73 |
74 | 25. Select the final output node for the Dimension View.
75 | 26. Navigate to the properties on the right hand side.
76 |
77 | 27. Navigate to the Attributes area.
78 | 28. Use the pencil icon (top right area) to open the details for the Attributes.
79 |
80 |
81 | 29. Ensure the Semantic Type for the line item Store Name is set to Text.
82 | 30. Set the Label Column for the line item Store ID to be the Store Name.
83 | 31. Click ***Close***.
84 |
85 |
86 | 32. In the toolbar in the General menu, use the option to Save your changes.
87 | 33. After saving your dimension View, ensure you deploy the view.
88 |
89 | 34. In the menu on the left hand side, click on the Home icon.
90 |
91 | ## Summary
92 |
93 | You've now created your dimension view ***Dimension Store*** including the added geo-coordinates column ***Store location***, which is required to use visualizations on maps within SAP Analytics Cloud.
94 |
95 | Continue to - [Exercise 08: Creating the View (part of Fast Track and a mandatory exercise)](../ex08/README.md)
96 |
97 |
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/exercises/ex04/README.md:
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1 | # Exercise 04 - Creating the Entity Relationship Model
2 |
3 | >:memo: **Note:** This is an Optional Exercise
4 |
5 | ## :beginner: Detour: SAP Datasphere - Entity Relationship Model
6 |
7 | Let us clarify what an Entity Relationship model is and why we are creating it, before we start the exercise.
8 |
9 | An Entity Relationship Model provides a variety of benefits:
10 | - Definition of entity-relationship models
11 | - Design physical or remote database models
12 | - Re-use existing entities (table, view) from Data Builder
13 | - Add new entities on-the-fly
14 | - In-editor real time data preview
15 | - Model Import / Export
16 |
17 | Basically, the Entity Relationship Model is not a view that you would consume in SAP Analytics Cloud, but instead it represents the relationship between the tables or views, and it helps you to define the relationship once, so that when you create a new view, that you do not have to define those relationships each time.
18 |
19 | ## End of Detour
20 |
21 | ## Create the new Entity Relationship Model
22 |
23 | 1. Log On to your SAP Datasphere tenant.
24 | 2. Select the menu option Data Builder on the left-hand side
25 | 3. Select the option New Entity - Relationship Model.
26 |
27 |
28 | 4. On the left-hand side you are presented with the local Tables and Views from your Repository and you also
29 | have the option to bring in Tables from any remote Sources that you have configured.
30 | 5. Ensure to select the option Repository, so that we see the local Tables.
31 | 6. Open the list of Tables.
32 | 7. Here you should see the tables that we created previously.
33 |
34 |
35 | 8. Now drag and drop the table Sales Transactions to the canvas.
36 |
37 |
38 | 9. When you select the Sales Transactions table on the canvas, you are presented with additional options, that
39 | allow you to add a new column, create a new View, create a new table, create a join, open the table in the
40 | editor, and to preview the data.
41 | 10. Now drag and drop the table Store to the canvas next to the table Sales Transactions.
42 | 11. Select the table ***Sales Transaction***.
43 | 12. Click on the ***"->"*** symbol to Create a new Association.
44 |
45 |
46 | 13. Click on the ***arrow symbol*** and drag the icon over to the table Store. You are creating a new Association
47 | between the table Sales Transactions and the table Store.
48 |
49 |
50 | 14. In the Properties panel on the right-hand side click on the ***Expand*** icon.
51 |
52 |
53 | 15. Ensure that the join between the table Sales Transactions and Store is based on column Store ID
54 | (STORE_ID). The system suggests this based on matching columns, but you can also add / remove these
55 | joins manually
56 |
57 |
58 | 16. Click on the ***Expand*** icon in the top right corner again to reduce the size of the panel.
59 | 17. Now drag the table Product onto the canvas.
60 | 18. Select the table ***Sales Transactions*** on the canvas.
61 | 19. Select the ***arrow symbol*** and drag and drop the arrow to table Products to create a new Association between the table Sales Transactions and table Product.
62 |
63 |
64 | 20. Ensure that the join between the Sales Transactions table and table Product is based on the Product ID (Product ID) column.
65 | 21. Now drag the table Sales Manager onto the canvas.
66 | 22. Create a new Association between table Sales Transactions and table Sales Manager.
67 | 23. Ensure that the join between the Sales Transactions table and the Sales Manager table is based on the Sales Manager ID (Sales Manager ID) column.
68 | 24. On the left hand side in the Repository panel, open the list of Views
69 |
70 |
71 | 25. Now drag the View Time Dimension – Day to the canvas.
72 | 26. Select the table ***Sales Transactions*** on the canvas.
73 | 27. Select the ***arrow symbol*** and drag and drop the arrow to the view Time Dimension - Day to create a new Association between the table Sales Transactions and the view Time Dimension - Day.
74 | 28. In the panel on the right-hand side now, create a join between the Transaction Date column and the Date column.
75 |
76 |
77 | 29. You can use the option to arrange all tables in the toolbar.
78 |
79 |
80 | 30. Your Entity Relationship Model should look like the image shown below
81 |
82 |
83 | 31. Save the changes to your Entity Relationship model by using the Save option in the General menu.
84 | 32. Enter the following details:
- Business Name - My First Entity Model
- Technical Name - My_First_Entity_Model
85 |
86 |
87 | 33. Click ***Save***.
88 | 34. Deploy the model.
89 |
90 |
91 | ## Summary
92 |
93 | You've now created the Entity Relationship Model and will make use of it in the next steps when we create the Views.
94 |
95 | Continue to - [Exercise 05: Importing Tables (part of Fast Track and a mandatory exercise)](../ex05/README.md)
96 |
--------------------------------------------------------------------------------
/exercises/ex08/README.md:
--------------------------------------------------------------------------------
1 | # Exercise 08 - Creating the View & Analytic Model
2 |
3 | > :memo: **Note:** This is part of the Fast Track and a mandatory exercise.
4 |
5 | ---
6 | ## :beginner: Detour: SAP Datasphere - Views & Analytic Models
7 |
8 | A View in SAP Datasphere provides you with several benefits:
9 | - Graphical or script-based editor
10 | - Define views on top of remote, replicated data sources, or tables
11 | - Define unions and joins, rename and remove columns, add calculations and filters
12 | - Create Fact, Dimensions or Relational Datasets
13 | - Create Parent-Child or Level-based hierarchies in Dimension views
14 | - Define measures & attributes in Fact
15 | - In the Graphical View Builder, you can compute and display the corresponding SQL Statement
16 |
17 |
18 | A View in SAP Datasphere allows you to leverage local tables, remote tables, or views and combine
19 | those into a new View. A View can also contain additional elements, such as filters and calculated columns and
20 | a view is consumable in SAP Analytics Cloud.
21 |
22 | An Analytic Model in SAP Datasphere provides you with several benefits:
23 | - Rich measure modelling
24 | - Create very complex calculation models and even refine them in SAP Analytics Cloud
25 | - Collection of user input via prompts in SAP Analytics Cloud
26 | - Data previewing
27 | - Time-dependency support
28 | - Dependency Management & Transport
29 |
30 |
31 | Analytic models are the analytical foundation for making data ready for consumption in SAP Analytics Cloud. They allow you to create and define multi-dimensional models to provide data for analytical purposes to answer different business questions. Pre-defined measures, hierarchies, filters, parameters, and associations provide flexible and simple navigation through the underlying data.
32 |
33 | ## End of Detour
34 |
35 | ## Start of the exercise
36 | In this exercise, we will create our first view based on our previously created tables into an asset in SAP Datasphere, which then can be consumed in SAP Analytics Cloud.
37 |
38 | 1. Log On to your SAP Datasphere tenant.
39 | 2. Select the menu option Data Builder on the left-hand side.
40 | 3. Click New Graphical View.
41 |
42 |
43 | 4. On the left-hand side you can decide between:
- Repository: Here you have access to the local tables (imported data), Views, Intelligent Lookups and Shared Objects.
- Sources: Here you have access to tables and views from your connections.
44 |
45 | 5. Ensure you select the option Repository.
46 | 6. Open the list of Tables.
47 | 7. You are presented with the list of tables, which we created previously.
48 |
49 |
50 | 8. Drag and Drop the table Sales Transactions to the canvas.
51 | 9. You automatically will – in addition to the table you dragged to the canvas – receive the output view as well,
52 | in our example called View 1.
53 |
54 |
55 | 10. Ensure the option Details (top right corner) is enabled. Otherwise, click on the output view that was added, in our example View 1.
56 | 11. Navigate to the Properties window.
57 |
58 |
59 | 12. Here you can configure Properties for the final output:
- You can configure the Business Name as well as the Technical Name.
- You can configure the Type of Dataset.
- You can decide if the View can be consumed or not.
- You can choose which of the available Columns are shown or will be hidden.
- You can define additional Associations.
- You can provide details on the Business Purpose, which then will be available as part of the
60 | Business Catalog.
61 | 13. Enter ***Sales View – Fact*** as Business Name.
62 | 14. The Technical Name will be generated based on the Business Name, but you can also change it and enter ***Sales_View__Fact***.
63 | 15. Set the semantic Usage to the ***Fact*** option.
64 |
65 |
66 | 16. Enable the option Expose for Consumption.
67 |
68 |
69 | 17. In the panel on the right hand side, scroll down to the Attributes section.
70 | 18. Now open the context menu for the Attribute ***Profit***
71 | 19. Select the option Change to Measure.
72 |
73 |
74 | 20. Repeat the steps for the Attributes ***Discount***, ***Revenue***, and ***Cost***.
75 | 21. Scroll down to the Associations.
76 | 22. Click the “+” sign to add a new Association.
77 |
78 |
79 | 23. Select the option Association.
80 | 24. You are being presented with the list of Tables and Views from your Space.
81 | 25. Select the entry for TABLE ***Product*** [Local Table (Dimension)].
82 | 26. Click ***OK***.
83 |
84 |
85 | 27. Ensure the Association is based on the column Product ID in both tables. In case the Association is not
86 | suggested or is defined on another column, you can delete the join and use a simple drag and drop motion
87 | to define a new one.
88 |
89 |
90 | 28. In the Details panel, click on the “Sales View - Fact” option to navigate back to the main Properties window.
91 |
92 |
93 | > :boom: ***Important*** :boom:
94 | > 29. You can now continue and repeat the steps 24 - 28 for the other associations:
- TABLE: Sales Manager [Local Table (Dimension)]
- VIEW: Dimension Store [View (Dimension)] – not the local table
- VIEW: Time Dimension – Day [View (Dimension)] – not the local table
95 |
96 | 30. Ensure the Association for the Sales Manager table is based on the column ***Sales Manager ID*** in both tables.
97 | 31. Ensure the Association for the Dimension Store View is based on the column ***Store ID*** in both tables.
98 | 32. Ensure the Association for the time dimension is based on the columns ***Transaction Date*** and ***Date***.
99 |
100 | 33. In the Details panel now, click on the “More” option to navigate back to the main Properties window and
101 | select the Fact option.
102 | 34. ***Deploy*** your View. (Deploying your view automatically saves your view)
103 | 35. You will be asked to confirm and ***Save*** the Business Name and Technical Name.
104 |
105 | 36. Once the view has been successfully deployed, you can now create your analytic model from the Model Properties screen.
106 |
107 |
108 | 37. You should now be within your Analytic Model as depicted below. You're able to see the sales view and its associated dimensions that you added previously.
109 |
110 |
111 | 38. ***Deploy*** your Analytic Model.
112 | 39. Enter ***Sales - Analytic Model*** as Business Name and ***Save***.
113 |
114 | ## Summary
115 |
116 | You have now created and deployed your first Fact based View and Analytic Model. The Analytic Model can now be consumed within SAP Analytics Cloud.
117 |
118 | Continue to - [Exercise 09: Business Layer - Dimension ](../ex09/README.md)
119 |
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/exercises/ex03/README.md:
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1 | # Exercise 03 - Data Layer - Prepare Your Data
2 |
3 | > :memo: **Note:** This is an Optional Exercise
4 |
5 | ## :beginner: Detour: SAP Datasphere - Model Types
6 | Before we are going to start with the first exercise in creating a table and creating the first model, let us have a look at the
7 | different asset types that you can create in the Data Builder of SAP Datasphere.
8 |
9 | So, let’s now look at the different asset types:
10 | - **Table**: Create a table to contain data by defining its column structure. You can configure each field of the table, and already define semantics and associations. You have the ability to upload data to this table later on.
11 | - **Graphical View**: In the Graphical View you can leverage Tables and Views to prepare data and the create new Views using a graphical no-code/low-code editor. Join datasets as appropriate, add other operators to remove or create columns and filter or aggregate data, and specify measures and other aspects of your output structure in the output node.
12 | - **SQL View**: In the SQL View you can leverage Tables and Views to the create new Views by using SQL or SQL-script (table function) view in a powerful SQL editor.
13 |
*Not used in the DA183 exercises.*
14 | - **Entity Relationship Model**: Here you define the relationships between Tables or Views, which then are being leveraged when you create a new View based on the Tables or Views.
15 | - **Analytic Model**: Analytic models are the analytical foundation for making data ready for consumption in SAP Analytics Cloud. They allow you to create and define multi-dimensional models to provide data for analytical purposes to answer different business questions.
16 | - **Data Flow**: You define Data Flows to use data transformations and leverage the option to load data from a source system and persist it in SAP Datasphere.
*Not used in the DA183 exercises.*
17 | - **Replication Flow**: Create a replication flow if you want to copy multiple data assets from the same source to the same target in a fast and easy way and do not require complex projections.
*Not used in the DA183 exercises.*
18 | - **Transformation Flow**: Create a transformation flow if you want to load data from one or more source tables, apply transformations (such as a join), and output the result in a target table. You can load full or delta data sets of data from one or more source tables to a target table.
*Not used in the DA183 exercises.*
19 | - **Intelligent Lookup**: Create an intelligent lookup to merge data from two entities even if there are problems joining them.
*Not used in the DA183 exercises.*
20 | - **Task Chain**: Create a task chain that groups multiple tasks, which can be run in series manually or through a schedule.
*Not used in the DA183 exercises.*
21 |
22 | ## End of Detour
23 |
24 | ## Start of the exercise
25 |
26 | In this section we will start creating the tables for our sample models and then upload the raw data to those tables in [Exercise 5](../ex05/README.md).
27 |
28 | We will start to create the tables in SAP Datasphere.
29 |
30 | 1. Log On to your SAP Datasphere tenant.
31 | 2. Select the menu option Data Builder on the left-hand side.
32 | 3. In case you are being asked, select your Space (e.g. GE12345)
33 | 4. Select the option New Table
34 |
35 |
36 | 5. You are being presented with the details to create a new table.
37 |
38 | 6. Enter the following details:
- Business Name - Sales Transactions
- Technical Name - Sales_Transactions
- Semantic Type - Relational Dataset
39 |
40 |
41 |
42 | 7. Scroll down to the area Columns (you can also use the tabs in the page header for navigation). Here you
43 | define now the structure of the table by adding the individual columns.
44 |
45 | 8. Use the “+” sign in the top right corner of the Columns area to start the process of creating your first table
46 | column
47 |
48 |
49 | 9. You now need to enter a Business Name, a Technical Name, and you need to configure the Data Type
50 | 10. For the first column, enter the following details:
- Business Name - Transaction ID
- Technical Name - Transaction_ID
- Data Type - Integer64
51 |
52 | ---
53 |
54 | > :bulb: **Tip:** Please note, that you can change the Data Type simply by clicking on the item in the Data Type column.
55 |
56 | ---
57 |
58 | 11. After you entered the details for the first column, please enter additional columns. All columns of the table are listed as follows:
59 |
60 | | Key: | Business Name: | Technical Name: | Data Type: |
61 | |:-----|:--------------------|:--------------------------------------|:--------------------|
62 | | X | Transaction ID | Transaction_ID | Integer64 |
63 | | | Transaction Date | Transaction_Date | Date |
64 | | | Store ID | Store_ID | String (6) |
65 | | | Product ID | Product_ID | String (4) |
66 | | | Sales Manager ID | Sales_Manager_ID | String (4) |
67 | | | Profit | Profit | Decimal(15,2) |
68 | | | Discount | Discount | Decimal(15,2) |
69 | | | Revenue | Revenue | Decimal(15,2) |
70 | | | Cost | Cost | Decimal(15,2) |
71 |
72 | 12. After you entered all columns for the table, ensure you enable the Key Column option for the column
73 | Transaction ID.
74 |
75 | 13. Now use the Save option in the General menu.
76 |
77 |
78 | 14. On the first time you save the table, you will be asked to confirm the name and technical name.
79 | 15. Click ***Save***.
80 | 16. After you saved the changes, you also have to deploy the table, so that we can later on upload data to the table.
81 | 17. Use the Deploy option from the General menu
82 |
83 |
84 | 18. We configured, saved, and deployed our first table.
85 | 19. Click on ***"back arrow"*** on the top menu bar to navigate back to the list of tables.
86 |
87 |
88 | 20. You are back at the home screen of the Data Builder and you should see your table in the list of objects.
89 | 21. Use the option New Table.
90 | 22. Enter the following details:
- Business Name - Store
- Technical Name - Store
- Semantic Type - Dimension
91 |
92 |
93 |
94 | 23. Navigate to the Attributes area.
95 |
96 | 24. Use the “+ sign to create new attributes.
97 |
98 |
99 | ---
100 |
101 | > :bulb: **Tip:** You will notice, based on the Type Dimension, we now have two additional options for each Column:
102 | Semantic Type and Label Column.
103 | The Label Column allows you to specify a column from the table to be used as Label. For example, you
104 | could have a Product ID and a Product Description in the table and use the Product Description column as
105 | Label for the Product ID.
106 | The Semantic Type option provides you with several option to choose from, so that you can configure an
107 | additional context for the column, such as the option to configure the column as a Currency column or a
108 | language column.
109 |
110 | ---
111 |
112 | 25. Enter the following Attributes for the table:
113 |
114 | | Key: | Business Name: | Technical Name: | Data Type: | Semantic Type | Label Column |
115 | |:-----|:--------------------|:--------------------------------------|:--------------------|:--------------------|:---------------|
116 | | X | Store ID | Store_ID | String (6) | None | Store_Name |
117 | | | Store Name | Store_Name | String (30) | Text |
118 | | | Store City | Store_City | String (20) |
119 | | | State ID | State_ID | String (2) |
120 | | | State Name | State_Name | String(30) |
121 | | | Country | Country | String(30) |
122 | | | Latitude | Latitude | Decimal(15,8) |
123 | | | Longitude | Longitude | Decimal(15,8) |
124 |
125 | ---
126 |
127 | > :bulb: **Tip:** Please note, that you can only select the Store Name for the Label Column after you entered the details for
128 | the Store Name into the Attributes.
129 |
130 | ---
131 |
132 | 26. After you entered all columns for the table, ensure you enable the Key Column option for the column Store ID.
133 | 27. Click ***Save*** in the General menu.
134 | 28. You will be asked to confirm the Business Name as well as the Technical Name.
135 | 29. Click ***Save***.
136 | 30. Click ***Deploy*** in the General Menu.
137 | 31. Click on ***"back arrow"*** on the top menu bar to navigate back to the list of tables.
138 |
139 |
140 | 32. Use the option New Table.
141 | 33. Enter the following details:
- Business Name - Product
- Technical Name - Product
- Semantic Type - Dimension
142 |
143 |
144 | 34. Navigate to the Attributes for the table.
145 | 35. Enter the following Attributes for the table:
146 |
147 | | Key: | Business Name: | Technical Name: | Data Type: | Semantic Type | Label Column |
148 | |:-----|:----------------------|:--------------------------------------|:--------------------|:--------------------|:----------------------|
149 | | X | Product ID | Product_ID | String (4) | None | Product Name |
150 | | | Product Name | Product_Name | String (30) | Text | |
151 | | | Product Category ID | Product_Category_ID | String (4) | None | Product Category Name |
152 | | | Product Category Name | Product_Category_Name | String (30) | Text | |
153 |
154 | 36. After you entered all columns for the table, ensure you enable the Key Column option for the column Product ID.
155 | 37. Click ***Save*** in the General menu.
156 | 38. You will be asked to confirm the Business Name as well as the Technical Name.
157 | 39. Click ***Save***.
158 | 40. Click ***Deploy*** in the General Menu.
159 | 41. Click on ***"back arrow"*** on the top menu bar to navigate back to the list of tables.
160 |
161 |
162 | 42. Use the option New Table.
163 | 43. Enter the following details:
- Business Name - Sales Manager
- Technical Name - Sales_Manager
- Semantic Type - Dimension
164 |
165 | 44. Navigate to the Attributes for the table.
166 | 45. Enter the following Attributes for the table:
167 |
168 | | Key: | Business Name: | Technical Name: | Data Type: | Semantic Type | Label Column |
169 | |:-----|:----------------------|:--------------------------------------|:--------------------|:--------------------|:----------------------|
170 | | X | Sales Manager ID | Sales_Manager_ID | String (4) | None | Sales Manager Name |
171 | | | Sales Manager Name | Sales_Manager_Name | String (30) | Text | |
172 |
173 | 46. After you entered all columns for the table, ensure you enable the Key Column option for the column Sales
174 | Manager ID.
175 | 47. Click ***Save*** in the General menu.
176 | 48. You will be asked to confirm the Business Name as well as the Technical Name.
177 | 49. Click ***Save***.
178 | 50. Click ***Deploy*** in the General Menu.
179 | 51. Use the Back option in top menu or as alternative you can click on your Space name to navigate back to the home screen of the Data Builder.
180 |
181 | ## Summary
182 |
183 | You've now created all tables that we need for our model and the overview in the Data Builder should look
184 | like this:
185 |
186 |
187 | Continue to [Exercise 04 - Creating the Entity Relationship Model ](../ex04/README.md)
188 |
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