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hozumi
Product and Topic Expert
Product and Topic Expert
828

We are pleased to announce our latest GitHub sample, which demonstrates how to utilize SAP Data Quality Management microservices for location data for preparing data in SAP Datasphere. This sample provides insights into reading and writing data in SAP Datasphere using Open SQL schema. For detailed information on Open SQL schemas, please refer to the SAP Datasphere documentation: Integrating Data via Database Users/Open SQL Schemas.

Samples for SAP Data Quality Management, microservices for location data

Our sample can be found in the SAP-samples / cloud-dqm-sample-payloads repository.

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Within this repository, you will find the following folders:

/ datasphere-geo-map

This folder contains the SAP Datasphere data model objects for preparing data to be sent to the microservices for location data and consuming the result data from the microservices. The API calls to the microservices are invoked from the sample Python script. The final model object is ultimately used in a Geo Map widget in SAP Analytics Cloud Story.

/ datasphere-python

This folder contains a Python script to retrieve address data from the user space in SAP Datasphere, invoke the calls to the microservices for location data to obtain the corresponding location data, and subsequently write the results to a table within the Open SQL schema in SAP Datasphere.

/ postman-collections

This folder contains Postman scripts that showcase various API payloads for different countries. These scripts can be utilized for API testing prior to invoking them from Python scripts or UI applications.

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In summary, our sample repositories provide a Python script for invoking the API call to SAP Data Quality Management microservices for location data, as well as SAP Datasphere objects that enable you to consume location data and model it for visualization in the Geo Map widget of SAP Analytics Cloud stories. We hope that this resource will serve as a valuable starting point for leveraging the microservices in your own use cases and effectively preparing data in SAP Datasphere.