Integrate SAP IoT with SAP Analytics Cloud

Set up Postman parameters before you start working with SAP Analytics Cloud Integration with SAP IoT. We provide a set of APIs with sample model payload on GitHub for you to download. Using the Postman collection of APIs you will run thru the tutorial to complete our exercise.

1. Go to the GitHub repository sap-iot-samples at https://github.com/SAP-samples/sap-iot-samples.

2. Download and unzip the collection, and open the folder iot-sac-integration-samples for Postman collection and Postman environment files.

   

3. Open Postman and click Import button on top left. Upload the unzipped Postman collection and environment files from folder iot-sac-integration-samples.

   

   Pick the tenant name (or subdomain in developer lingo) from Cloud Foundry in SAP Cloud Platform.

4. Go to variable tenant_name and update the tenant name.

5. Go to variables client_id and client_secret and update your tenant client credentials.

   This will be found in the service key of the service instance in your tenant (in Cloud Foundry lingo called subdomain and in SAP Cloud Platform lingo called ‘Cloud Foundry sub-account’).

6. From the Postman collection run Step A: GET JWT Token followed by next step Pre 1:F etch Tenant Name Space. Copy package from the response payload received and update the Postman environment variable TenantPackageNamespace

   GET https://business-partner.cfapps.eu10.hana.ondemand.com/Tenants
   

7. From the Postman collection run Pre 2: Fetch Object Group ID step. Copy objectGroupID from the response payload received and update the Postman environment variable ObjectGroupId

   GET https://authorization.cfapps.eu10.hana.ondemand.com/ObjectGroups/?$filter=objectGroupParentID%20eq%20null&$format=json
   

8. Click Update.

   Upon updating the Postman environment parameters successfully, you are done with the Postman setup.

1. To check the thing model that you just configured go to the Thing Modeler app in the SAP IoT launchpad by replacing iot-iottrainingdev-709f with the tenant name in the following:

   https://iot-iottrainingdev-709f.leonardo-iot.cfapps.eu10.hana.ondemand.com/

   User with the right privileges can find the the SAP IoT Launchpad by going to the Cloud Foundry sub-account and launching it from the ‘Subscriptions’.

2. Go to the top left corner dropdown and switch to the package com.sap.silosaXX (XX is the random number as updated in Step 1). Check that the thing model looks as expected.

   

3. Use case definition: Based on the business needs, user can configure the dimensions and measures based on property set types available in the thing model.

   Let us configure a chart in SAP Analytics Cloud to map the Average Temperature of each silo for last 7 days of data where the data is aggregated Daily.

   Sample data model with daily aggregates for the last 7 days:

   
   {
     "Name": "TrainingModelXX",
     "AggregationWindow": "DAILY",
     "TimeWindowIdentifier": "day",
     "TimeWindowValue": 7,
     "Descriptions": [
       {
         "Description": "Daily Silo Temperature Analysis XX",
         "LanguageCode": "en"
       }
     ],
     "Fields": [
       {
         "FriendlyName": "EQUI",
         "AnalyticType": "Dimension",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_process_data",
         "DatasetType": "MasterData",
         "Name": "EQUI"
       },
       {
         "FriendlyName": "HANU",
         "AnalyticType": "Dimension",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_process_data",
         "DatasetType": "MasterData",
         "Name": "HANU"
       },
       {
         "FriendlyName": "HumidityMAX",
         "AnalyticType": "Measure",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_status",
         "DatasetType": "TimeSeriesData",
         "Name": "humidity",
         "AggregationType": "MAX"
       },
       {
         "FriendlyName": "HumidityMIN",
         "AnalyticType": "Measure",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_status",
         "DatasetType": "TimeSeriesData",
         "Name": "humidity",
         "AggregationType": "MIN"
       },
       {
         "FriendlyName": "HumidityCOUNT",
         "AnalyticType": "Measure",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_status",
         "DatasetType": "TimeSeriesData",
         "Name": "humidity",
         "AggregationType": "COUNT"
       },
       {
         "FriendlyName": "HumiditySUM",
         "AnalyticType": "Measure",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_status",
         "DatasetType": "TimeSeriesData",
         "Name": "humidity",
         "AggregationType": "SUM"
       },
       {
         "FriendlyName": "TemperatureMAX",
         "AnalyticType": "Measure",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_status",
         "DatasetType": "TimeSeriesData",
         "Name": "temperature",
         "AggregationType": "MAX"
       },
       {
         "FriendlyName": "TemperatureMIN",
         "AnalyticType": "Measure",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_status",
         "DatasetType": "TimeSeriesData",
         "Name": "temperature",
         "AggregationType": "MIN"
       },
       {
         "FriendlyName": "TemperatureCOUNT",
         "AnalyticType": "Measure",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_status",
         "DatasetType": "TimeSeriesData",
         "Name": "temperature",
         "AggregationType": "COUNT"
       },
       {
         "FriendlyName": "TemperatureSUM",
         "AnalyticType": "Measure",
         "CatalogName": "{{TenantPackageNamespace}}.{{PackageName}}:silo_type_1",
         "CatalogType": "ThingType",
         "DatasetName": "silo_status",
         "DatasetType": "TimeSeriesData",
         "Name": "temperature",
         "AggregationType": "SUM"
       }
     ]
   }
   

   This model has both dimension and time series measure configured out of the existing properties. “Temperature” with all possible aggregates - SUM, COUNT, MIN, MAX. Currently you are allowed to configure aggregates only to measures but not the dimensions.

4. Go to Step T request payload body and update the model name and model description in Postman collection – replace XX with random number as updated in Step 1.

5. Execute Step T: Create the data model with the payload provided in the API (should return 201 Created).

   POST https://sap-iot-noah-live-cm-metadata-management.cfapps.eu10.hana.ondemand.com/metadata/v1/model.svc/DataModels
   

   

6. Check that the model status is shown as NEW in the API response.

   • Aggregates: Currently following are supported - SUM, MIN, MAX, COUNT.
   • Time window: Currently time aggregate frequency supported are – Hourly, Daily, Weekly, and Monthly.
   • Dimension: It is a property based on which properties can be aggregated.
   • Data model: The combination of information the dimension, measure and aggregations are called a data model.
   • Retention period: It sets the boundary condition for how much aggregated data will be persisted at any point in time.
   • Lag period: which becomes useful in cases where the data arrives late. The changes of late data is high in cases of low internet connectivity for sensors.

   You are now done with data model definition.

Activation of a data model creates and persists aggregated data based on the input parameters defined in the data model.

1. Execute Step U: Read the data model to fetch the ETAG – ETAG is required to execute the model activation (should return 200 OK).

   

   GET https://sap-iot-noah-live-cm-metadata-management.cfapps.eu10.hana.ondemand.com/metadata/v1/model.svc/DataModels('<Model ID>')
   

2. Execute Step V: Activate the data model with the payload provided in the Postman (should return 202 Accepted).

   

   PATCH https://sap-iot-noah-live-cm-metadata-management.cfapps.eu10.hana.ondemand.com/metadata/v1/model.svc/DataModels('<Model ID>')
   
   {
   	"Status" : "Active"
   }
   

3. Execute Step W: Read the data model to check the model activation status. On triggering activation model status turns “ACTIVATION REQUESTED” and subsequently changes as “Activation In Progress”. In few minutes, the status will turn to Active status - you are good if the returned status code is 200 OK.

   

   GET https://sap-iot-noah-live-cm-metadata-management.cfapps.eu10.hana.ondemand.com/metadata/v1/model.svc/DataModels('<Model ID>')
   

   Info:

   • Depending on the model and number of aggregates configured it may take few minutes to activate the model as - the process behind activation is to generate all the aggregates configured in the data model and generate the aggregated data.
   • Calculation view is auto generated from activation and cannot be manually accessed or edited.
   • Different data model states.
     • ‘New’ on creation
     • ‘Activation Requested’ on triggering activation.
     • ‘Activation in Progress’ while activation is on going.
     • ‘Active’ upon successful activation.
     • ‘Deactivation in Progress’ while deactivation is on going.
     • ‘Deactive’ upon successful deactivation.

   You are done with data model activation. Upon activation, aggregates are created and calculation view is generated that will be exposed in SAP Analytics Cloud. Please proceed with further steps.