Modern SAP cloud data architectures manage harmonized data across multi-cloud environments with business semantics to unlock data-driven intelligent applications integrated with the SAP Business Suite.
Intelligent SAP Cloud Business Applications harness Business Data & AI to implement data-driven and AI-powered processes. Multi-cloud data management with SAP Business Data Cloud (BDC) harmonizes data across line-of-business solutions and offers self-service options with data democratization to empower business users with insights.
Integration layers of composable business data cloud platforms integrate large distributed data volumes and reduce data silos.
SAP BDC offers metadata driven data governance across heterogeneous systems with unified enterprise data models and views. Business data catalogs offer metadata based data access with data insights from origin to consumption visualized with data lineage information.
Composable data management platforms like SAP BDC combine data integration, storage and compute capabilities for data-driven SaaS applications. Cloud data platforms implement Data Fabric, Data Mesh and Data Lakehouse architecture patterns with metadata-driven business context, domain-specific teams and curated data layers on elastic compute engines.
Cloud Data Lakehouse architectures are built on modern cloud data storage layers optimized for data management and processing of all kind of data. These cloud data storage layers offer curated data which is organized, consumption-ready and conform with organizational standards and data models.
Furthermore, modern cloud storage layers implement data design patterns like medallion architectures, Delta Lake concepts, ACID transactions and schema support. Cloud data management includes metadata catalogs for governance purposes and to add a semantic layer as business representation.
Cloud data compute layers shall be separated from storage layer with independent scalability to to build data architectures with high performance and high availability distributed over multiple availability zones. Data compute engine examples are Databricks SQL Warehouses and Slowflake massive parallel processing (MPP) capabilities.
Intellgent applications integrate full-stack environments with semantic data models, AI enhancements and apps as consumption layer.
Integrated data products enable end-to-end busienss processes with semantics and offer AI-driven insights on the consumption layer.
Modern Business Data Cloud platforms offer data integration patterns for various sources of structured or unstructured data.
Data transformation and replication both create redundant data in the target system which can improve the performance of data reporting. Data Lake House environments support both data integration pattern with compute capabilities to transform data of ELT process on the target platform and large volume capabilities to store the replicated data.
Data virtualization abstracts data access, eleminates the need to move data and performs computing in the source systems. Federation combines the virtual access to different data sources in distributed landscapes. Virtual data access ensures that the business data is always up-to-date and facilitates compliance with company policies like restrictions to data locations.
SAP Datasphere Data Fabric data models add semantic layers as business context on top of the integrated data to avoid losing business context.
Real-time data replication works by continuously monitoring changes in source data and immediately replicating these changes to one or more target systems. This process often utilizes change data capture (CDC) technology to detect changes in real-time without impacting system performance.
SAP Business Data Cloud (SAP BCD) is a fully managed SaaS data platform which harmonizes data from SAP and non-SAP sources into a unified semantic layer of trusted data.
SAP BDC supports an open data platform with BDC Connect which implements bi-directional zero-copy data sharing between SAP Datasphere and cloud data management solutions like Databricks, Snowflake (planned for Q2/2026) or Azure Fabric (planned for Q3/2026). SAP data is shared via Data Products.
SAP BDC empowers distributed domain teams with Data Mesh concepts to assume ownership of data products through isolated, role-based Datasphere spaces and domain-specific modeling environments.
SAP HANA Cloud acts as central database service of the SAP Business Data Cloud.
SAP HANA Cloud can be managed with tools like Cloud Central to work with HANA instances, the SAP HANA Cloud Cockpit for administration tasks and the SAP HANA Database Explorer to browse the database catalog, execute statements and import or export data.
SAP HANA Cloud is scalable with regard to compute and data storage independently (memory + disk), vertically (in/out) and horizontally (up/down). SAP Hana Cloud does not support auto-scaling and cannot scale dynamically based on the current workload to speed up running queries.
SAP Hana Cloud supports hybrid transactional analytical processing (HTAP) with a column oriented in-memory column store. Data is stored according to the usage type within different layers of a storage data pyramid with frequently uses hot data hold in memory, infrequently used warm data stored in the SAP HANA Native Storage Extension (NSE) on disk and cold data in the SAP HANA Data Lake. Sizing of SAP HANA defines the ratio between the data pyramid layers with memory quota and required space on disk and data lake.
The connectivity options of SAP HANA Cloud support virtualization, federation and replication capabilities supported by HANA Smart Data Access (SDA) with virtual tables and Smart Data Integration (SDI).
SDI Adapters support Change Data Capture (CDC) to extract only the delta between two sets of data and implement Operational Data Provisioning (ODP) with ABAP backend systems. ODP usage should be avoided by third-party applications because SAP doesn't guarantees stable external access in the future.
SAP HANA calculation views are the main graphical design time objects, which will be converted to SQL processible column views at run-time. Calculation models are translated into a relational SQL representation whenever possible to execute SQL engine optimization. This unfolding transformation is applied globally and automatically.
SAP HANA calculation views can be enhanced with read-only SQLScript procedures and functions. These procedures or functions return tables or scalar values of the view or calculate view input parameters for dynamic placeholders. Pushing down input parameters as filters, pruning (join, union, column) and parallelization are optimization concepts.
There are three types of calculation views, dimension or central master data view, cube with and without star schema.
SAP HANA offers multi model capabilities for different data types and tasks like spatial, vector or graph data. Graph modeling can be applied to data representing networks with entities (nodes) and lines (edges) with node attributes to add additional meaning.
SAP HANA Deployment Infrastructure (HDI) containers allow multiple deployments on same HANA instance with different corresponding schemas of schema less database artifacts.
SAP HANA's holistic security framework is implemented on different levels like authorization management based on privileges bundled with roles or assigned to users, on object level or as analytical privileges to define access on row level.
Further security levels are masking or anonymization to hide sensitive data and Personal Security Environments (PSE) to securely store cryptographic information like certificates or private keys.
HANA's column-based storage and processing is optimized for online analytical processing (OLAP). Column tables enable multi-core parallel processing and fast aggregations in contrast to row storage which is regarded as the optimal storage design for an online transactional processing (OLTP).
HANA Cloud column store tables are automatically compressed to reduce size in memory and CPU cache. Compression is applied during the delta merge operation. SAP HANA table columns can be partitioned to improve performance which allows to load only actually needed columns for processing to memory.
Dictionary encoding compression removes the repetition of values in a column. Each distinct value is stored only once in the dictionary store. Integers representing unique dictionary values are addressable with indices. Some further compression techniques are run-length, cluster and prefix encoding.
SAP Analytics Cloud (SAC) is the primary front-end tool for data visualization and planning in SAP Business Data Cloud.
SaC Data analytics offers different categories to gain valuable data insights from descriptive analytics to understand the past business, predictive analytics which predict future business and prescriptive analytics which extends predictive analytics with specific, optimal actions to achieve desired outcomes.
SAP Analytics Cloud offers Augmented Analytics and AI-powered Visualizations. Smart Predict answers business questions about future trends. The coupled date and target values information are called the signals which can be analyzed by Smart Predict Time Series Forecasting. The Just Ask AI-based assistant enables natural language queries on data models with visualized answers generated as charts or tables.
SAP Analytics Cloud predictive planning uses automated machine learning from historical data to smart predict what could happen with Time Series Forecasting and to improve planning accuracy focused on strategic decisions.
SAP Analytics Cloud visualizes data in stories which can be imported or accessed live without storing the data inside SAC. SAP BDC Intelligent Applications use SAP-managed or custom live data connections between SAP Datasphere and SAP Analytics Cloud.
SAC stories visualize data of models in tables and charts on one or multiple pages with interactive elements which allows business users to explore data interactively.
SAC offers templates and predefined story page types as starting point for custom story implementations. Additionall, the SAP Analytics Cloud Designer SDK enables the extension of analytical applications with custom widgets as web components.
Modern Azure data warehouse platforms can be built on Azure Data Lake with high performant data ingestion to enable analytics BI reporting and data-driven serverless SaaS applications. Efficient data access with hierarchical namespaces on Blob storage enables enterprise big data analytics based on Azure Data Lake Storage Gen2. Data lakes can store huge amounts of frequently used of data for SaaS applications or BI reports as presentation layers.
Azure Data Factory (ADF) data ingestion implements ETL data integration and transformations with scale-out capabilities. The ADF management interface allows to orchestrate multiple activities (processing steps) of data pipelines. Transformations with mapping data flows on serverless infrastructures can be visually designed and monitored.
ADF pipelines perform actions with integration logic like running SQL Server Integration Services (SSIS) packages, which are similar to ADF pipelines. Linked services define required information to link disparate data sources like SAP HANA, S/4HANA, SAP ECC or BW to the data factory with available connectors. Automated uploads can be implemented with copy activities e.g. from Azure Blob storage for reporting to Azure SQL DB.
Azure offers solutions to generate value from data with. data lakes storing unstructured, raw data to be cleaned and structured by data warehouses for analytics or data-driven business processes.
Azure DataBricks Lakehouse architectures combine governance of data warehouses with Spark based big data processing for analytics and machine learning. DataBricks supports Delta Lake with tables for ACID transactions and the Unity Catalog for Data Governance or Discovery. Azure Data Lake Storage credential passthrough is a premium feature which allows storage access without configured service principals.
Azure Synapse Analytics combines data integration, enterprise data warehousing and big data analytics with massive parallel processing (MPP) to run high-performance analytics. The data integration capabilities (pipelines, data flows) are based on a subset of Azure Data Factory features.
Synapse Analytics uses the PolyBase virtualization technology to access external data stored in Azure Blob storage or Azure Data Lake Store via T-SQL language. Data from disparate sources (such as on-premise, IoT) can be aggregated with the data warehouse features.
Azure Synapse Link integrates SQL or Cosmos DB databases with Azure Synapse Analytics to provide near realtime analytical processing (HTAP) on operational data.
Azure Analysis Services enables companies to implement tabular semantic data models for business users to query data interactively with Power BI which offers integrated Analysis Services premium features. Tabular models of connected databases can run in-memory (default for e.g. SAP HANA, PostgreSQL) or in DirectQuery (to overcome memory limitations for e.g. Azure SQL) mode.
Azure Data Explorer is a fully managed, scalable, high-performance, big data analytics platform, optimized for interactive, ad-hoc queries over various data types (structured, semi-structured, unstructured) ingested into single set data collection.
Some Azure Data Explorer use-cases are log analytics (clickstream data, product logs), time series analytics (e.g. IoT) with built-in capabilities, general-purpose exploratory analytics e.g. on streaming data or scoring of high-quality machine learning models (applying on data) created and managed with Azure ML.
Power BI provides visualization and reporting insights using LiveQueries without datasets or DirectQueries to PowerBI datasets of connected data sources. Data imports enable fast processing with advanced and premium features for large datasets or large scale support.
Some sample connection options for Power BI are DirectQuery to SAP HANA, Amazon Redshift or Athena and LiveQuery to Azure Analysis Services.
| Layer / Service | Short Description |
|---|---|
| Ingestion Layer | |
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AWS Glue is a serverless data integration service. Glue offers built
in connectors
to common sources like S3, Kinesis, Redshift, Postgres. Furthermore, AWS
Glue allows development of custom Spark, JDBC or Athena based
connectors. Glue crawlers populate the AWS Glue Data Catalog with tables from source data stores. |
 |
Appflow ingests SaaS applications data |
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Data Migration to ingest data from several operational RDBMS and NoSQL databases |
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Datasync ingests files from NFS and SMB |
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Kinesis Firehouse ingests streaming data. Kinesis Data Stream enables building real-time analytics pipelines. |
| Storage Layer | |
 |
S3 to store structured, semi-structured, and unstructured data, typically using open file formats. |
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Amazon Redshift provides petabyte scale data warehouse storage for highly structured data. |
| Catalog Layer | Discovery and Governance. Layer stores the schemas of structured or semi-structured datasets in Amazon S3 |
|
AWS Glue Data Catalog central metadata catalog for entire data landscape |
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AWS Lake Formation offer a Data Catalog based on Glue. Enables row level security |
| Processing Layer | |
|
AWS SQL based ELT with Redshift Spectrum ELT |
| Big Data Processing | Apache Spark Jobs |
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AWS EMR Big Data Spark job processing |
|
AWS Glue Spark jobs |
| Near realtime ETL | |
 |
Spark Streaming with Kinesis Analytics |
|
AWS EMR Big Data Spark Streaming |
|
AWS Glue Spark Streaming |
| Consumption Layer | |
 |
QuickSight is machine learning powered. Generated data models automatically understand the meaning and relationships of business data |
| Data Virtualization | Athena and Redshift spectrum are serverless services offering virtualization features like Federated Query and User Defined Functions. Both services have native integration with the AWS Glue metadata catalog as well as the Hive Metastor and use SQL to query virtual tables of data stored on S3. Both services provide JDBC drivers. Athena is a standalone interactive service, whereas Spectrum is part of the Redshift stack |
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AWS Redshift in built on a MPP architecture. AWS Glue virtualizes Redshift spectrum data access to S3. External tables define schemas and location of S3 data files. Virtualized tables can be joined with tables in Redshift. Federated Query allows you to run a Redshift query across additional databases and data lakes like S3, Amazon RDS or Aurora. |
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Athena query results stored on S3, can be loaded into Redshift |