Introduction
Objective
SAP Datasphere is a unified service for data integration, cataloging, semantic modeling, data warehousing, and virtualizing workloads across all your data.
It enables a business data fabric architecture and makes it easy for organizations to deliver meaningful data to every data consumer with business context and logic intact. It provides the core components needed to do this. Such as self-service data access, data discovery, orchestration, processing and persistency, data governance, and data ingestion.
And a business data fabric allows for the rapid creation of intelligent data-driven applications and the quick spin up of new analytics and planning projects. SAP Datasphere at its core can help you deliver on your data initiatives with the key capabilities I mentioned earlier.
The SAP Datasphere Overview gives an insight into the toolset to ingest, process and visualize data. SAP Business Content or data from the Data Marketplace accelerates the build up a data fabric architecture.
This workshop focuses on Data Ingestion, Data Governance and Processing. Based on the Bikes Sales Sample Content, we define a data model as the foundation for an analytic model that allows multidimensional and semantically rich analytical modeling, making it easier, faster, and more efficient to address business questions.
For this modeling, we ingest data from several SAP and non-SAP system sources, using federation and replication flow procedures to set up the dataset. The dataset becomes the foundation of the modeling to build up dimensions including hierarchies and fact views.
In the Analytic Model, we design how analytics users see the data and define new calculations based on restricted or exception aggregations. The user input prompts help us to filter the dataset.
The results of our modeling is seen in the in-place preview of the Analytic Model tool.
Due to the tight integration of the Analytic Model and SAP Analytics Cloud, we analyse the information in SAP Analytics Cloud.
Finally, we explore the SAP Datasphere Catalog. How our business data is organized and classified. Which metadata, glossary, terms, KPI, and so on, are offered to simplify the search and understanding for the business users.
Following figure shows the Data Entity Perspective. The data objects are created in a SAP Datasphere space. Each user has their own space for modeling. Additionally, a predefined dataset with tables, dimensions, and fact objects are stored in the central space CENTRAL_DATA, and shared to each user’s space.
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At the lowest level in the figure, we see the source system types, like SAP HANA Cloud, and their objects that we want to ingest into the SAP Datasphere user space for modeling.
-
On the next level above - the Data Ingestion layer in SAP Datasphere - the corresponding tables are listed. For example, the Sales Orders and Sales Order Items tables, that are replicated from SAP HANA Cloud.
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In the modelling level of SAP Datasphere, the entities are created on the basis of the tables or other relational datasets. The Sales Fact entity is modeled, based on the Sales Orders and Sales Order Items tables. Depending how the entity should be used, there are various semantic usage types. For example:
-
Fact - Contains one or more measures and attributes. A fact typically has associations pointing to one or more dimensions.
-
Dimension - Contains attributes containing master data like a product list or business partner information, and supporting hierarchies.
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-
The modeling level also includes analytic models to provide data for analytical purposes. Here, the Sales Analytic Model relies on the Sales Fact entity with his associated dimensions. It represents the topmost data entity inside SAP Datasphere, shown in above figure.
In summary, the key takeaways of the scenario include:
- How to ingest data using several methods
- Build a data model combined of federated and replicated datasets
- Create an analytic model based on fact and dimension views
- Preview the analytic report inside SAP Datasphere
- Create an SAP Analytics Cloud BI report based on the analytic model
- Get an insight into the SAP Datasphere Catalog
Modular Approach
The following units can be completed in sequence or individually:
- Data Acquisition
- Data Modeling
- Analytic Modeling
- SAP Analytics Cloud Visualization
- Explore the Catalog
For this modular approach, the data entities - table, dimension, fact objects - created in the units, are also predefined in a central SAP Datasphere space and shared to the user space. If you did not complete a lesson, you can still continue with the next unit, or only focus on units you are interested in.
Prerequisites
-
Participants should ensure they have logon information and credentials.
-
Each user will have access to their own SAP Datasphere space with predefined system connections, shared objects, and generated time data.
-
It’s recommended to use the Google Chrome browser in incognito mode.
SAP Datasphere is a unified service for data integration, cataloging, semantic modeling, data warehousing, and virtualizing workloads across all your data.
It enables a business data fabric architecture and makes it easy for organizations to deliver meaningful data to every data consumer with business context and logic intact. It provides the core components needed to do this. Such as self-service data access, data discovery, orchestration, processing and persistency, data governance, and data ingestion.
And a business data fabric allows for the rapid creation of intelligent data-driven applications and the quick spin up of new analytics and planning projects. SAP Datasphere at its core can help you deliver on your data initiatives with the key capabilities I mentioned earlier.
The SAP Datasphere Overview gives an insight into the toolset to ingest, process and visualize data. SAP Business Content or data from the Data Marketplace accelerates the build up a data fabric architecture.
This workshop focuses on Data Ingestion, Data Governance and Processing. Based on the Bikes Sales Sample Content, we define a data model as the foundation for an analytic model that allows multidimensional and semantically rich analytical modeling, making it easier, faster, and more efficient to address business questions.
For this modeling, we ingest data from several SAP and non-SAP system sources, using federation and replication flow procedures to set up the dataset. The dataset becomes the foundation of the modeling to build up dimensions including hierarchies and fact views.
In the Analytic Model, we design how analytics users see the data and define new calculations based on restricted or exception aggregations. The user input prompts help us to filter the dataset.
The results of our modeling is seen in the in-place preview of the Analytic Model tool.
Due to the tight integration of the Analytic Model and SAP Analytics Cloud, we analyse the information in SAP Analytics Cloud.
Finally, we explore the SAP Datasphere Catalog. How our business data is organized and classified. Which metadata, glossary, terms, KPI, and so on, are offered to simplify the search and understanding for the business users.
Following figure shows the Data Entity Perspective. The data objects are created in a SAP Datasphere space. Each user has their own space for modeling. Additionally, a predefined dataset with tables, dimensions, and fact objects are stored in the central space CENTRAL_DATA, and shared to each user’s space.
-
At the lowest level in the figure, we see the source system types, like SAP HANA Cloud, and their objects that we want to ingest into the SAP Datasphere user space for modeling.
-
On the next level above - the Data Ingestion layer in SAP Datasphere - the corresponding tables are listed. For example, the Sales Orders and Sales Order Items tables, that are replicated from SAP HANA Cloud.
-
In the modelling level of SAP Datasphere, the entities are created on the basis of the tables or other relational datasets. The Sales Fact entity is modeled, based on the Sales Orders and Sales Order Items tables. Depending how the entity should be used, there are various semantic usage types. For example:
-
Fact - Contains one or more measures and attributes. A fact typically has associations pointing to one or more dimensions.
-
Dimension - Contains attributes containing master data like a product list or business partner information, and supporting hierarchies.
-
-
The modeling level also includes analytic models to provide data for analytical purposes. Here, the Sales Analytic Model relies on the Sales Fact entity with his associated dimensions. It represents the topmost data entity inside SAP Datasphere, shown in above figure.
In summary, the key takeaways of the scenario include:
- How to ingest data using several methods
- Build a data model combined of federated and replicated datasets
- Create an analytic model based on fact and dimension views
- Preview the analytic report inside SAP Datasphere
- Create an SAP Analytics Cloud BI report based on the analytic model
- Get an insight into the SAP Datasphere Catalog
Modular Approach
The following units can be completed in sequence or individually:
- Data Acquisition
- Data Modeling
- Analytic Modeling
- SAP Analytics Cloud Visualization
- Explore the Catalog
For this modular approach, the data entities - table, dimension, fact objects - created in the units, are also predefined in a central SAP Datasphere space and shared to the user space. If you did not complete a lesson, you can still continue with the next unit, or only focus on units you are interested in.
Prerequisites
-
Participants should ensure they have logon information and credentials.
-
Each user will have access to their own SAP Datasphere space with predefined system connections, shared objects, and generated time data.
-
It’s recommended to use the Google Chrome browser in incognito mode.