Name: Data Intelligence 1 of 21 ; Overview
Uploaded: 2023-9-6
Duration: 490 s

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<div><div><div><div>In this video series, we'll introduce some of the applications when using Data Intelligence, and this video will go through an introduction to Data Intelligence, a 3 minute walkthrough of highlights of the whole series, and then will show some available resources. The video is intended for folk who just want to the know the highlights before delving in !<br><br><br>0:00 - Introduction and production information from the corporate website sap.com<br>2:30 - About connection management, metadata explorer, and the data intelligence modeler<br>4:00 - Graphs and operators<br>6:00 - APIs<br>6:45 - About the documentation<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></div></div>

Data Intelligence 1 of 21 ; Overview

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 first video of the series, we'll be going through a quick overview of what data intelligence is. We'll then look at an overview of the whole video series, what we're going to cover, and lastly we'll look at resources. If you perform a search for data intelligence, you'll find there's lots of information available on the internet. Here we can go to SAP's own product page, and here you can see lots of examples and information on what it is. Now there's lots of marketing information on this page about what it is, but essentially it's a comprehensive data management solution that connects, discovers, enriches and orchestrates disjointed data assets into actionable business insights at enterprise scale. It enables the creation of data warehouses from heterogeneous enterprise data, you can perform management of any of your IoT data streams, and it also facilitates scalable machine learning. This means that it enables business applications to deliver the intelligent enterprise and ultimately provides a holistic unified way to manage, integrate and process all of your enterprise data. Now in this video series we're going to be focusing on three main applications within data intelligence. The one that you see here is the Modeler, which enables you to build pipelines. This enables data to be transformed and then processed. The transformations can be typical ETL transformations or even use complex machine learning algorithms or any type of custom transformation. Before you actually transform the data, firstly you must discover, access and prepare the data. So we'll also be looking at this application here, which is called the Metadata Explorer, which helps you understand, cleanse and prepare your data. Now SAP Data Intelligence works with any data source, whether it's structured, unstructured or streamed data. Of course, this could be data in various hybrid applications spanning SAP and non-SAP sources. So we'll start the series by looking at connection management. And again, that's the tool you can see on the screen. So when you use data intelligence you access a launchpad with your various applications. And in the first video we'll be looking at connection management and we'll be uploading some data sources. Then for the next five or six videos we're going to be looking at using the metadata explorer. And we'll look at the concepts of publishing and profiling data. And to demonstrate this, we're going to go for an overview of the data governance capabilities available. So we'll look at the concept of building rules, grouping rules together, and then binding those rules to your datasets, which will ultimately enable us to test the rules on your datasets and then produce rule dashboards which help you analyze your results. We'll finish the metadata explorer section by looking at the concepts of self-service data preparation. Here you can see an example of a business user combining data sets together and then ultimately we'll look at data lineage, which is where if we do some data transformation or if a business user does that data transformation, we can analyze how the data was created. And that's where we'll wrap up using the Metadata Explorer. So after using the Metadata Explorer, we're going to then move to looking at the Modeler. The first few videos will be focused on looking at the concepts of graphs and the operators. And here you can see an example graph which contains five or six operators. In this simple graph that I'm going to show you how to build, we simply read a list of files from our semantic data lake and combine them all together. So as well as building the graphs, of course we're going to run and test them. And so we'll also look at the concept of troubleshooting your graphs. We'll then look at some other types of operators. Here's one, for example, that we'll look at, which will join disparate data sets together. Again, back to troubleshooting, we're going to look at the concept of using wiretaps to aid testing and evaluating your graphs. Wiretaps will enable you to see the data in the graph as it passes through the graph. We'll then look at some different types of operators. So here's one that we're going to look at, which enables you to perform validational rules on the data that passes through your graph. It's quite an easy operator to use. So going from easy operators, we're going to look at some ones where you can code. So another one that we're going to look at is using the Python 3 operator, where we're going to be running some Python during the execution of one of our graphs. Finally, in this graph, after having used a lot of different operators to perform a lot of transformations, we're going to write the data to an SAP HANA database. And for us, that will be the SAP HANA Cloud. The last three videos in this series cover some extra topics. One of them is using the application called Machine Learning Scenario Manager. So within this Machine Learning Scenario Manager, what we're going to do is do some statistical analysis. And we'll be doing this within some Jupyter Notebooks. Lastly, we'll finish with two videos which will show how you can use APIs and Data Intelligence. So in our example we're going to be loading some data to our semantic data lake using data intelligence but using a REST API and we're going to be executing those commands from Mailman. And then in the last video we'll do the same thing but we'll be executing the commands using a Python script. So from my Apple Mac what we'll be able to do is with a little bit of Python we'll be able to execute it and send some data which will ultimately end up in our semantic data lake. So that's what we'll be covering in the video series. Now in terms of resources, again on that landing page, if you scroll right to the bottom, you've got the SAP Data Intelligence Resource Center with a list of links to blogs and webcasts and videos, which will help you in your understanding of data intelligence. You've also got a list of data intelligence frequently asked questions. And of course, as always, if you do a search on help.sap.com and if we do a search for data intelligence, you'll find that there's lots of documentation available here. And this is the documentation that we've actually used to build this video series. All you need to do is select the product SAP Data Intelligence Cloud and then here you'll see a list of available resources which you can use to further enable your understanding of SAP Data Intelligence. So that's the resources. So now you should go onwards to the next video which is again where we introduced the Connection Manager and we also look at uploading some files into our Symantec Data Lake. They will be a CSV and a parquet file. 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.