Acquiring, Preparing, and Modeling Data

Install SAP Business Data Cloud data products. See Obtaining Access to Data Products.

Create a connection to a source system. See Connection Types Delivered by SAP:

• Import semantically rich data from SAP S/4HANA Cloud and SAP S/4HANA on-premise systems with rich metadata and choose whether to federate or replicate it. See Importing Entities with Semantics from SAP S/4HANA.

• Bring your existing SAP BW models and queries into SAP Datasphere. See Integrating Data from SAP BW.

• Replicate data from your source into local tables with support for delta loading via replication flows. See Creating a Replication Flow.

• Federate data via remote tables with the option to replicate data in the future . See Import Remote Tables.

Replicate large quantities of data cost-effectively to the object store. See Acquiring and Preparing Data in the Object Store.

Import data from CSV files to a local table with support for basic transformations, such as filtering, splitting, replacing, and changing case. See Creating a Local Table from a CSV File.

Use your preferred ETL tools to write data to an Open SQL schema and then access it as a local table in your space. See Integrating Data via Database Users/Open SQL Schemas.

Associate HDI containers to your space and access their data as local tables. See Exchanging Data with SAP HANA for SQL Data Warehousing HDI Containers.

Load data into spaces and assign users to the spaces to grant them access to the data. See Working in SAP Datasphere Spaces.

Provide row-level security via data access controls, so that each user can access only those records she is entitled to. See Securing Data with Data Access Controls.

Share data from one space to make it available in other named spaces. See Sharing Entities and Task Chains to Other Spaces.

Trace the movement of personal data. See Modeling with Personal Data.

Only the data you choose to expose can be accessed outside SAP Datasphere. See Exposing Data For Consumption.

Use drag and drop to create Join and Union operators in a graphical view. See Create a Join in a Graphical View and Create a Union in a Graphical View.

Write your own SQL manually or using AI assistance to create join and union clauses in an SQL view. See Creating an SQL View.

Combine data, transform it and output it to a table with support for delta loading via a transformation flow. See Creating a Transformation Flow or Creating a Transformation Flow in a File Space.

Define rules and iteratively match records (where join data is incomplete or unreliable), and then review and process the results in an intelligent lookup. See Creating an Intelligent Lookup.

Federate or replicate all the data you could need so that SAP Datasphere can serve as a single source of truth for any form of analytics or other modeling, but you shouldn't load data that will never be of use:

• Optimize performance of remote tables by removing unnecessary columns and setting filters. See Restrict Remote Table Data Loads.

• Filter data and remove unneeded columns in replication flows. See Define Filter Expressions in a Replication Flow Object Projection and Define Mappings in a Replication Flow.

• Ensure that you understand and are using appropriate column data types. See Column Data Types.

In each processing step, try to minimize the data you are working with and consider persisting data where necessary to optimize performance:

• Use the Filter operator in a graphical view. See Filter Data in a Graphical View.

• Use an appropriate WHERE clause in your SQL view. See Creating an SQL View.

• Define parameters to allow users to choose only the data they need. See Create an Input Parameter in a Graphical View.

• Filter data via an appropriate transform in a transformation flow. See Creating a Transformation Flow or Creating a Transformation Flow in a File Space.

When exposing data for consumption, optimize performance by exposing only the data necessary to answer your specific business question:

• Set a global filter in your analytic model and ensure you exclude unneeded measures and attributes. See Create a Global Filter in an Analytic Model.

Use the Aggregation node in your graphical view. See Aggregate Data in a Graphical View.

Include a GROUP BY clause in your SQL view. See Creating an SQL View.

Use an appropriate transform in a transformation flow. See Creating a Transformation Flow or Creating a Transformation Flow in a File Space.

Perform a wide range of aggregations and exception aggregations on measures in your analytic model. See Create a Measure in an Analytic Model.

Provide translations of master data such as product names and time and geographical data in text entities. See Create a Text Entity for Attribute Translation.

Enable translation in your space and use the Translation Dashboard to ensure that the names of your columns are available in appropriate languages. See Translating Metadata for SAP Analytics Cloud.

Convert currencies and units to ensure that totals makes sense. See Converting Currency and Unit Values.

Select a Semantic Usage of Fact to indicate that your entity contains numerical measures that can be analyzed. See Create a Fact to Contain Measurable Data.

In our example, Acme Sales View is a fact containing sales data.

Select a Semantic Usage of Dimension to indicate that your entity contains attributes that can be used to analyze and categorize measures defined in other entities. See Create a Dimension to Categorize Data.

In our example, four dimensions surround the fact, allowing us to analyze it by Salespeople, Time, Product, and Geo attributes.

Select a Semantic Usage of Text to indicate that your entity contains strings with language identifiers to translate text attributes in other entities. See Create a Text Entity for Attribute Translation.

In our example, translations are available for product lines, and days, months, and quarters.

Select a Semantic Usage of Hierarchy to indicate that your entity contains parent-child relationships for members in a dimension. See Create an External Hierarchy for Drill-Down.

In our example, the Acme Salespeople Hierarchy provides a hierarchy for the Salespeople dimension.

Select a Semantic Usage of Hierarchy with Directory to indicate that your entity contains one or more parent-child hierarchies and has an association to a directory dimension containing a list of the hierarchies. See Create a Hierarchy with Directory.

Deselect measures and attributes to leave only those that are relevant to answer your particular analytic question.

Create additional calculated and restricted measures as necessary. See Create a Measure in an Analytic Model.

Create structures to share calculations and restrictions between measures. See Create a Structure for an Analytic Model.

Use one analytic model as a source for other analytic models to allow them to inherit its measures and other calculations.

Trace the source of a column and the transformations it has passed through in your graphical view. See Visualize Column and Input Parameter Lineages in a Graphical View.

Visualize a set of entities and the associations between them by adding them to an entity-relationship model. See Creating an Entity-Relationship Model.

Visualize the objects that your object depends on (its lineage) and those that depend on it (its impacts) by opening its impact and lineage analysis. See Impact and Lineage Analysis.

Visualize the runtime status of all objects in the lineage of your analytic model, view, or local table and debug any issues in the Data Pipeline Analyzer. See Data Pipeline Analyzer.