</div></div>

Data Intelligence 4 of 21 - Glossaries & Relationships

Welcome to the SAP HANA Academy. My name is Bob, and in this series of videos by Digital Partner Engineering and the SAP HANA Academy, we'll be looking at Data Intelligence 3.1. In this video, we'll be going through the concepts of glossaries and relationships when using the Metadata Explorer. In the previous video we imported some data into the SAP semantic data lake using data intelligence. Here's an example of one of those files which is a simple parquet file which is like a CSV file. And this is the zip code column in that file. These are the values of that column and these are the distinct values. So this is a good example of profiling. Now as well as profiling we can look at the concept of relationships. And these relationships can help you more fully understand the dataset. These relationships consist of terms, tags and rules which are then applied to the dataset. Terms provide definitions and context for your data, and tags provide some classification. Rules, which we will look at in the next video provide a view into the data quality. We'll set this up in a few minutes. Now we've also got the concept of reviews. This is where you can rate, comment and create a discussion about a published dataset. You can rate and add comments to any published dataset. Those ratings and comments will provide opinions about the quality of the dataset. The ratings can mean whatever you want to convey to others about the dataset. For example, you can give a data set five stars to mean that it's ready for another department to consume the data. Now when you or others have questions or comments about the data set, they can then create a discussion. So as we saw in the previous video, there are some issues. So number one, the US addresses need to be standardized. So I'll put that as a comment. And also we have an issue with the zip codes in that we've got a lot of zip codes which are missing. So after adding the comment I only need to select OK and we can see our rating, the overall rating for this data set and of course the comment that I've just made. We've also got the concept of a Glossary, and this essentially provides a central and shared repository for defining terms and describing how and where it's used in the business. So, we can access the glossary by going back to our Metadata Explorer and there's a section which is called glossary. So, I'll select glossary and then we can look at our business glossaries. A business glossary can promote a common, consistent understanding of your business terms within your organization. And the definitions provide context for the terms, and the term relationships provide additional meaning. Term relationships are links to other terms, datasets, and columns. Now the aim is to use the business glossary enterprise-wide or division-wide and make the terms as clear and understandable as possible. So for example, what does sales mean to your organization? Could it mean is it currency exchange for goods or is it currency exchange for services? Or is it a discounted price on a product? Now all these definitions may be correct, but creating the correct definition for your organization will help reduce ambiguity. Here on the screen you can see we've got five different glossaries. And what we'll do is we'll modify the main glossary. So I'll go into the glossary. And what we'll do is we'll make a new term. So, if I click on the plus button, this creates a term. So, I'll call the term address. We're going to apply this to that column in the parquet file. And then what we'll need to do is define that term. So, I'll put in this phrase to describe an address. And then alongside the term we need to specify the individual keywords, which for me are street, city, state, and we'll also put zip code. And then as an optional extra we can add a synonym. Now as you know a synonym is just a word or phrase that means nearly the same thing as the original term. So when we've saved that the next thing to do is define the relationship. So this is a relationship between the business term and the actual technical term. So we do this within edit related objects. So when I select that, then what we can do is link this to various options such as rules, rule books, but we're going to link it to a data set. So for us, firstly, we need to choose our columns, which are in the data lake within Shared SHA. Then within the customers us2 parquet file what we'll need to do is choose our various columns. So if I expand the parquet file what we'll want to do is link it to our address columns. So what I'll do is I'll select address one, two and three, city, state and zip code. Now once we've done that, all we need to do is click on save related objects. And what we'll see now is that we've created a relationship between the address glossary term and the columns in that customer parquet file. So after saving, what we'll want to do is go and see what it looks like. So if I go back to my catalog and go and browse that catalog, what we'll do is we'll go and grab that file, the parquet file, which is in my data lake in Shared, SHA. If I go to my customers.us2 parquet file and I scroll down, here we can see we've linked that glossary term for the address. If I go to my columns and scroll down, here are those columns that we're actually linking to. Again, if I select that column, here we can see that same glossary term. Now that we've looked at using terms within glossaries and relationships between technical items and business items, in the next videos we're going to look at creating and using validation and quality rules against that data. You can find more tutorials on our YouTube channel, and if you'd like to be informed as soon as new video tutorials are published, then please subscribe. Thanks for watching.