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CAP just got faster: What's new in cds-caching 1.0CAP just got faster: What's new in cds-caching 1.0What changed in cds-caching at 1.0, announced at reCAP 2025 by the plugin's own maintainer. The substantive change is the read-through API: cache.rt.run(), send(), exec() and wrap() replace the bare cache.run() and cache.send(), and each returns the cache key and hit-or-miss metadata beside the result rather than the value alone. The second is quieter and easier to miss on an upgrade: user, tenant and locale stopped being folded into request-based cache keys by default and became opt-in through keyManagement, or per operation through a key template. Both of those still describe 3.x. What does not is the second half of the post: the metrics configuration it shows was reshaped at 2.0 and the statistics and dashboard settings were removed at 3.0, so take the monitoring and dashboard sections from the README instead. Read it as the successor to the introduction post, which still shows the deprecated calls.published 2025-07-15
CAP just got faster: What's new in cds-caching 1.0
cds-caching 1.0, now 3.1.1, cds 9, maintenance until 2027-06. Affects A reference.
Explore your Hyperscaler data with SAP Business Data CloudExplore your Hyperscaler data with SAP Business Data CloudSAP's map of the ways data held on hyperscaler and partner platforms is joined with SAP data in SAP Business Data Cloud, with a sub-page each for AWS, Azure, Google Cloud, Databricks and Snowflake. It sets four modes side by side: BDC Connect shares data products in both directions over the Delta Sharing protocol from the object store layer, while in the SAP Datasphere layer federation reads remote tables in place, Replication Flows copy SAP data out to stores such as Amazon S3 or Google BigQuery, and Data Flows import files and transform them into Datasphere tables, the last three being the ground loading a warehouse covers. The Snowflake and BigQuery connectors are dated to 2026 releases, and a closing page lists what slows federation, from source and tenant in different regions to queries that do not filter and aggregate at the source.published 2026-05-08
Federated Machine Learning with SAP DatasphereFederated Machine Learning with SAP DatasphereSAP's design for training predictive models on an external machine learning platform against SAP Datasphere data that is not replicated to that platform. The open-source FedML Python library runs in the platform's notebook, connects to Datasphere over Python/SQLDBC and reads the analytic models in which Datasphere unifies SAP and non-SAP sources; a trained model can then be deployed to SAP AI Core, or in several sub-pages to SAP BTP, Kyma runtime, which is the predictive models path where training happens outside SAP. Sub-pages cover Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Databricks, NVIDIA RAPIDS on GPUs and IBM watsonx.published 2025-01-23
Integrate and Extend with SAP Build Process AutomationIntegrate and Extend with SAP Build Process AutomationSAP's reference architecture for SAP Build Process Automation integrating and extending SAP and non-SAP solutions in cloud and hybrid landscapes, the ground process automation covers. Processes start from events, APIs, schedules or forms, the forms built in SAP Build Process Automation, as UI5 applications or in SAP Build Apps; they reach other systems through the SAP Connectivity service with actions over APIs from sources that include CAP and RAP OData destinations and SAP Business Accelerator Hub specifications, and users reach them through SAP Build Work Zone and SAP Mobile Start, with SAP Task Center as the place tasks are managed. Decisions, Process Visibility, Processes and Automations come inside the one subscription and cannot be decoupled from it; CAP appears only as a source of API actions, and the examples are business scenarios such as mass job requisitions in SAP SuccessFactors.published 2025-05-14
Medallion Reference Architecture for Big Data Processing in SAP HANA CloudMedallion Reference Architecture for Big Data Processing in SAP HANA CloudSAP's medallion layout for SAP HANA Cloud: structured, semi-structured and unstructured data lands raw in HANA Cloud, data lake files as the bronze layer, is cleansed into a common structure in silver, held in data lake files or the HANA database, and is curated and read-optimised in gold, from where it can also be exposed as a Delta Share. The staged path from raw to consumable is the ground loading a warehouse covers, with Spark jobs and SAP Datasphere data flows named as ways to move data between layers and SAP Analytics Cloud among the consumers of gold. It leaves the ingestion method open, to be chosen by source, freshness and performance, and names no pipeline or schedule beyond that.published 2025-04-03
SAP HANA Cloud as an Esri GeodatabaseSAP HANA Cloud as an Esri GeodatabaseSAP's design for running an Esri ArcGIS Enterprise geodatabase inside SAP HANA Cloud, so that geometries and SAP business data sit in one database and are joined in a HANA calculation view, which is the spatial part of graph, spatial and document data. It traces three personas: an analyst in ArcGIS Pro building query layers over that view, a developer whose CAP application models spatial types such as ST_Point in CDS, and an analyst using a Fiori app on top of such an application. The deployment advice stops at placement: ArcGIS Server and HANA Cloud go in the same region and availability zone, because Esri's certified architectures allow under 5 milliseconds of network latency between them.published 2025-03-25
Transforming Enterprise Data Strategy with SAP Business Data CloudTransforming Enterprise Data Strategy with SAP Business Data CloudSAP's layered picture of SAP Business Data Cloud, from lake storage and compute up to a knowledge core and agents, with eight sub-pages that each work one part through. The data products page is the cataloguing and lineage part: datasets described by Open Resource Discovery metadata, listed in the BDC catalog and shared over Delta Sharing to SAP Databricks and SAP HANA Cloud, with SAP Datasphere as a consumer on the intelligent content page; other pages install intelligent content, bring SAP BW data in through the Data Product Generator, provision groups by SCIM from Cloud Identity Services into SAP Databricks, which is identity provisioning, and train predictive models in SAP Databricks that SAP AI Core then serves for batch and real-time scoring. The main page states direction more than mechanism, and the BW page marks SQL Share for derived and OData for analytical data products as to be available.published 2026-05-12