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Changes for page Discovery

Last modified by Danniar Firdausy on 2024/08/20 15:09
From version 10.1
edited by Carlijn Kokkeler
on 2023/09/06 13:55
Change comment: There is no comment for this version
To version 12.1
edited by Carlijn Kokkeler
on 2023/09/06 13:59
Change comment: There is no comment for this version

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13 13  * Before developing the integration landscape in eMagiz, technical requirements need to be established.
14 14  * In eMagiz, discovery concerns the Capture & Design phases.
15 15  
16 -== 3. Data Models ==
16 +== 3. Technical requirements ==
17 17  
18 -In this fundamental, we will zoom in on what data models are and how they are used within the various integration patterns we support in the platform. Then, we start our journey with a more theoretical look at the concept of data models. Following, we will zoom in on each of the three integration patterns. Finally, per integration pattern, we will explain the role of the data model within each of these three patterns.
18 +Before entering the Capture phase, several technical requirements need to be considered.
19 +* First of all, it needs to be discussed who will provide data and who will consume data.
20 +* Secondly, the exact definition of the data elements that will be exchanged has to be obtained.
21 +* Thirdly, the size of the data packets needs to be established, as well as the frequency of the packets.
22 +* Lastly, it has to be considered what constraints with respect to connectivity have to be in place.
19 19  
24 +A more elaborate description of the technical requirements can be found in [[this>>doc:Main.eMagiz Academy.Microlearnings.Crash Course.Crash Course Platform.crashcourse-platform-intro-technrequirements||target="blank"]] microlearning.
25 +
26 +
20 20  === 3.1 What is a data model ===
21 21  
22 22  A data model is a visual representation of how a system has structured its data. With the help of a data model, you can show the relationships between entities and define the attributes on the entity level. See below for a small example of a data model.