SAP Datasphere: Beginner-Friendly with Hands-On Scenarios

SAP Datasphere - Spaces

Spaces as part of the SAP Datasphere solution are virtual team environments where your administrator has the ability to assign users and roles, as well as additional resources, connections to data sources, and allocated storage.
In SAP Datasphere all data related workflows start with the selection of a space, so you can see the space is a fundamental concept. Users can share tables and view to another space to allow users assigned to that space to use it as a source for their objects.

SAP Datasphere - Spaces

Spaces as part of the SAP Datasphere solution are virtual team environments where your administrator has the ability to assign users and roles, as well as additional resources, connections to data sources, and allocated storage.
In SAP Datasphere all data related workflows start with the selection of a space, so you can see the space is a fundamental concept. Users can share tables and view to another space to allow users assigned to that space to use it as a source for their objects.

Overview about the Business Scenario & the Data Model

The Business Scenario

The sample data set for the session represents retail transactions from a number of outlet stores located in the United States. The transaction details include the store, the sold product, and the sales manager. In addition, the information on revenue, cost, discount, and profit is available for each transaction.

The sales department is looking for a few analytics they need:

• Top 10 Revenue Generating Products
  They want to discover the top 10 revenue generating products.

• Sales Per Region
  Due to an increase in the number of sales, the customer wants to understand how the different regions are performing. Based on this visualization, the marketing team would identify the regions which are doing good as well as the regions which need attention or better marketing campaigns.

• Best Sales Representative
  It is time for the company to reward the best sales representative for all the hard work that has resulted in the sales report. For this purpose, the company needs to have a visualization that shows revenue per sales representative.

The exercises will walk you through the steps using SAP Datasphere and SAP Analytics Cloud to answer those open questions, using tables created in this session.

SAP Datasphere - Entity-Relationship Model

Let us clarify what an Entity-Relationship model is and why we are creating it, before we start the exercise.

An Entity-Relationship Model provides a variety of benefits:

• Depict the relationship of different entities

  
  

• Design physical or remote database models

  
  

• Re-use existing entities (table, view) from Data Builder

  
  

• Add new entities on-the-fly

  
  

• In-editor real time data preview

  
  

• Model Import / Export

  
  

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 you can reuse those when creating a new view.

Maintenance of associations and other capabilities of the E/R Model (like definition of semantic properties) can also be done in the table/view editor. Rules for creating associations depend on the Semantic Usage. The advantage of the E/R Model is that multiple entities can be modified at the same time while being visualized as diagram.

• 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.

  
  These four tables consist of one fact table which includes the sales transactions details, and three dimension tables for more details about the product, the store and the sales manager. You can fine the resource for this in previous lecture.

Creating the Dimension

In this exercise, we will create a new view of the semantic type dimension based on our previously created table. We will enhance this data by configuring a geographic enrichment so that we can visualize the store location on a geo map later in SAP Analytics Cloud. Please refer to attached resource for step by step process.

SAP Datasphere - Views & Analytic Models

A view in SAP Datasphere provides you with several benefits:

• No-code/low-code using the graphical editor as well as SQL and SQLscript editor,

• Foundation of data modeling on top of remote or local tables or views,

• Choose the semantic usage (e.g., Fact or Dimension) - enabling specific functionality and behavior depending on the semantic usage chosen,

• Define unions and joins, rename and remove columns, add calculations and filters.

A view in SAP Datasphere allows you to leverage local tables, remote tables, or views and combine those into a new view. Transformations defined in a Graphical or SQL View are executed during runtime when a view is accessed, for example, via the data preview option or as part of an Analytic Model. By default, views are not persisted, but there is the option to run and schedule snapshot replications.

Applying transformations to data is also possible in the entity Transformation Flow. In contrast to views, transformation flows load data from one or more sources and persist the result in a target table. This integration entity can detect delta changes when reading data from a local table that is enabled for delta.

An Analytic Model in SAP Datasphere provides the following benefits:

• Builds the analytical foundation to make data ready for consumption in SAP Analytics Cloud or exposure through the public OData API,

• Allows multi-dimensional and rich analytical modeling,

• Provides data for analytical purposes to answer different business questions by reusing predefined measures, hierarchies, filters, input parameters, and associations,

• Offers many features like calculated & restricted measures, exception aggregation, non-cumulative measures, pruning, variable support, analytical preview, multi-lingual metadata, etc.

The sources for analytic models are views or tables of the semantic type Fact, which can contain dimensions, texts, and hierarchies.

Live Data Connections to SAP Datasphere

You can create live data connections to SAP Datasphere systems, and access any SAP Datasphere Analytical Dataset, Analytic Model or Perspective within SAP Analytics Cloud.

For connection related details please refer to sap hep portal

In this session, We can how can we switch from Datasphere to SAC & from SAC to Datasphere without opening 2 browser sessions.

Top 10 Revenue Generating Products

In this exercise, we will create a story in SAP Analytics Cloud (SAC), which allows us to analyze and identify the top 10 revenue generating products.

Geographic Revenue Distribution

In this exercise, we will set up a story in SAP Analytics Cloud that allows us to visualize the measures on a geographic map.

SAP Analytic Cloud - Just Ask

Just Ask is the natural language query feature powered by artificial intelligence in SAP Analytics Cloud. It offers an easy and efficient way to ask questions about your data and provides fact-based answers in the form of tables and charts.

SAP Datasphere - Hierarchies and Data Access Controls

You can specify the following types of hierarchies:

1. Parent-Child - the hierarchy is recursive, may have any number of levels, and is defined by specifying a parent column and a child column within the dimension. For example, a departmental hierarchy could be modeled with the Parent Department ID and Department ID columns.

2. Level-Based - the hierarchy is non-recursive, has a fixed number of levels, and is defined by specifying two or more level columns within the dimension. For example, a time hierarchy could be modeled with the: Year, Quarter, Month, Week, and Day columns.

3. External Hierarchy - the parent-child hierarchy information is contained in a separate entity, which needs to be associated with the dimension (see Create an External Hierarchy for Drill-Down).

4. Hierarchy with Directory - your entity contains one or more parent-child hierarchies and has an association to a directory dimension containing a list of the hierarchies. These types of hierarchy entities can include nodes from multiple dimensions (for example, country, cost center group, and cost center) and are commonly imported from SAP S/4HANA Cloud and SAP BW (including SAP BW Bridge) systems.

Data access controls allow you to apply row-level security to your objects. There are different options for specifying criteria to determine which user is allowed to access specific data. One option is to define access based on a hierarchy. Each user can only view records that match the hierarchy values they are authorized for in the permissions entity, along with any of their descendants. Only external hierarchies with a single pair of parent-child columns are supported.

Explore the Analytic Model

SAP Datasphere - Analytic Model

Analytic models form the analytical foundation for preparing data for consumption in SAP Analytics Cloud. They enable the creation and definition of multi-dimensional models, providing data for analytical purposes to answer various business questions. Predefined measures, hierarchies, filters, parameters, and associations offer flexible and straightforward navigation through the underlying data.

In this exercise, you will learn how to use the data preview of the Analytic Model and create different types of new measures to enhance the existing model.

In This session I am going to explore more on the Data persistence in Views And what are all the options available. Also what is view analyzer and how to run that and How to schedule data persistence on regular basis.

Also i will explore option available under Data integration Monitor  and What all we can see in this section.

When to Use SQL Views in SAP Datasphere

1. Advanced Transformations and Logic

   • SQL views allow for complex calculations, aggregations, and transformations that might not be feasible or efficient in graphical views.

   • Examples: Custom ranking functions, window functions, advanced conditional logic, or recursive queries.

2. Performance Optimization

   • SQL views can be optimized for performance by writing tailored queries that fetch and process only the necessary data.

   • Useful when dealing with large datasets where graphical modeling might introduce unnecessary overhead.

3. Custom Joins and Relationships

   • When relationships between tables require specialized join conditions or non-standard logic that cannot be represented easily in graphical models.

   • Examples: Outer joins with specific filtering criteria, Cartesian joins, or using UNION to merge datasets.

4. Reusability Across Models

   • SQL views can encapsulate reusable logic, enabling them to be used across multiple models or projects without redundancy.

   • This is especially helpful in maintaining consistency and reducing effort for common data transformation scenarios.

5. Integration with External Systems

   • SQL views may be used when transforming data that comes from multiple external sources, ensuring compatibility with non-SAP systems.

   • Example: Processing data from external APIs or third-party databases before further modeling in Datasphere.

6. Debugging and Fine-Tuning

   • SQL views provide precise control, making them easier to debug and fine-tune when encountering complex data errors or mismatches.

   • This is particularly helpful during initial prototyping and troubleshooting.

When to Prefer Graphical Views Over SQL Views

While SQL views are powerful, graphical views are more intuitive and should be preferred for:

1. Simple joins and transformations.

2. Collaborative scenarios where non-SQL users work with the data model.

3. Scenarios emphasizing visual representation for ease of understanding.

Best Practices for SQL Views in Datasphere

1. Keep Queries Efficient: Use indexed columns, avoid unnecessary joins, and minimize the amount of data processed.

2. Comment Your Code: Include comments to explain complex logic for easier maintenance.

3. Reuse and Modularize: Create smaller, reusable SQL views for better manageability.

4. Test for Performance: Validate the SQL view performance, especially with large datasets.

By combining the strengths of both SQL and graphical views, you can leverage Datasphere's full potential to build robust and efficient data models.

SAP Datasphere - Transformation Flows

Transformation Flows load data from one or more sources and persist the result in a target table. This integration entity can detect delta changes when reading data from a local table which is enabled for delta. Transformation Flows are also useful in scenarios when utilizing Replication Flows for Premium Outbound Integration: Replication Flows can access local tables (delta enabled) which are updated by a transformation flow and transfer the data records in a delta mode.

SAP Analytics Cloud - Replace Model in Stories

You can replace a model in your SAP Analytics Cloud story with another compatible model, for example an SAP Datasphere Analytic Model with a different Analytic Model. You don't have to recreate your full story if you want to replace the data source (model) with a compatible one. While some features may need to be recreated, the structure and formatting of your dashboard won't be affected.

Intelligent Lookup in SAP Datasphere is a powerful feature that enhances the process of enriching datasets by automatically identifying and matching related data across different tables or views. This is particularly useful when you need to combine or augment data from multiple sources without manually mapping fields.

Key Features of Intelligent Lookup:

1. Automated Matching:

   • Identifies and matches relevant fields between datasets based on their data type, structure, and, sometimes, content patterns.

2. Data Enrichment:

   • Allows you to add additional information or attributes from a source dataset to a target dataset. For example, you can bring in customer details like region or industry to a sales dataset using customer ID as the key.

3. Machine Learning-Powered:

   • Utilizes machine learning algorithms to suggest the best matching fields, reducing manual effort and potential errors in data mapping.

4. No-Code Integration:

   • Designed for business users and data analysts, allowing them to perform lookups without needing coding expertise.

5. Transparency:

   • Provides detailed logs and insights into how the matching was done, ensuring trust in the results.

Steps to Use Intelligent Lookup:

1. Select Target Dataset:

   • Identify the dataset you want to enrich.

2. Choose Source Dataset:

   • Select the dataset containing the additional attributes you want to include.

3. Define Match Criteria:

   • SAP Datasphere will suggest match criteria (keys) based on its analysis, but you can manually adjust or define your own.

4. Review Suggestions:

   • Validate the suggestions provided by the system to ensure the mappings are correct.

5. Execute Lookup:

   • Once the mappings are confirmed, execute the lookup to enrich your dataset.

Example Use Case:

Imagine you have a Sales Data table with customer IDs but no customer information. You also have a Customer Master Data table that contains customer details like name, region, and industry. Using Intelligent Lookup, you can:

• Match the Customer ID in the Sales Data with the Customer Master Data.

• Enrich the Sales Data table with additional customer attributes, such as the customer's name and region.

Benefits of Intelligent Lookup:

• Efficiency: Speeds up the data integration process by automating field matching.

• Accuracy: Reduces errors compared to manual mapping.

• User-Friendly: Makes complex data operations accessible to non-technical users.

• Scalability: Handles large datasets efficiently.

In SAP Datasphere, Data Builder and Business Builder serve distinct purposes, catering to different aspects of data modeling and business logic. Here’s a breakdown:

1. Data Builder

Purpose: The Data Builder is used for managing and modeling raw data. It allows you to create technical data models that integrate data from various sources, enabling data preparation, transformation, and blending.

Key Features:

• Integration: Connect to multiple data sources like SAP systems, databases, and third-party systems.

• Data Modeling: Create fact models, dimension models, and views such as:

  • Graphical Views: For visual modeling of data.

  • SQL Views: For advanced data manipulation using SQL.

• Transformations: Perform complex data transformations like joins, unions, filters, calculations, and aggregations.

• Data Replication: Bring data from remote systems into Datasphere for centralized access.

Use Case: Ideal for IT and data engineers who handle technical aspects of data preparation and transformation.

2. Business Builder

Purpose: The Business Builder focuses on creating business-contextual models. It translates technical data into terms and objects that are meaningful to business users, enabling self-service analytics and reporting.

Key Features:

• Semantic Modeling: Define business entities like products, customers, and orders, aligning with business processes.

• Business Logic: Add attributes, measures, and hierarchies to entities, linking technical data to business terminology.

• Reusable Models: Build reusable objects that can be used by business analysts in tools like SAP Analytics Cloud (SAC).

• Collaboration: Allow collaboration between IT teams and business users by simplifying data consumption.

Use Case: Designed for business analysts and domain experts who focus on understanding and using data rather than preparing it.

Key Differences:

AspectData BuilderBusiness BuilderAudienceData engineers and IT teamsBusiness users and analystsFocusTechnical data preparation and modelingSemantic and business context modelingOutputViews (Graphical, SQL)Business entitiesUsageData integration, preparation, and ETLCreating business-relevant models

Workflow in Datasphere:

1. Data Builder: Prepare raw data, combine multiple sources, and create views.

2. Business Builder: Use those views to create semantic models for business reporting and analysis.

By separating these functionalities, SAP Datasphere ensures a clear distinction between technical data preparation and business consumption, fostering collaboration between IT and business teams.

Under Security in SAP Datasphere, you can manage user accounts and their access to the system. Here's what you can do in this section:

1. User Management

• Add New Users:

  • Create new user accounts by inviting them via email.

  • Assign roles during the user creation process.

• Edit Existing Users:

  • Modify user details such as email, name, or roles.

  • Adjust their permissions or deactivate accounts as needed.

• Deactivate Users:

  • Temporarily disable access for specific users without deleting their accounts.

2. Assign Roles and Permissions

• Role Assignment:

  • Assign predefined roles (e.g., Modeler, Administrator, Viewer).

  • Customize roles to grant specific levels of access.

• Granular Access Control:

  • Define which spaces or data objects the user can access.

  • Grant additional permissions, such as creating, editing, or viewing data models.

3. Monitor User Activities

• Audit Logs:

  • Track user activities to ensure accountability.

  • View login history and actions performed by each user.

4. Password Management

• Reset Passwords:

  • Administrators can initiate password resets for users.

This section provides centralized control over who can access the Datasphere system, what they can do, and which data they can view or modify.

In SAP Datasphere's Semantic Onboarding under Content Network, you have three options for business content installation:

1. SAP Business Content:

   • Description: Pre-built data models, templates, and scenarios developed by SAP to accelerate your data warehousing and analytics projects.

   • Purpose: Jump-start implementations with best-practice content tailored for various industries and lines of business.

   • Access: Available directly within the Content Network under the Business Content category.

   • Installation: Select the desired package and import it into your tenant. You can choose to deploy the content immediately after import or deploy it later using the deployment options in SAP Datasphere.

2. Partner Business Content:

   • Description: Content packages provided by SAP partners, offering specialized solutions and data models that integrate with SAP Datasphere.

   • Purpose: Extend the capabilities of your data environment with industry-specific or function-specific content developed by trusted partners.

   • Access: Found in the Content Network under the 3rd Party Business Content category.

   • Installation: Similar to SAP Business Content, select and import the desired partner package. Note that some partner content may require additional licensing or agreements.

3. Samples:

   • Description: Sample content packages that provide example data models, datasets, and scenarios to help you explore SAP Datasphere's features and functionalities.

   • Purpose: Learn and experiment with SAP Datasphere using sample data without affecting your production environment.

   • Access: Available in the Content Network under the Samples category.

   • Installation: Import the sample content to your tenant to explore and understand how to build and manage data models within SAP Datasphere.

To install any of these content types, navigate to the Content Network within the Semantic Onboarding app, select the desired category, choose the specific content package, and follow the import instructions.

In SAP Datasphere's Semantic Onboarding under Content Network, you have three options for business content installation:

1. SAP Business Content:

   • Description: Pre-built data models, templates, and scenarios developed by SAP to accelerate your data warehousing and analytics projects.

   • Purpose: Jump-start implementations with best-practice content tailored for various industries and lines of business.

   • Access: Available directly within the Content Network under the Business Content category.

   • Installation: Select the desired package and import it into your tenant. You can choose to deploy the content immediately after import or deploy it later using the deployment options in SAP Datasphere.

2. Partner Business Content:

   • Description: Content packages provided by SAP partners, offering specialized solutions and data models that integrate with SAP Datasphere.

   • Purpose: Extend the capabilities of your data environment with industry-specific or function-specific content developed by trusted partners.

   • Access: Found in the Content Network under the 3rd Party Business Content category.

   • Installation: Similar to SAP Business Content, select and import the desired partner package. Note that some partner content may require additional licensing or agreements.

3. Samples:

   • Description: Sample content packages that provide example data models, datasets, and scenarios to help you explore SAP Datasphere's features and functionalities.

   • Purpose: Learn and experiment with SAP Datasphere using sample data without affecting your production environment.

   • Access: Available in the Content Network under the Samples category.

   • Installation: Import the sample content to your tenant to explore and understand how to build and manage data models within SAP Datasphere.

To install any of these content types, navigate to the Content Network within the Semantic Onboarding app, select the desired category, choose the specific content package, and follow the import instructions.

The Data Marketplace in SAP Datasphere is a centralized platform that facilitates seamless data exchange within and between organizations. It enables users to access, share, and monetize data products, enhancing data-driven decision-making and collaboration.

Key Features of the Data Marketplace:

1. Data Exchange Facilitation:

   • Supports sharing of both public and private data, empowering self-service data exchange within and outside organizations. Teamwork

2. Data Products:

   • Datasets offered as data products can be downloaded into SAP Datasphere spaces via remote tables.

   • Available either free of charge or requiring a license purchase.

   • Some data products are one-time shipments, while others are regularly updated by data providers. Teamwork

3. Extensive Catalog:

   • Offers more than three thousand data products, filterable by categories such as SAP Application, Contract Type, Industry, Provider, and more. Teamwork

Options within the Data Marketplace:

1. Internal Data Marketplace:

   • Facilitates sharing and consumption of data products internally, democratizing data access between internal SAP Datasphere tenants. Teamwork

2. Public & Private Data Exchange:

   • Enables provision or consumption of data products between organizations, promoting data access across different SAP Datasphere tenants. Teamwork

Becoming a Data Provider:

• Users with a modeler role can create a data provider profile and publish data products to public, private, and internal Data Marketplaces.

• For more details on Data Marketplace please check out SAP Help Portal