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SAP Datasphere enables a business data fabric architecture that uniquely harmonizes mission-critical data throughout the organization, unleashing business experts to make the most impactful decisions. It combines previously discrete capabilities into a unified service for data integration, cataloging, semantic modeling and data-warehousing. SAP Datasphere is designed to combine federation and ingestion of SAP and non-SAP data. SAP Datasphere preserves the full meaning and context of SAP data across systems and clouds. It integrates with other data vendor’s platforms, delivering seamless and scalable access to one authoritative source for your most valuable enterprise data. SAP Datasphere leverages existing data investments. It does not require moving data into yet another data store. It radically simplifies your data landscape, ensuring inherent governance throughout the data life-cycle. With the announcement of the strategic partnerships, we clearly underline our openness to best integrate with external tools many of you might already be using. Each of these strategic partners brings the unique strengths of their ecosystems: Collibra for data cataloging and data governance Databricks integrates SAP data with their Data Lakehouse platform, especially for machine learning Confluent sets your data in motion with real-time event and streaming data DataRobot empowers organizations to leverage augmented intelligence with AutoML Google, using SAP Datasphere together with Google’s data cloud, customers can build an end-to-end data cloud that brings data from across the enterprise landscape + AI + Cloud Storage/BigQuery SAP Datasphere provides a multi-cloud, multisource business semantic service for enterprise analytics and planning. SAP Datasphere is the latest innovation in the data warehousing portfolio of SAP. It is based on the SAP HANA Cloud and follows a clear data warehouse as a service (DWaaS) approach in the public cloud, with fast release cycles.
The figure, SAP Datasphere Architecture, shows a high-level architecture of SAP Datasphere. Business modeling (self service): Use graphical low-code or no-code tools that support self-service modeling needs for business users, multi-dimensional modeling with powerful analytical capabilities, and a built-in data preview. Data modeling: Use graphical low-code or no-code tools, powerful built-in SQL, and data-flow editors for modeling, transformation, and replication needs. Enrich existing datasets with external data, coming from the Data Marketplace, CSV uploads, and third-party sources. Data Marketplace: Data Marketplace is fully integrated into SAP Datasphere. It is tailored for businesses to easily integrate third-party data. You can search and purchase analytical data from data providers. The data comes in the form of objects packaged as data products, which you can use in spaces of your SAP Datasphere tenant. Data products are either provided for free, or require the purchase of a license at a certain cost. Some data products are available as one-time shipments. Data providers regularly update other data products. Data space: To provide secure modeling environments for different departments and use cases, centrally create and provision spaces. Allocate disk and in-memory storage to spaces, set their priority, add users, and use monitoring and logging tools to manage spaces. Data catalog: A catalog is a comprehensive solution that collects and organizes data and metadata, enabling businesses and technical users to make confident data- driven decisions. Catalog improves productivity and efficiency by building trust in enterprise metadata through consistent data quality and governance. Data quality (governance): Publish high-quality trusted data and analytic assets, glossary terms, and key performance indicators to a catalog. This supports self- service discovery and promotes their reuse. Data orchestration: SAP Datasphere can connect to SAP, non-SAP cloud, and on- premise sources, including data lakes, to federate, replicate, and transform and load data. Re-use and migrate trusted meta and data models residing in SAP Business Warehouse and SAP SQL Data Warehouse implementations.
Content Network, as well as the Public Data Marketplace and other marketplace contexts. Consumption: Connect your standalone SAP Analytics Cloud solution or consume available models with your SQL tool or BI clients of choice.
Introducing SAP Datasphere Spaces
A space is a secure area that an administrator creates. In it, members can acquire, prepare, and model data. The administrator allocates disc storage and in-memory storage to the space, sets its priority, and can limit how much memory and how many threads its statements can consume. If the administrator assigns one or more space administrators as members of the space, they can then assign other members, create connections to source systems, secure data with data access controls, and manage other aspects of the space. Spaces are virtual work environments with their own databases. Spaces are decoupled, but are open for flexible access, thus enabling your users to collaborate without being concerned about sharing their data. To model your data and create stories, you must start off with a space. You can decide how much and what kind of storage you need, as well as how important your space is compared to other spaces. You also add space members and set up connections here. You might already have transformed data you want to access through SAP Datasphere. Or, you may have data in SAP Datasphere that you want to use in other tools or apps. In these cases, you can set up an open SQL schema or a space schema. Note If you do not define a default role, then the system assigns a user the minimum required permissions. The user can log in and request a role, but only if you configure one or more roles for self-service and assign users a manager.
Spaces are secured virtual work environments which: Provide isolation for metadata objects and space resources Define storage quota, control resource usage, and workload class settings per space Maintain space-specific source system connections and a common time dimension Manage user access for space members in combination with scoped roles Enable sharing of data and currency conversion settings with other spaces Watch this video to understand how to create spaces. Database User Within the space definition, you can create one or more database users for this space. A database user lets you ingest data from third-party SQL-compatible ETL or Business Intelligence tools and allows you to expose your space data to third-party SQL tools. In addition, you can ingest or expose models to HDI clients. These tools directly access the SAP HANA Cloud database of SAP Datasphere with this database user. This provides a secure method for data exchange for 3rd party tools. Note If you need access for several spaces, you need to define a dedicated database user for each space because such a user can only access the specific database schema of its corresponding space. With SAP Datasphere Spaces, you can seamlessly share information across departments. The sharing mechanism simplifies this process, allowing you to model master data once for use by multiple departments.
Integration of Data into SAP Datasphere
SAP Datasphere provides a large set of default Built-in-connectors to access data from a wide range of sources, in the cloud or on-premise, from SAP or from non-SAP sources or partner tools.
Depending on the connection type, you can use remote tables for the following tasks: o Directly access data in the source (remote access) o Copy the full set of data (snapshot or scheduled replication) o Copy data changes in real time (real-time replication) Data Flows, Replication Flows, and Transformation Flows The flow feature supports building data flows, replication flows, and transformation flows. After you as a modeler have created a connection, in the respective flow editors of the Data Builder, you can add a source object from the connection to a data flow to integrate and transform your data. External Tools SAP Datasphere is open to SAP and non-SAP tools to integrate data to SAP Datasphere. By default, when you import a remote table, its data does not replicate and you must access it using federation each time from the remote system. You can improve performance by replicating the data to SAP Datasphere and you can schedule regular updates (or, for many connection types, enable real-time replication) to keep data fresh and up-to-date.
Integration Architecture
SAP Datasphere leverages different technologies to setup connections to sources. Most SAP Datasphere connection types support creating views and accessing or replicating data, but different connection types have different prerequisites and provide different functionality and user experience. You need connections for remote tables and flows.
1. Remote Tables Remote Tables take data 1:1. If you need data transformation, you must use additional objects, such as views, data flows, or transformation flows. Prerequisites - Data Provisioning Agent: Before using the connection, it requires an appropriate setup. For this purpose, use SAP HANA Smart Data Integration (SDI) and its data provisioning framework (DP Server + DP Agent). The DP Agent is a lightweight component running outside the SAP Datasphere environment. It hosts data provisioning adapters for connectivity to remote sources, enabling data federation and replication scenarios. The DP Agent acts as a gateway to SAP Datasphere providing secure connectivity between the database of your SAP Datasphere tenant and the adapter-based remote sources. The DP Agent is managed by the Data Provisioning Server. It is required for all SDI connections in general. Through the Data Provisioning Agent, the pre-installed data provisioning adapters communicate with the Data Provisioning Server for connectivity, metadata browsing, and data access. The Data Provisioning Agent connects to SAP Datasphere using JDBC. It needs to be installed on a local host in your network and needs to be configured for use with SAP Datasphere. There are some source types which use alternative methods. For example SAP SuccessFactors or SAP HANA Cloud are capable of using direct connection without additional setup. Others support SAP HANA Smart Data Access (SDA) with the Cloud Connector (e.g. SAP HANA on premise). 2. Flows You can integrate SAP Data Intelligence (DI) connectors which are embedded in SAP Datasphere to extract data from remote sources using Data Flow or Replication Flow functionality. Prerequisites - Cloud Connector: To establish a connection to these sources a Cloud Connector is required to act as link between SAP Datasphere and the source. Before creating the connection, the Cloud Connector requires an appropriate setup. The Cloud Connector serves as a link between SAP Datasphere and your on- premise sources and is required for connections that you want to use for following use cases: Data flows Replication Flows
SAP Datasphere offers multiple modeling capabilities that address different user groups – from business analysts with deep business understanding to tech-savvy developers and power users. In a typical end-to-end scenario, the SAP Datasphere Data Layer contains the basic modeling of the underlying data sources and tables. The related set of tools is available in the SAP Datasphere Data Builder. Here, developers and modelers use tables, views, and intelligent lookups to combine, cleanse, and prepare data. You can expose views directly to SAP Analytics Cloud and other analytics clients. On top, you can create an Analytic Model. It is intended to be a top-level model that combines, renames, refines, and enriches the underlying artifacts with further calculations and semantic information before exposing lightweight, tightly-focused objects. SAP Analytics Cloud and other analytic clients can consume these objects. Some customers have already defined more complex SAP Datasphere Business Layermodels instead of Analytic Models to map existing data builder models to new models with business-related terms. The SAP Datasphere Business Builder provides the related set of tools. The Business Builder artifacts can still be created or maintained, but are no longer recommended. Instead, SAP recommends to replace them with Analytic Models or SAC models.
Data Builder Artifact Types
You should follow a modular modeling approach. The idea is that master data for each business entity is modeled only once and re-used (associated) in several transaction data contexts. This increases data quality and ensures reusability. The property Semantic Usagereflects the object type of the business data model. Depending on the semantic usage chosen, you can then define appropriate semantic typesfor specific columns, such as currency code, date, year, text, language, amount with currency, or quantity with unit. This has an effect in reports, for example, the total amount is not displayed if different currencies are involved. The following options for semantic usage are common: Use Fact to generally indicate that your entity contains transactional data. It must have at least one measure defined. Use Dimension to indicate that your entity contains master data attributes that are used to analyze and categorize measures defined in other entities. A Dimension must have a key defined. Use Hierarchy to indicate that your entity contains parent-child relationships for members in a dimension. Use Text to indicate that your entity contains strings with language identifiers to translate text attributes. It must have a key defined that contains exactly one column of semantic type language and a non-key column of semantic type text . You can associate texts or dimensions for facts, or texts or hierarchies for dimensions. An association acts like a join that is only executed when columns of the dimension or text are demanded in the analysis.
table is imported, it is available for use by all users of the space and can be used as a source for views. By default, remote tables federate data, this means, data is not replicated and must be accessed from the remote system each time the data is used. Consider the following options for a better performance: You can improve performance by enabling replication to store the data in SAP Datasphere. Some connections support real-time replication and for others, you can keep your data fresh by scheduling regular updates (see Replicate Remote Table Data). To optimize replication performance and reduce your data footprint, you can remove unneccessary columns and set filters (see Restrict Remote Table Data Loads). To maximize access performance, you can store the replicated data in- memory (see Accelerate Table Data Access with In-Memory Storage). You can automate sequences of data replication and loading tasks with task chains (see Creating a Task Chain). To identify source changes for several remote tables sharing the same connection, you can either proceed from the Repository Explorer or from the Data Builder landing page. Identify available table structure updates for all tables sharing the same source connection, and avoid errors and impact on dependent objects and runtimes in SAP Datasphere resulting from these updates, such as view runs, Remote Table replications or deployment.
SAP Datasphere provides an editor for defining new tables manually by defining its properties and column structure: General properties: Technical and business name of the table, semantic usage. Column list: Primary key and columns with descriptions, field names, data types, default values, and semantics. Associations as reference from specific columns to other entities of semantic usage dimension or text Business Purpose (for reuse and search capabilities)
The Intelligent Lookup in SAP Datasphere is a tool supporting merging data from two entities even if there are problems joining them. When combining data, there may be no column in your primary entity that contains data to uniquely identify a record in the other entity, and which would thus allow the creation of a standard join. Or, if such a column (a foreign key) exists, its data may be incomplete or unreliable. Imagine the following scenario: You want to combine external data with internal data via product ID. The data may be inconsistent, such as the external data contains the letter "O" where the internal data contains the digit 0.This can be particularly common when one of the entities comes from outside your organization. It may require a lot of manual work in spreadsheets to join the entities, and the results of this work may not be easy to reuse when new data arrives. You need to run these rules, manually or scheduled. If the system does not find a match, or if it finds too many candidates, you can then add a different rule that is applied to this situation. Moreover, you can manually select the correct match which is then used in future for identical entries. You can integrate the result of an Intelligent Lookup in a view and use the view for another Intelligent Lookup. In summary, SAP Datasphere Intelligent Lookup lets you iteratively join entities by defining rules to match records and then reviewing and processing the results.
Views
Views are the most important models to adjust and combine existing data sets. Like for a table, you should define the semantic usage property of a model (as facts, dimensions, texts, or hierarchies). Depending on skills and requirements, you can either define a Graphical View using an intuitive graphical editor, or an SQL view for a single SQL statement or SQL code with several statements using a text editor. For example, you can create an SQL view with a JOIN operation.
Model E/R-models, tables, views, SQL & SQL-script views
Graphical Views
The easiest way to create a view is using the no code/low code editor. It focuses on the most-used modeling operators and properties. The result is a graphical view. The first step in defining a source for a data model is to choose the repository or, for external sources, a predefined connection from the Source Browser. SAP Datasphere provides various methods for importing (remote) tables or database views into your space: A connection using the Sources tab of the Source Browser in any of the data builder editors. Data Builder start page: Use the import wizard to import remote tables from any connection. E/R Model: Import tables into the space and add them to the E/R model diagram. Graphical or SQL view: Import tables into the space and use them directly as sources in the view.
Enable the " Expose for Consumption " switch (reporting layer) in the Model Properties area to make a view available for consumption in SAP Analytic Cloud, other analytic clients, and in ETL and other tools. Properties are effective after you save and successfully deploy the model. Note Instead of creating a view with the Expose for Consumption property, SAP recommends to use the model type Analytic Model for consumption with SAP Analytic Cloud. This object type does not require the Expose for Consumption setting. All Analytic Models are automatically exposed. Creating Flows and Task Chains in the Data Builder Flows and Task Chains Suppose, the federation approach shows suboptimal runtime performance or puts much stress on your source system. In such cases, consider persisting the transformed values, either as a periodic snapshot or in a delta load scenario.
Data Flows
SAP Datasphere Data Flow functionality enables the definition of more advanced ETL (Extract, Transform, Load) operations that complement existing data federation (including also Snapshot and real-time access replication). SAP Datasphere Data Flow functionality enables the definition of more advanced ETL flows that complement existing data federation (including also Snapshot and real-time access replication). Create a data flow to move and transform data in an intuitive graphical interface. A data flow always has a persistent target table. You can drag and drop sources from the Source Browser, join them as appropriate, add other operators to remove or
create columns, aggregate data, and do Python scripting, before writing the data to the target table. In the data flows, you can use a series of standard transformations without the need of programming knowledge in a graphic way. Therefore, what is the difference between views and Data Flows? Views transform the data at the moment they are read without storing the result (although storing the view result as a snapshot is possible). Data Flows transform and persist the changes in a target table with a dedicated name. Moreover, with a data flow, you can append new records or even delete selected records from the target table. Data flows follow the traditional ETL approach, this means that data is first transformed and then stored. If you apply complex transformations or replicate mass data, resources are blocked in the source system for a long time. In such cases, consider a different approach called ELT. ELT means that you extract and load (store) data first and then apply the transformation only inside SAP Datasphere. This sequence allows businesses to initially load raw data into the data warehouse and then flexibly modify (transform) the same data set in different ways as needed for different use cases.
