SAP Business Data Cloud and Datasphere News in January

SAP Business Data Cloud and Datasphere News in January

This year we really started with a bang!
You will see many great features and enhancements being delivered with our three releases already in the first month of the year.

Learn more in my short top features video in January 2026 on YouTube 📺

In addition, explore the latest updates in our community news blogs and more.

My top features in January

2026 really starts with a firework of excellent new features. Therefore, this list is a lot longer than usual.

Task Chains & Scheduling

Task ports of tasks in Task Chains

You can now use task ports to provide enhanced flexibility and error handling in task chains. Task ports allow you to control the flow of task based on success or failure outcomes. Additionally, the new Ignore Error feature allows you to disregard the status of a specific task from the overall task chain status evaluation. This feature is beneficial for maintaining workflow continuity, managing errors effectively, and customizing task execution paths.
🔗 More information

Enable Auto-Retry for a Task in a Task Chain

The auto-retry feature allows up to three automatic retries for a task in a task chain. This feature will automatically retry failed tasks to help complete a task in a task chain and improve resilience.
🔗 More information

Technical user for scheduling

You can now assign a technical user to schedule data integration tasks. This allows you to minimize the number of users that manage schedules. In addition, this avoids failed schedules and tasks related to expired consent and authorization.
🔗 More information: Modify the Owner of a Schedule and Scheduling Data Integration Tasks.

Manage Tasks with REST APIs

You can use REST APIs to run task chains and retrieve task logs and existing task log history without using the Command Line Interface (CLI). In addition, you can now also use a new CLI command to get flexible and controlled log information.
🔗 More information Manage Tasks Using REST APIs and Manage Tasks and Task Chains via the Command Line.

Analytic Model: New functions in the Expression Editor (YTD, QTD, MTD)

For calculated and restricted measures, as well as for calculated and restricted structures, the new standard functions YTD, QTD, MTD and %GrandTotal are now supported in the expression editor for these measures in the Analytic Model.

Replication Flow

Reuse a target table of one replication as a source of another replication flow

You can reuse a target local table (with or without delta capture enabled) of an existing replication flow as a source table in another replication flow. It will automatically create a relationship between the two replication flows and allows now multi-step replication scenarios. This simplifies the distribution of data from SAP Datasphere to multiple downstream systems.
🔗 More information.

Support parquet format for cloud storage sources

You can now replicate data stored in parquet files when using the following cloud storage providers as the source of your replication flow:

Amazon Simple Storage ServiceGoogle Cloud StorageMicrosoft Azure Data Lake Gen2

🔗 More information.

Partitioning of local tables with data

You can create partitions on existing local tables that contains existing data stored in a standard space (HANA Cloud storage and compute). That way you can break down your data into smaller tables and better manage tables with a large volume of data.

At deployment you can decide if the partitioning task happens

Automatically After Deployment: Once the deployment is complete, the partition creation task will start automatically.Automatically at a Scheduled Time: You define a timestamp when the partitions must be created. Once deployment is complete, the partition creation task will start automatically at the scheduled timestamp.Manually at Later Time: Once the deployment is complete, you will be able to create the partitions when you want.

🔗 More information.

Longer technical names of data builder objects

We increased maximum length for technical names for data builder objects and elements that now allow for more descriptive and detailed naming conventions.
The maximum length has changed for the following objects and elements:

Views, tables, E/R models: 100Views with semantic usage Analytical Dataset (Deprecated): 60Analytic Models: 60Hierarchies created inside a dimension view: 20Columns: 100Input parameters: 100

🔗 More information.

BW Modernization

We also delivered major milestones for the BW modernization scenarios. Metadata extraction of SAP BW 7.5 is now enabled for the catalog, plus also a data lineage from SAC story down to the BW 7.5 objects.

Extension of the data product generator with

Introduction of Scenario Generator: semantical import of BW 7.5 and BW/4HANA InfoProvider and its dependencies including attributes and textsMultiple target support: select any Datasphere tenant and spaceTransport of BW subscriptionsMass handling of BW InfoProviders

Requires BWMT 1.27 and SAP Note 3692414

🔗 More information Connecting to BW Systems and Catalog Asset Details.

📺 Check out the Top Features Playlist: https://www.youtube.com/playlist?list=PL3ZRUb1AKkpS8bwmAREXo78O2xccXzoiy

Webinar Series: The Next Era of Business Data

SAP Business Data Cloud is part of a comprehensive strategy for enterprise data designed to address complex enterprise data management challenges. We provide an overview of SAP Business Data Cloud and guide you through its integral pillars and strategic vision.
👉 Sign up now: SAP BDC: The Next Era of Business Data

🔹Jan 29 | Discover SAP Business Data Cloud in 2026 – recording

🔹Feb 05 | The Smart Path to SAP BW Modernization – recording

🔹Feb 19 | Empowering SAP Datasphere Users with SAP Business Data Cloud Innovations

🔹Feb 26 | From Data to Decisions: Analytics with SAP Analytics Cloud and SAP BDC

🔹Mar 12 | From Data Silos to Business Insight: The Power of BDC Connect

🔹Mar 19 | An Architect’s Blueprint for SAP Business Data Cloud as the Foundation for Enterprise AI

🔹Mar 26 | Design. Govern. Deliver. Data Products – Data Product Studio

🔹Apr 16 | Breaking Down the SAP BDC Pricing Model

My favorite blogs in January

What a great blog about seamless planning by @Max_Gander and @marc_daniau.
The blog shows how to run SAP HANA APL time‑series forecasts in SAP Datasphere and feed the results into a seamless SAP Analytics Cloud (SAC) planning model as a live forecast version:
🔗 Generating and Integrating Automated Predictive Library (APL) Forecasts in a Seamless Planning Model

@Phil_from_Madrid wrote a nice piece about implementing a “Shift-Left” architecture within the SAP business data fabric shifts responsibility for data quality to the source, aiming for data to originate as a governed, high-quality asset. While this approach accelerates time-to-insight through the creation of reusable data products, it necessitates development teams taking on new data-modeling tasks and managing increased technical debt upstream.
🔗 The consequences of implementing strictly Shift-left architectures

This blog from @vitvas details a method to execute ABAP code directly from an SAP Datasphere Data Flow without relying on the BW Bridge. This approach uses virtual elements in CDS views and a generic OData connection to trigger complex logic or external processes in the source system.
🔗 How to call AS ABAP from SAP Datasphere

More blogs about SAP BDC and Datasphere to check out 👇

Find more information and related blog posts on the topic pages for SAP Business Data Cloud and SAP Datasphere. Also check out the on SAP Help for SAP Business Data Cloud and SAP Datasphere, including for troubleshooting and analysis guides, onboarding guides, and more.

SAP Business Data Cloud and Datasphere News in JanuaryThis year we really started with a bang!You will see many great features and enhancements being delivered with our three releases already in the first month of the year.Learn more in my short top features video in January 2026 on YouTube 📺In addition, explore the latest updates in our community news blogs and more. My top features in January2026 really starts with a firework of excellent new features. Therefore, this list is a lot longer than usual. Task Chains & SchedulingTask ports of tasks in Task ChainsYou can now use task ports to provide enhanced flexibility and error handling in task chains. Task ports allow you to control the flow of task based on success or failure outcomes. Additionally, the new Ignore Error feature allows you to disregard the status of a specific task from the overall task chain status evaluation. This feature is beneficial for maintaining workflow continuity, managing errors effectively, and customizing task execution paths.🔗 More information Enable Auto-Retry for a Task in a Task ChainThe auto-retry feature allows up to three automatic retries for a task in a task chain. This feature will automatically retry failed tasks to help complete a task in a task chain and improve resilience. 🔗 More information Technical user for scheduling You can now assign a technical user to schedule data integration tasks. This allows you to minimize the number of users that manage schedules. In addition, this avoids failed schedules and tasks related to expired consent and authorization.🔗 More information: Modify the Owner of a Schedule and Scheduling Data Integration Tasks. Manage Tasks with REST APIs You can use REST APIs to run task chains and retrieve task logs and existing task log history without using the Command Line Interface (CLI). In addition, you can now also use a new CLI command to get flexible and controlled log information.🔗 More information Manage Tasks Using REST APIs and Manage Tasks and Task Chains via the Command Line. Analytic Model: New functions in the Expression Editor (YTD, QTD, MTD)For calculated and restricted measures, as well as for calculated and restricted structures, the new standard functions YTD, QTD, MTD and %GrandTotal are now supported in the expression editor for these measures in the Analytic Model. Replication FlowReuse a target table of one replication as a source of another replication flow You can reuse a target local table (with or without delta capture enabled) of an existing replication flow as a source table in another replication flow. It will automatically create a relationship between the two replication flows and allows now multi-step replication scenarios. This simplifies the distribution of data from SAP Datasphere to multiple downstream systems.🔗 More information. Support parquet format for cloud storage sourcesYou can now replicate data stored in parquet files when using the following cloud storage providers as the source of your replication flow:Amazon Simple Storage ServiceGoogle Cloud StorageMicrosoft Azure Data Lake Gen2🔗 More information. Partitioning of local tables with dataYou can create partitions on existing local tables that contains existing data stored in a standard space (HANA Cloud storage and compute). That way you can break down your data into smaller tables and better manage tables with a large volume of data.At deployment you can decide if the partitioning task happensAutomatically After Deployment: Once the deployment is complete, the partition creation task will start automatically.Automatically at a Scheduled Time: You define a timestamp when the partitions must be created. Once deployment is complete, the partition creation task will start automatically at the scheduled timestamp.Manually at Later Time: Once the deployment is complete, you will be able to create the partitions when you want.🔗 More information. Longer technical names of data builder objectsWe increased maximum length for technical names for data builder objects and elements that now allow for more descriptive and detailed naming conventions.The maximum length has changed for the following objects and elements:Views, tables, E/R models: 100Views with semantic usage Analytical Dataset (Deprecated): 60Analytic Models: 60Hierarchies created inside a dimension view: 20Columns: 100Input parameters: 100🔗 More information. BW ModernizationWe also delivered major milestones for the BW modernization scenarios. Metadata extraction of SAP BW 7.5 is now enabled for the catalog, plus also a data lineage from SAC story down to the BW 7.5 objects.Extension of the data product generator withIntroduction of Scenario Generator: semantical import of BW 7.5 and BW/4HANA InfoProvider and its dependencies including attributes and textsMultiple target support: select any Datasphere tenant and spaceTransport of BW subscriptionsMass handling of BW InfoProvidersRequires BWMT 1.27 and SAP Note 3692414🔗 More information Connecting to BW Systems and Catalog Asset Details. 📺 Check out the Top Features Playlist: https://www.youtube.com/playlist?list=PL3ZRUb1AKkpS8bwmAREXo78O2xccXzoiy Webinar Series: The Next Era of Business DataSAP Business Data Cloud is part of a comprehensive strategy for enterprise data designed to address complex enterprise data management challenges. We provide an overview of SAP Business Data Cloud and guide you through its integral pillars and strategic vision.👉 Sign up now: SAP BDC: The Next Era of Business Data🔹Jan 29 | Discover SAP Business Data Cloud in 2026 – recording🔹Feb 05 | The Smart Path to SAP BW Modernization – recording🔹Feb 19 | Empowering SAP Datasphere Users with SAP Business Data Cloud Innovations🔹Feb 26 | From Data to Decisions: Analytics with SAP Analytics Cloud and SAP BDC🔹Mar 12 | From Data Silos to Business Insight: The Power of BDC Connect🔹Mar 19 | An Architect’s Blueprint for SAP Business Data Cloud as the Foundation for Enterprise AI🔹Mar 26 | Design. Govern. Deliver. Data Products – Data Product Studio🔹Apr 16 | Breaking Down the SAP BDC Pricing Model My favorite blogs in JanuaryWhat a great blog about seamless planning by @Max_Gander and @marc_daniau.The blog shows how to run SAP HANA APL time‑series forecasts in SAP Datasphere and feed the results into a seamless SAP Analytics Cloud (SAC) planning model as a live forecast version:🔗 Generating and Integrating Automated Predictive Library (APL) Forecasts in a Seamless Planning Model @Phil_from_Madrid wrote a nice piece about implementing a “Shift-Left” architecture within the SAP business data fabric shifts responsibility for data quality to the source, aiming for data to originate as a governed, high-quality asset. While this approach accelerates time-to-insight through the creation of reusable data products, it necessitates development teams taking on new data-modeling tasks and managing increased technical debt upstream.🔗 The consequences of implementing strictly Shift-left architectures This blog from @vitvas details a method to execute ABAP code directly from an SAP Datasphere Data Flow without relying on the BW Bridge. This approach uses virtual elements in CDS views and a generic OData connection to trigger complex logic or external processes in the source system. 🔗 How to call AS ABAP from SAP Datasphere More blogs about SAP BDC and Datasphere to check out 👇SAP Business AI & Sustainability: WHAT[BDC-SAC] IP Whitelist and Access Control in SAP Analytics Cloud (SAC)[BDC-SAC] Akamai CDN , Metadata Understanding Data Download When Access SACHow to call AS ABAP from SAP DatasphereSneak Peek in to SAP Analytics Cloud release for Q1 2026What’s New on My Metrics 2026 Q1 QRC: Schedule a Metrics ReportThe Integration of SAP Business Data Cloud and Customer Data Platform Multiplies the ValueSAP Datasphere and SAC Modernization: A Commercial Move with RewiringThe Future of SAP Analytics – Smart Modernization Journey Powered by SAP BDCGuiding Customers from SAP Business Data Cloud Discovery Workshop to a Clear Adoption RoadmapEssential preparation and expert tips: Data analyst – SAP Analytics Cloud certificationSAP RPT‑1: Why is it Essential for Predicting Business Outcomes in Today’s and Future Generative AI?Integrating SAP Business Data Cloud data products with SAP HANA centralBuilding a Graph Native AI Chatbot on SAP BTPThe consequences of implementing strictly Shift-left architecturesUnlock Faster Insights: Modernizing SAP BW with SAP Business Data CloudFilling the gab between BW and Datasphere (a bit): Implementing a few standard variablesWhat’s New in SAP BTP – Q4 2025 InnobytesConnecting SAP Sales Cloud V2 to a Standalone SAP Analytics Cloud – A Practical AlternativeSAP Business Data Cloud and AI: Telling the story to drive adoptionIntroducing SAP Business Data Cloud test, demo and development serviceGenerating and Integrating Automated Predictive Library (APL) Forecasts in a Seamless Planning ModelWorking with BW Authorizations in Datasphere on Enterprise LevelError message when trying to access “My Products Packages” for SAP BDCSAP BDC: The Next Era of Business DataBeyond Data Mesh: Entering the Data Product Economy with SAP Business Data CloudSAP Business Data Cloud Certification (C_BCBDC): The Ultimate Guide to Passing SAP’s New AI ScenarioAdopting UX best practices for intelligent applications in SAP Business Data CloudLeverage System Job Live Model to display Acquired Model Last Refreshed Time in StorySAP Developer News January 8th,, 2026SAP Business Data Cloud customer adoption: Teaming up for successSelling SAP Business Data Cloud: sales plays, bill of material and sizingBeyond the Buzz: Making SAP Business AI Work for the Real WorldSAP Business Data Cloud dev inspiration: blueprints for next-gen intelligent appsCommunicating the value of SAP Business Data Cloud using personasPositioning SAP Business Data Cloud with SAP SnowflakeHow to Access your SAP Business Data Cloud Free TrialEnhancing SAP HANA Database Development with Generative AI Support for HDI Artifacts Find more information and related blog posts on the topic pages for SAP Business Data Cloud and SAP Datasphere. Also check out the on SAP Help for SAP Business Data Cloud and SAP Datasphere, including for troubleshooting and analysis guides, onboarding guides, and more. Read More Technology Blog Posts by SAP articles

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SAP Business Data Cloud and Datasphere News in January