SAP Datasphere frequently releases updates containing new features. Here is an overview of the main developments from this quarter.
SAP Datasphere continues to evolve to simplify and accelerate data management, modeling, and analysis. The Q1 2026 update brings major innovations that strengthen the platform by combining automation, artificial intelligence, and the reuse of existing work. These new features help save time, improve model accuracy, and make the user experience more intuitive.
AI and productivity in modeling
AI-assisted semantic generation
To simplify data creation and organization, SAP Datasphere now integrates AI to assist with semantic modeling. AI can automatically identify data types, detect measures and attributes, suggest table and view keys and structures, and define semantic usage (fact, dimension, etc.).
In Q4 2025, this capability was extended to tables and views, providing an even more complete generation of the semantic structure. Users remain at the center of the process with the ability to validate and adjust suggestions. This significantly reduces modeling time and improves model standardization.
AI-assisted SQL generation
SAP Datasphere also introduces automatic SQL generation in the view builder. This feature accelerates development for experienced users, facilitates learning for beginners, and maintains full control through validation and adjustment of generated code. The goal is to make the SQL experience more intuitive and productive.
Enhanced analytics and business functions
Native time functions
SAP Datasphere enriches analytical models with ready-to-use business functions: MTD (month-to-date), QTD (quarter-to-date), YTD (year-to-date), and percentage of total calculations. These functions simplify time-based analysis and reduce the need for manual calculations within models.
Currency conversion and analytical model enhancements
SAP Datasphere improves currency conversion by optimizing its behavior and simplifying the sharing of views using it, while strengthening analytical models through smoother sharing of Analytic Models, support for associated dimensions in Data Access Controls (DAC), and more robust access governance.
Data integration and replication
Data replication and delta management
A major enhancement involves SAP source replication. It is now possible to define primary keys at the source entity level in Replication Flows, enable delta management even without a native primary key, use Initial + Delta mode for SAPI extractors, and support ODP sources (SAPI, CDS views, SAP BW). This allows data to be replicated even when the source does not have a defined primary key.
Performance optimization and data pipelines in SAP Datasphere
SAP Datasphere improves data management and performance by introducing support for Parquet files, handling large files in Object Store targets, optimizing partitioning, and reusing local tables in Replication Flows. It also strengthens transformation and replication flows through object versioning, removing the inbound buffer for local file tables, advanced pipeline analysis via Data Pipeline Analyzer, and managing Replication Flow executions via REST API and CLI.
Transporting transformation flow configurations
Execution configurations for Transformation Flows can now be transported between environments. Supported elements include the execution mode (SAP HANA / Spark), batch settings, and Spark application selection. This improvement guarantees better consistency across development, test, and production environments.
Orchestration, automation, and governance
Process orchestration
SAP Datasphere strengthens process orchestration with the introduction of auto-retry in Task Chains, allowing automatic recovery upon failure. Task Ports improve the structuring of dependencies between steps, while output parameters in Task Chains, added in February, facilitate dynamic chaining of executions. Finally, using a dedicated technical user for scheduling improves the robustness and governance of automated executions.
Governance and security
Governance is reinforced with better visibility of Data Access Controls applied to sources, enabling a deeper understanding of access rules. Consent expiration notifications complement this approach by improving proactive authorization management. Furthermore, data lineage is enriched, particularly for BW DTPs, to strengthen end-to-end data traceability.
Monitoring and operations
Monitoring capabilities evolve with the introduction of the Data Pipeline Analyzer, enabling advanced diagnostics of data flows. Execution tracking is also improved thanks to exposure via API and CLI, facilitating automation and integration into external tools. Overall, this contributes to a global reinforcement of data pipeline supervision.
Conclusion
Recent developments in SAP Datasphere confirm a clear direction:
- increased automation through AI and orchestration
- industrialization of data pipelines
- strengthened governance and traceability
- simplified developer experience
SAP Datasphere is gradually evolving into a complete platform for intelligent, integrated, and governed data engineering.
