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Accelerate agentic planning with trusted data productsAccelerate agentic planning with trusted data productsGoverned data products as the ground for AI planning in SAP Analytics Cloud: using a ready data product or building one in SAP Business Data Cloud, the cataloguing and lineage concern, then agentic AI automating planning tasks and producing insights on top. The abstract names the two products and the data product step and does not say which planning tasks the agents take over. A TechEd 2026 hands-on session (Thursday 29 October, Berlin), presented by Jie Deng, Maximilian Paul Gander and Richard Mooney, written here from SAP's abstract; the recording is not yet published.
Flexible analytics at speed: Calculation views in SAP Business Data CloudFlexible analytics at speed: Calculation views in SAP Business Data CloudCalculation views in SAP HANA Cloud, the reusable analytic modelling blocks built in its graphical modelling environment, with three newer parts the abstract names: AI-assisted modelling, refactoring, and advanced materialisation, and the way the views sit inside SAP Business Data Cloud. What a materialisation is chosen on, and what the AI assistance generates, is not stated. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Hansika Colomboge Dona, Jan Zwickel, Niklas Siemer and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Transform business data into AI-supported insightsTransform business data into AI-supported insightsBusiness metrics turned into insights, narratives and recommendations by SAP Joule's agents, with People Intelligence, the SAP Snowflake solution extension for SAP Business Data Cloud and SAP Analytics Cloud connected in SAP Business Data Cloud. The abstract names what is connected and what comes out, and does not say what the exercise builds or where the Snowflake extension sits in it; People Intelligence and the extension are not atlas entries. A TechEd 2026 hands-on session (Wednesday 28 October, Berlin), presented by Babacar Toure, Maximilian Paul Gander, Neetha Pucknat and San Tran, written here from SAP's abstract; the recording is not yet published.
Discovering SAP Fiori Elements Analytical functions by implementing SAP Support Message ReportingDiscovering SAP Fiori Elements Analytical functions by implementing SAP Support Message ReportingAnalytical annotations learned by building something the author wanted — a view over SAP support messages — after a conversation in February 2021 that the post dates precisely. Exploration written up as exploration.published 2021-03-14
Predictive Asset Insights embedded Live analytics using CAP and SACPredictive Asset Insights embedded Live analytics using CAP and SACCAP as the layer between a line-of-business application's data and a live Analytics Cloud story embedded back into it — master data and operational data both. Structured as ten steps, which keeps it honest about how much of this is configuration rather than modelling.Saif, published 2021-07-18
Accelerate agentic planning with trusted data productsAccelerate agentic planning with trusted data productsGoverned data products as the ground for AI planning in SAP Analytics Cloud: using a ready data product or building one in SAP Business Data Cloud, the cataloguing and lineage concern, then agentic AI automating planning tasks and producing insights on top. The abstract names the two products and the data product step and does not say which planning tasks the agents take over. A TechEd 2026 hands-on session (Thursday 29 October, Berlin), presented by Jie Deng, Maximilian Paul Gander and Richard Mooney, written here from SAP's abstract; the recording is not yet published.
Agent-to-agent in action: SAP and Adobe CX Enterprise Coworker at HenkelAgent-to-agent in action: SAP and Adobe CX Enterprise Coworker at HenkelA customer story: SAP's data agents and SAP Business Data Cloud with Adobe CX Enterprise Coworker putting enterprise data in front of Henkel's marketing teams, with faster decisions as the stated result. The abstract names Business Data Cloud as the data side of an agent-to-agent setup and says nothing about what it does there or how the two agents talk; neither Adobe's product nor the data agents are atlas entries. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Ankur Jain, Ramalingam Ramani and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Agentic SAP BW modernization: Your path to SAP Business Data CloudAgentic SAP BW modernization: Your path to SAP Business Data CloudStrategies for moving from SAP Business Warehouse to SAP Business Data Cloud, with dedicated migration tools and AI agents to shorten the move while keeping what was built in SAP BW, the platform migration concern for a warehouse. A Fresenius speaker co-presents; the abstract names neither the migration tools nor the agents. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Constantin Herbst, Hannes Keil and Sandy Tran, written here from SAP's abstract; the recording is not yet published.
AI-enhanced analytics with SAP Business AI PlatformAI-enhanced analytics with SAP Business AI PlatformSAP Business Data Cloud data products joined to an ontology, a semantic layer the abstract says is supported by the Model Context Protocol, and read from Joule Work (SAP Joule) and SAP Analytics Cloud: that is the architecture SAP's abstract states for its AI-enabled analytics. The abstract names the parts and not how the semantic layer is modelled or what an analytics developer builds against. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Hacen Benkhedah, Joaquin Recio Huertas, Matilda Hyka and Oliver Huth, written here from SAP's abstract; the recording is not yet published.
Architecting your data foundation for the Autonomous EnterpriseArchitecting your data foundation for the Autonomous EnterpriseBuilding a data foundation for what SAP calls the Autonomous Enterprise on SAP Business Data Cloud: a business data fabric that unifies trusted data, business context and governed data products across an organisation's systems, so that Joule Agents in SAP Joule have the context their answers rest on. The abstract states Business Data Cloud's role and no product, data product or integration beneath it. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Mudit Mittal and Prakash Nanduri, written here from SAP's abstract; the recording is not yet published.
Build agentic SAP extensions with Google CloudBuild agentic SAP extensions with Google CloudGoogle Cloud's architectures for agentic AI apps on SAP with a clean core: SAP Business AI Platform, SAP S/4HANA and SAP Business Data Cloud joined with BigQuery, Gemini and Google's Agent Development Kit. The abstract names zero-copy data agents, developer API workflows and ABAP extensions for AI integration, the last of which is the Extensibility concern; Business Data Cloud is the one atlas entry it names, and how any piece connects to another is not stated. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Ekaterina Kruse, Ian Moria and Stany Winkler, written here from SAP's abstract; the recording is not yet published.
Build AI agents on SAP and non-SAP data and context in an open lakehouseBuild AI agents on SAP and non-SAP data and context in an open lakehouseSAP Business Data Cloud as an open lakehouse: the abstract states that it holds third-party data beside SAP data and its semantic context on Apache Iceberg and Apache Polaris, that architects and developers publish governed data products and ontologies from it, which is the cataloguing and lineage concern, and that agents built on them for SAP Joule can be reached from outside over A2A. How a data product is defined, and what the ontology layer adds to it, is not in the abstract. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Rahim Bhojani and Sivakumar N, written here from SAP's abstract; the recording is not yet published.
Build and govern data products in the Joule Studio solutionBuild and govern data products in the Joule Studio solutionData products in SAP Business Data Cloud are built and deployed from Joule Studio, in SAP's abstract, with no-code, AI-assisted tools that combine SAP and third-party data, and are then governed, catalogued and shared for analytics and AI, the cataloguing and lineage concern on the product side. The abstract names the steps and not what a data product built this way contains or how Joule Studio reaches the data. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Arne Ludwig, Aruna Kashinath, Rene Kirchhoff and Sandy Tran, written here from SAP's abstract; the recording is not yet published.
Build real-time, unified data products for agentic AI with SAP ReltioBuild real-time, unified data products for agentic AI with SAP ReltioSAP Reltio inside SAP Business Data Cloud unifies master data across SAP and third-party systems and complements SAP Master Data Governance, in SAP's abstract, with Customer 360 as the named scenario and real-time data products for applications, analytics and AI as the output. The abstract states Reltio's place in the product and nothing about how the unification or the data products work. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Arne Ludwig, Rene Kirchhoff, Richard Mooney and Sriraj Rajaram, written here from SAP's abstract; the recording is not yet published.
Building custom Joule Agents with SAP Knowledge GraphBuilding custom Joule Agents with SAP Knowledge GraphCustom Joule Agents built in a hands-on with SAP Knowledge Graph and Joule Studio: discovering APIs from a user's intent, grounding an agent's answers in business context, and integrating data products for SAP Business Data Cloud. The agents are SAP Joule's; the Knowledge Graph is not an atlas entry, and the abstract does not say which Joule Studio edition the exercises use. A TechEd 2026 hands-on session (Wednesday 28 October, Berlin), presented by Jan Portisch, Lucas Kiesow, Pavithra G K and Rafael Martin, written here from SAP's abstract; no recording is announced.
Data Product Governance in SAP Business Data CloudData Product Governance in SAP Business Data CloudA Devtoberfest 2026 data-track session (DAT-41) on data product governance in SAP Business Data Cloud, released as an MVP: every data product belongs to a domain, a consumer puts one in a cart and raises an access request with a purpose, a target location and acknowledged terms and conditions, and a data steward's approval becomes an access agreement recording who uses which data product where. The demo runs both sides in the BDC cockpit — domains, access management and data product management for the steward, catalog, cart and request for the consumer, with Databricks as the target workspace — and that is what the Cataloguing and lineage edge rests on. It also states the current limits: one default domain until custom domains and subdomains arrive, auto-approved requests for space copies, transports and intelligent content, stewards who cannot yet revoke an active agreement, and an install step still done by hand after approval.SAP Developers
Enhancing agentic AI with trusted business dataEnhancing agentic AI with trusted business dataHow SAP Business Data Cloud gives AI applications and agents governed access to business data and its context, and how zero-copy architectures let AI read enterprise data with less of it moved, which is the data federation concern. The abstract names the product and the architecture and states no mechanism or product the zero-copy path runs over. A TechEd 2026 session (Tuesday 27 October, Berlin; also DA100V, virtual), presented by Aruna Kashinath, Hannes Keil and San Tran, written here from SAP's abstract; the recording is not yet published.
Enriching AI models with SAP AI Core in SAP Business Data CloudEnriching AI models with SAP AI Core in SAP Business Data CloudThe integration between SAP Business Data Cloud and SAP AI Core, through which data products with their semantics feed AI models for benchmarking, custom AI development and agentic analytics and applications. The abstract names the two products and the three uses and states nothing about how the data reaches AI Core or what the integration consists of. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by San Tran and Sandy Tran, written here from SAP's abstract; the recording is not yet published.
Joule and SAP Knowledge Graph: Context-aware AI across SAP solutionsJoule and SAP Knowledge Graph: Context-aware AI across SAP solutionsThe catalogue carries RA107's abstract under this title, and what it describes is SAP AI Core as the management and orchestration layer of an AI architecture: extending Joule Agents in SAP Joule, the model library in Joule Studio for contextual reasoning, connecting data products from SAP Business Data Cloud, and embedding AI in SAP S/4HANA. The SAP Knowledge Graph the title names does not appear in that text, so the edges rest on the abstract as published, and whether the session follows the title or the abstract is not known until it is recorded. A TechEd 2026 session (Wednesday 28 October, Berlin; also RA110V, virtual), presented by Jan Portisch and Pavithra G K, written here from SAP's abstract; the recording is not yet published.
Reimagining master data as the foundation for enterprise AIReimagining master data as the foundation for enterprise AIMaster data as the foundation for AI: SAP Business Data Cloud unifying, governing and delivering master data as reusable data products, the cataloguing and lineage concern, for analytics, autonomous applications and AI that share one business context. The abstract names no component of Business Data Cloud and no master data product, and Reltio, the co-presenter's company, is not named in it. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Arne Ludwig and Sriraj Rajaram, written here from SAP's abstract; the recording is not yet published.
Road map for SAP HANA Cloud: What’s next for the AI databaseRoad map for SAP HANA Cloud: What’s next for the AI databaseSAP's road map for SAP HANA Cloud, which the abstract places as a core component of SAP Business AI Platform within SAP Business Data Cloud. The abstract promises a firsthand look at what is next for the database and names no feature, release or date, so the two edges rest on the products it names and nothing more. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Niklas Siemer and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
SAP HANA Cloud as the AI database for SAP Business Data CloudSAP HANA Cloud as the AI database for SAP Business Data CloudSAP HANA Cloud as the AI database at the centre of SAP Business Data Cloud: the strategy behind its AI-first direction, and a Carl Zeiss speaker on how it runs in production today. The abstract names the two products and the customer and no feature, workload or figure, so the edges rest on the products as its subject. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Kush Kashyap and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Share data products with zero copies using SAP Business Data Cloud ConnectShare data products with zero copies using SAP Business Data Cloud ConnectSAP Business Data Cloud Connect shares data products between SAP Business Data Cloud and third-party platforms with zero-copy access, in both directions and for metadata as well as data, in SAP's abstract, with the business context kept on the way. The abstract names the product and its two properties, bidirectional and zero-copy, and does not say which third-party platforms or which protocol carry the sharing. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Babacar Toure, Hannes Keil, Maria Uruena and Neetha Pucknat, written here from SAP's abstract; the recording is not yet published.
The future of the SAP Business Data Cloud solution: Data and AI road mapThe future of the SAP Business Data Cloud solution: Data and AI road mapThe road map for SAP Business Data Cloud as the data foundation of SAP Business AI Platform: upcoming work in data products, AI models and agentic analytics, framed as activating business context and running autonomous processes. A road map session, so nothing in it ships yet, and the abstract names the three areas and no item within them; a Fresenius speaker co-presents. A TechEd 2026 session (Tuesday 27 October, Berlin; also DA802V, virtual), presented by Constantin Herbst and Prakash Nanduri, written here from SAP's abstract; the recording is not yet published.
The next evolution of SAP Business Data Cloud with Dremio and SAP ReltioThe next evolution of SAP Business Data Cloud with Dremio and SAP ReltioSAP Business Data Cloud's data fabric with two additions SAP's abstract names, Dremio and SAP Reltio: data products, open catalogues, unified master data and business semantics as the pieces of a data foundation for analytics and agentic AI. The abstract names the two products and not what each takes over in the fabric. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Florian Schmidt, Rene Kirchhoff and Sriraj Rajaram, written here from SAP's abstract; the recording is not yet published.
Transform business data into AI-supported insightsTransform business data into AI-supported insightsBusiness metrics turned into insights, narratives and recommendations by SAP Joule's agents, with People Intelligence, the SAP Snowflake solution extension for SAP Business Data Cloud and SAP Analytics Cloud connected in SAP Business Data Cloud. The abstract names what is connected and what comes out, and does not say what the exercise builds or where the Snowflake extension sits in it; People Intelligence and the extension are not atlas entries. A TechEd 2026 hands-on session (Wednesday 28 October, Berlin), presented by Babacar Toure, Maximilian Paul Gander, Neetha Pucknat and San Tran, written here from SAP's abstract; the recording is not yet published.
What’s next for agentic analytics and planning in SAP Business Data CloudWhat’s next for agentic analytics and planning in SAP Business Data CloudWhat is next for agentic analytics and planning in SAP Business Data Cloud: SAP Joule's agents automating insight delivery, analysis, narrative generation and modelling tasks. A road map session; the abstract names the four kinds of task and no agent or date. A TechEd 2026 session (Tuesday 27 October, Berlin; also DA806V, virtual), presented by Kristian Ruemmelin, written here from SAP's abstract; the recording is not yet published.
Enhancing agentic AI with trusted business dataEnhancing agentic AI with trusted business dataHow SAP Business Data Cloud gives AI applications and agents governed access to business data and its context, and how zero-copy architectures let AI read enterprise data with less of it moved, which is the data federation concern. The abstract names the product and the architecture and states no mechanism or product the zero-copy path runs over. A TechEd 2026 session (Tuesday 27 October, Berlin; also DA100V, virtual), presented by Aruna Kashinath, Hannes Keil and San Tran, written here from SAP's abstract; the recording is not yet published.
The Full Picture: Federating, Replicating, and Caching Remote Data in CAP — Part 1The Full Picture: Federating, Replicating, and Caching Remote Data in CAP — Part 1Part one of two, and the concepts half: terminology, what CAP already does, and what a project still has to write. It fixes a vocabulary — federation is the umbrella for making remote data part of your model, delegation forwards each request to the remote at query time, replication is a scheduled copy into the local database, and caching is an option on either rather than a third strategy — which collides with CAP's own, since what this series calls replication is what the CAP-level Data Federation guide calls federation. From the ground the Service Integration guide covers, it lists what is left to hand-write: cross-service $expand, navigation filters across the boundary, write forwarding, timeouts and retries, then scheduling, delta detection, backoff, a guard for two app instances sharing one schedule, idempotent writes, and knowing whether the last run succeeded. That list is why the edge to cds-data-federation holds: the second half maps it onto @federation.delegate and @federation.replicate and says which package carries which part — annotations in cds-data-federation, the scheduled engine in cds-data-pipeline, response caching through cds-caching — so it is the design rationale for the annotations, not the API reference. The gap list and the plugins answering it come from the same person who maintains them; cap-replication-demo and SAP's xtravels sample are cited as the hand-written form of the same work. Part 2 is announced as a walkthrough of a demo application and is not covered here.
Under the hood of the Delta Sharing protocolUnder the hood of the Delta Sharing protocolHow a Delta Sharing server, dsserv, implements the open-source Delta Sharing protocol for zero-copy sharing: Delta tables ingested, governed and streamed live from local S3-compatible storage into cloud consumers such as Databricks. Neither the server nor the protocol is an atlas entry; the edges are to data federation, the concern zero-copy sharing answers, and to object storage, where the tables sit, and the abstract names no SAP product. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Philipp Nell, written here from SAP's abstract; the recording is not yet published.
Analyze and optimize ABAP Cloud model apps built on core data servicesAnalyze and optimize ABAP Cloud model apps built on core data servicesPerformance anti-patterns in data models built with ABAP CDS under ABAP Cloud applications, and the tools that find them: the abstract names ABAP cross trace, the SQL analyzer (PlanViz) in Visual Studio Code and the SAP HANA database explorer as what the session uses to follow a bottleneck from the application to the database. The anti-patterns are taken from real business applications, which is where the data modelling edge holds; which ones are shown is not in the abstract. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Ingo Braeuninger, Robert Werlich, Steffen Siegmund and Thomas Fiedler, written here from SAP's abstract; the recording is not yet published.
Creating structured types, enums and named aspects using the CDS Graphical ModelerCreating structured types, enums and named aspects using the CDS Graphical ModelerThe modelling constructs that are awkward to draw — structured types, enums, named aspects — done in the graphical modeller rather than in CDL.Xiao-fei_Song, published 2021-05-26
Explore the declarative power of status-transition flowsExplore the declarative power of status-transition flowsModelling which state a record may move to next, declaratively, rather than scattering the rule across the handlers that move it.SAP
Importing @sap/cds common.cds to your CAP project using the CDS Graphical ModelerImporting @sap/cds common.cds to your CAP project using the CDS Graphical ModelerPulling the common aspects and code lists from @sap/cds into a model through the modeller, so the reuse types appear in the diagram rather than only in the file.Xiao-fei_Song, published 2021-05-06
Learn how to define constraints declaratively in your CDS modelLearn how to define constraints declaratively in your CDS modelConstraints expressed in the model rather than checked in a handler — the distinction the atlas draws between input validation the framework enforces and validation somebody writes.SAP
SAP CAP Adapter to expose OData as custom REST ServiceSAP CAP Adapter to expose OData as custom REST ServiceWalks through consuming an external OData V2 service in a CAP project — cds import converts the Northwind service's EDMX into a CSN and cds.connect.to wires up the remote proxy — then re-exposing it as plain REST with the @protocol:'rest' annotation instead of OData's own envelope. Shows two ways to reshape the response, renaming and dropping fields in a CDS projection or rewriting the returned objects in a custom service handler, and remaps ?pageSize and ?offset onto OData's $top and $skip in that same handler.Former Member, published 2021-09-15
Agentic memory: Knowledge graph engine and recall in SAP HANA CloudAgentic memory: Knowledge graph engine and recall in SAP HANA CloudThe knowledge graph engine in SAP HANA Cloud as an agent's memory: what the engine, the graph side of graph, spatial and document data, does in an agentic workflow, how an agent keeps what it learns there, the state agent tooling holds between turns, and the SAP AI Core integration through which the database calls LLMs and tabular foundation models such as SAP-RPT-1. The abstract names the engine, the memory role and the AI Core link and does not say how the memory is written or read. DA263 on Wednesday 28 October is a two-hour hands-on on the same engine with a different cast. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Markus Fath and Shraddha Shetty, written here from SAP's abstract; the recording is not yet published.
Analyze and optimize ABAP Cloud model apps built on core data servicesAnalyze and optimize ABAP Cloud model apps built on core data servicesPerformance anti-patterns in data models built with ABAP CDS under ABAP Cloud applications, and the tools that find them: the abstract names ABAP cross trace, the SQL analyzer (PlanViz) in Visual Studio Code and the SAP HANA database explorer as what the session uses to follow a bottleneck from the application to the database. The anti-patterns are taken from real business applications, which is where the data modelling edge holds; which ones are shown is not in the abstract. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Ingo Braeuninger, Robert Werlich, Steffen Siegmund and Thomas Fiedler, written here from SAP's abstract; the recording is not yet published.
Build AI agents and applications natively on SAP HANA CloudBuild AI agents and applications natively on SAP HANA CloudSAP HANA Cloud as the base for agentic applications on SAP Business AI Platform: the session goes through CAP, the SAP HANA Cloud vector engine and SAP AI Core, with architecture patterns and lessons learned from building AI-supported applications. SAP's abstract names the three parts of that stack and the vector engine as the database's feature in it, and describes none of the patterns. A TechEd 2026 session (Wednesday 28 October, Berlin; also DA113V, virtual), presented by Christoph Morgen and Shraddha Shetty, written here from SAP's abstract; the recording is not yet published.
Build your custom knowledge layer with SAP HANA CloudBuild your custom knowledge layer with SAP HANA CloudSAP HANA Cloud's multi-model engines, the knowledge graph engine among them, as the base for a custom knowledge layer over the enterprise's data, with SAP Business Data Cloud as the product that includes the database. Graph, spatial and document data put to an AI use; the abstract names the graph engine and not the query language or the vector side. A TechEd 2026 session (Wednesday 28 October, Berlin; also ST108V, virtual), presented by Niklas Siemer and Sebastian Schmidt, written here from SAP's abstract; the recording is not yet published.
Building agentic applications with SAP HANA CloudBuilding agentic applications with SAP HANA CloudA CAP application extended with semantic search and agent features on top of SAP HANA Cloud's vector engine and SAP AI Core, built in a hands-on workshop. The abstract names the parts, CAP, the vector engine and AI Core, and the two additions: semantic search, the retrieval half of retrieval-augmented generation, and LLM-supported agent capabilities inside the application, which is agent tooling. Which embedding model, which CAP runtime and how the agent calls its tools are not stated. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Hui Li, Ritty Joseph, Shraddha Shetty and Tanuja Jadhav, written here from SAP's abstract; the recording is not yet published.
cds deploy to sqlite in-memory databasecds deploy to sqlite in-memory databaseTwo pages on what cds deploy --to sqlite does and what it prints. A reference note rather than an argument, and the shortest entry in this capability.AndrewBarnard, published 2021-04-25
Create a Business Service using CDS, Node.js, SQLite and a OData serviceCreate a Business Service using CDS, Node.js, SQLite and a OData serviceThe stack at its smallest — CDS, Node, SQLite, an OData service — worked through at length. Nothing cloud in it, which is the point: it is the shape of a CAP service before any of the platform arrives.raybanton, published 2021-07-12
Create a Business Service using CDS, Node.js, SQLite and a OData service
Database connectors has changed twice since this was published, most recently in @sap/cds 10.1.0, 2026-09-07. Affects A recipe, Runnable code.
Deploying CAP services with the in-memory SQLite databaseDeploying CAP services with the in-memory SQLite databaseIn-memory, deployed to Cloud Foundry — so the data is gone on restart, on purpose. For a service whose datastore does not need to survive anything, which is a narrower case than it sounds and a real one.liam_mischewski, published 2021-05-24
Flexible analytics at speed: Calculation views in SAP Business Data CloudFlexible analytics at speed: Calculation views in SAP Business Data CloudCalculation views in SAP HANA Cloud, the reusable analytic modelling blocks built in its graphical modelling environment, with three newer parts the abstract names: AI-assisted modelling, refactoring, and advanced materialisation, and the way the views sit inside SAP Business Data Cloud. What a materialisation is chosen on, and what the AI assistance generates, is not stated. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Hansika Colomboge Dona, Jan Zwickel, Niklas Siemer and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Get hands on with tabular AI and forecasting models in SAP HANA CloudGet hands on with tabular AI and forecasting models in SAP HANA CloudPrediction and forecasting as predictive models inside SAP HANA Cloud, worked through hands-on: tabular AI models such as SAP-RPT-1 for on-the-fly predictions, time series foundation models for ad hoc forecasts, and the AutoML in the predictive analysis library. The abstract names the model kinds and the library and does not say how a model is called from SQL or from an application. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christoph Morgen, Markus Fath, Ritty Joseph and Tanuja Jadhav, written here from SAP's abstract; the recording is not yet published.
How to share tables across different CAP projectsHow to share tables across different CAP projectsFour scenarios for reading one CAP project's table from another, worked out as a matrix of what each one needs: the same HDI container and namespace, the same container under a different namespace, a different container, and a different Cloud Foundry space. The mechanics change with each step — @cds.persistence.exists on the reused entity throughout, an .hdbsynonym once namespaces diverge, .hdbrole and .hdbgrants once containers do, and a user-provided service built from the grantor's credentials once spaces do. The author flags the caveat themselves: these are learning scenarios, and consuming the OData service is often the better answer than reaching into another project's database. Dated corrections from twelve days after publication sit inline — synonyms need only the name because the configuration comes from .hdbsynonymconfig, SERVICE_REPLACEMENTS in mta.yaml removes the hard-coded service name, and the reused container need not be bound to the srv module.published 2021-10-03
Knowledge graph and agent memory: Build an agent that remembersKnowledge graph and agent memory: Build an agent that remembersThe knowledge graph engine in SAP HANA Cloud as an agent's memory: what the engine, the graph side of graph, spatial and document data, does in an agentic workflow, how an agent keeps what it learns there, the state agent tooling holds between turns, and the SAP AI Core integration through which the database calls LLMs and tabular foundation models such as SAP-RPT-1. The abstract names the engine, the memory role and the AI Core link and does not say how the memory is written or read. DA205 on Tuesday 27 October is the 45-minute talk on the same engine, with a near-identical abstract and a different cast. A two-hour hands-on TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christoph Morgen, Jan Zwickel, Markus Fath and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Modernize your database landscape with SAP HANA CloudModernize your database landscape with SAP HANA CloudWhy to move a database estate to SAP HANA Cloud, and the migration tools and services for doing so, which is the platform migration concern. SAP's abstract is one sentence and names none of the tools. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Niklas Siemer and Roman Belosludtsev, written here from SAP's abstract; the recording is not yet published.
My Learnings in SAP-CAP Using MongoDBMy Learnings in SAP-CAP Using MongoDBCAP against a document database, which the supported list does not include — and the way it gets there is the point. The CDS model and the service definitions stay; the persistence is replaced by handlers calling the mongodb driver, with @sap/cds-odata-v2-adapter-proxy in front so a UI5 application can consume it. Learnings rather than a supported path, and the only MongoDB entry here.published 2021-01-11
My Learnings in SAP-CAP Using MongoDB
sap-cap-mongodb: no published releases, repository quiet for 69 months. Affects Runnable code.
Road map for SAP HANA Cloud: What’s next for the AI databaseRoad map for SAP HANA Cloud: What’s next for the AI databaseSAP's road map for SAP HANA Cloud, which the abstract places as a core component of SAP Business AI Platform within SAP Business Data Cloud. The abstract promises a firsthand look at what is next for the database and names no feature, release or date, so the two edges rest on the products it names and nothing more. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Niklas Siemer and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
SAP BTP Showcase - Use SAP Cloud Application Programming to do Native Development on HANA CloudSAP BTP Showcase - Use SAP Cloud Application Programming to do Native Development on HANA CloudThe fifth of a seven-post showcase, and the one where CAP is used to reach HANA-native artifacts rather than to avoid them. Long, and it sits inside a series whose other parts are about Data Warehouse Cloud and CI/CD.former_member271284, published 2021-01-31
SAP BTP Showcase - Use SAP Cloud Application Programming to do Native Development on HANA Cloud
Database connectors has changed twice since this was published, most recently in @sap/cds 10.1.0, 2026-09-07. Affects A recipe, Runnable code.
SAP BTP Showcase – SAP Continuous Integration and Delivery (SAP CI&D) for SAP HANA CloudSAP BTP Showcase – SAP Continuous Integration and Delivery (SAP CI&D) for SAP HANA CloudA pipeline for the database half of a CAP application, built with cds add pipeline — one command that writes a Jenkinsfile and a .pipeline folder — and then the mta module that makes a database change deploy itself.gcalixtos, published 2021-01-27
SAP BTP Showcase – SAP Continuous Integration and Delivery (SAP CI&D) for SAP HANA Cloud
Database connectors has changed twice since this was published, most recently in @sap/cds 10.1.0, 2026-09-07. Affects Runnable code.
SAP Business Application Studio – Using SAP HANA Tools with SAP Cloud Application Programming Model projectsSAP Business Application Studio – Using SAP HANA Tools with SAP Cloud Application Programming Model projectsNative HDB artifacts inside a CAP project — the case where CDS is not enough and something has to be written as a database object. Most of the work is making the two build outputs agree: an HDB module path, a CDS build output directory, and a package.json for the module, before the artifact is added and deployed. Published under an account that has since been closed, so the byline reads Former Member.Former Member, published 2021-01-14
SAP Business Application Studio – Using SAP HANA Tools with SAP Cloud Application Programming Model projects
Database connectors has changed twice since this was published, most recently in @sap/cds 10.1.0, 2026-09-07. Affects A recipe, Runnable code.
SAP HANA Cloud advisors and automationSAP HANA Cloud advisors and automationSAP HANA Cloud's advisors and the tasks they hand off: the elastic compute node, the partition, index and native storage extension advisors that produce recommendations for performance and efficiency, and SAP Automation Pilot as the service that runs the resulting tasks, which is operations automation on the database. The abstract lists the features and stops there, so what each advisor recommends and how a recommendation becomes an Automation Pilot command is not stated. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Hansika Colomboge Dona, Hui Li, Javier Martin and Roman Belosludtsev, written here from SAP's abstract; the recording is not yet published.
SAP HANA Cloud as a self-managed database: Advisors and automationSAP HANA Cloud as a self-managed database: Advisors and automationSAP HANA Cloud advisors generate recommendations for database administration, aimed at system performance and efficiency, and SAP Automation Pilot runs the manual and administrative tasks around the database as automated processes, which is the operations automation concern on the database side. SAP's abstract names the two services and claims reduced operational overhead; which advisors exist and which tasks the automation covers are not said. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Javier Martin and Roman Belosludtsev, written here from SAP's abstract; the recording is not yet published.
SAP HANA Cloud as the AI database for SAP Business Data CloudSAP HANA Cloud as the AI database for SAP Business Data CloudSAP HANA Cloud as the AI database at the centre of SAP Business Data Cloud: the strategy behind its AI-first direction, and a Carl Zeiss speaker on how it runs in production today. The abstract names the two products and the customer and no feature, workload or figure, so the edges rest on the products as its subject. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Kush Kashyap and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
SAP HANA Cloud: The data foundation for contextualizing and reasoningSAP HANA Cloud: The data foundation for contextualizing and reasoningWhere SAP is taking SAP HANA Cloud, described in the abstract as the anchor of SAP's data strategy and a unified layer to store, contextualise and reason across a business, and what that means for a data platform decision. The abstract is a direction statement and names no feature, release or date, so the one edge rests on the product itself. A TechEd 2026 session (Tuesday 27 October, Berlin; also ST106V, virtual), presented by Matthew Zenus and Stefan Baeuerle, written here from SAP's abstract; the recording is not yet published.
SAP HANA Data Lake Files (HDLF) Basic FunctionalitiesSAP HANA Data Lake Files (HDLF) Basic FunctionalitiesA Devtoberfest 2026 data-track session on SAP HANA data lake files, the managed object store inside SAP HANA Cloud that is provisioned from HANA Cloud Central, priced per API call and per stored gigabyte, and reached with the same REST API whichever hyperscaler it runs on. Two product managers set up an instance with its IP allow list, client certificates and trusts, then upload, list, read and delete files over the WebHDFS REST API and the HDLFS command-line utility — the Object storage half — and cover file versions with snapshots, access policies per file, and chunked upload with a server-side merge. They then link the instance to a HANA database and query a CSV file as a virtual table: SQL on files now runs through a file adapter in the database, while the older path through the data lake relational engine is deprecated.SAP Developers
Simple steps to deploy your CAP project to HANA cloudSimple steps to deploy your CAP project to HANA cloudWritten from the far side of a failed deploy — the author says these are the troubleshooting steps that ended up working, not a designed procedure. Short, and honest about being a record rather than a recommendation.OmPrakashJha, published 2021-02-11
Stop guessing, start counting: How generative AI fixed an inventory messStop guessing, start counting: How generative AI fixed an inventory messAn inventory-counting application a business built on SAP Business AI Platform with SAP HANA Cloud and generative AI, to replace recount lists kept by gut feel across SAP ERP Central Component, SAP S/4HANA and Microsoft Excel; the talk promises the app, its architecture and what went wrong. The generative AI part is LLM integration from application code; the abstract names HANA Cloud and generative AI as the two building blocks and says nothing about how either is used. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Ranjeet Kumar, written here from SAP's abstract; the recording is not yet published.
Talking to your database: The new data exploration tools in SAP HANA CloudTalking to your database: The new data exploration tools in SAP HANA CloudSAP HANA Cloud's new data exploration tools take a business question in plain language, find the right database objects and run the query, with no data model and no SQL written beforehand, shown live on a standard business data set. SAP's abstract says the session covers how the tools work, what they need and where they hit their limits, and states none of the three. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Anna Hartmann, written here from SAP's abstract; the recording is not yet published.
Unlock predictive and generative AI with your data in SAP HANA CloudUnlock predictive and generative AI with your data in SAP HANA CloudThe AI features SAP lists for SAP HANA Cloud: the function libraries for tabular prediction and forecasting, where predictive models run inside the database, the vector engine, the knowledge graph engine, which is graph, spatial and document data put to generative use, and SQL to LLM calls. The abstract names the engines and the use cases they are meant for and gives no detail of any one of them. A TechEd 2026 session (Tuesday 27 October, Berlin; also DA204V, virtual), presented by Christoph Morgen and Javier Martin, written here from SAP's abstract; the recording is not yet published.
Use SAP HANA as the Database for a CAP Java ApplicationUse SAP HANA as the Database for a CAP Java ApplicationMoving a CAP Java application off the development database onto HANA, which is the step where the persistence choice stops being invisible.SAP
Agentic memory: Knowledge graph engine and recall in SAP HANA CloudAgentic memory: Knowledge graph engine and recall in SAP HANA CloudThe knowledge graph engine in SAP HANA Cloud as an agent's memory: what the engine, the graph side of graph, spatial and document data, does in an agentic workflow, how an agent keeps what it learns there, the state agent tooling holds between turns, and the SAP AI Core integration through which the database calls LLMs and tabular foundation models such as SAP-RPT-1. The abstract names the engine, the memory role and the AI Core link and does not say how the memory is written or read. DA263 on Wednesday 28 October is a two-hour hands-on on the same engine with a different cast. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Markus Fath and Shraddha Shetty, written here from SAP's abstract; the recording is not yet published.
Build your custom knowledge layer with SAP HANA CloudBuild your custom knowledge layer with SAP HANA CloudSAP HANA Cloud's multi-model engines, the knowledge graph engine among them, as the base for a custom knowledge layer over the enterprise's data, with SAP Business Data Cloud as the product that includes the database. Graph, spatial and document data put to an AI use; the abstract names the graph engine and not the query language or the vector side. A TechEd 2026 session (Wednesday 28 October, Berlin; also ST108V, virtual), presented by Niklas Siemer and Sebastian Schmidt, written here from SAP's abstract; the recording is not yet published.
Knowledge graph and agent memory: Build an agent that remembersKnowledge graph and agent memory: Build an agent that remembersThe knowledge graph engine in SAP HANA Cloud as an agent's memory: what the engine, the graph side of graph, spatial and document data, does in an agentic workflow, how an agent keeps what it learns there, the state agent tooling holds between turns, and the SAP AI Core integration through which the database calls LLMs and tabular foundation models such as SAP-RPT-1. The abstract names the engine, the memory role and the AI Core link and does not say how the memory is written or read. DA205 on Tuesday 27 October is the 45-minute talk on the same engine, with a near-identical abstract and a different cast. A two-hour hands-on TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christoph Morgen, Jan Zwickel, Markus Fath and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Unlock predictive and generative AI with your data in SAP HANA CloudUnlock predictive and generative AI with your data in SAP HANA CloudThe AI features SAP lists for SAP HANA Cloud: the function libraries for tabular prediction and forecasting, where predictive models run inside the database, the vector engine, the knowledge graph engine, which is graph, spatial and document data put to generative use, and SQL to LLM calls. The abstract names the engines and the use cases they are meant for and gives no detail of any one of them. A TechEd 2026 session (Tuesday 27 October, Berlin; also DA204V, virtual), presented by Christoph Morgen and Javier Martin, written here from SAP's abstract; the recording is not yet published.
How to share tables across different CAP projectsHow to share tables across different CAP projectsFour scenarios for reading one CAP project's table from another, worked out as a matrix of what each one needs: the same HDI container and namespace, the same container under a different namespace, a different container, and a different Cloud Foundry space. The mechanics change with each step — @cds.persistence.exists on the reused entity throughout, an .hdbsynonym once namespaces diverge, .hdbrole and .hdbgrants once containers do, and a user-provided service built from the grantor's credentials once spaces do. The author flags the caveat themselves: these are learning scenarios, and consuming the OData service is often the better answer than reaching into another project's database. Dated corrections from twelve days after publication sit inline — synonyms need only the name because the configuration comes from .hdbsynonymconfig, SERVICE_REPLACEMENTS in mta.yaml removes the hard-coded service name, and the reused container need not be bound to the srv module.published 2021-10-03
SAP HANA Data Lake Files (HDLF) Basic FunctionalitiesSAP HANA Data Lake Files (HDLF) Basic FunctionalitiesA Devtoberfest 2026 data-track session on SAP HANA data lake files, the managed object store inside SAP HANA Cloud that is provisioned from HANA Cloud Central, priced per API call and per stored gigabyte, and reached with the same REST API whichever hyperscaler it runs on. Two product managers set up an instance with its IP allow list, client certificates and trusts, then upload, list, read and delete files over the WebHDFS REST API and the HDLFS command-line utility — the Object storage half — and cover file versions with snapshots, access policies per file, and chunked upload with a server-side merge. They then link the instance to a HANA database and query a CSV file as a virtual table: SQL on files now runs through a file adapter in the database, while the older path through the data lake relational engine is deprecated.SAP Developers
Under the hood of the Delta Sharing protocolUnder the hood of the Delta Sharing protocolHow a Delta Sharing server, dsserv, implements the open-source Delta Sharing protocol for zero-copy sharing: Delta tables ingested, governed and streamed live from local S3-compatible storage into cloud consumers such as Databricks. Neither the server nor the protocol is an atlas entry; the edges are to data federation, the concern zero-copy sharing answers, and to object storage, where the tables sit, and the abstract names no SAP product. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Philipp Nell, written here from SAP's abstract; the recording is not yet published.
Improved Search Performance in SAP BTP Core, SAP Cloud Application Programming Model, Node.js RuntimeImproved Search Performance in SAP BTP Core, SAP Cloud Application Programming Model, Node.js RuntimeExplains CDS version 5's @cds.search annotation, which lets a service declare which elements a search query covers, and states that only String-typed elements are searched by default so the generated SQL stays simple. Covers two further changes to @sap/cds's Node.js runtime: metadata about which elements are searchable is now cached, and search over localized text can resolve at query time instead of through a compiled view, avoiding a coalesce function that blocks filter push-down on SAP HANA. Also describes an experimental toggle, cds.env.features.optimized_search, that lets the runtime choose the CONTAINS predicate over LIKE for large data sets.published 2021-08-18
SAP Cloud Application Programming Model（CAP）：カスタムロジックの実装 – Part 2SAP Cloud Application Programming Model（CAP）：カスタムロジックの実装 – Part 2A walkthrough of implementing custom logic in a CAP Node.js service for the capire bookshop sample, registering before, on and after handlers around the generated CRUD service for its Books and Orders entities. The before handler validates an incoming order's amount and runs a transactional CQN UPDATE to decrement stock, raising a 400 or 409 through req.error when the amount is invalid or no rows match; an after handler on READ appends a discount note to overstocked books.rasai, published 2021-01-01
Explore SAP Fiori elements for ABAP developersExplore SAP Fiori elements for ABAP developersSAP Fiori elements for an ABAP developer who now builds the UI as well as the RAP business object behind it, the transactional model of ABAP Cloud, in SAP Business Application Studio; the abstract calls the UI side new territory for that developer, and a Fiori elements app is rendered from the service's Fiori annotations rather than hand-written UI code. The abstract names the tool and the stack and does not say which floorplan or annotations are shown. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Björn Schulz, written here from SAP's abstract; the recording is not yet published.
Level up app development: ABAP Cloud model innovations and Joule solutionLevel up app development: ABAP Cloud model innovations and Joule solutionThe ABAP RESTful application programming model, in SAP's abstract, generates multi-node business objects and adds event-driven side effects, collaborative draft, background processing and analytical tables, with the SAP Joule for Developers, ABAP AI capabilities add-on used to speed development up. The generated business objects are the transactional model concern; the abstract names each feature and says nothing about how it is used. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Anne Keller, Carine Tchoutouo Djomo, Frank Jentsch and Marina Blank, written here from SAP's abstract; the recording is not yet published.
