Product Information
SAP Datasphere News in Q2
What’s New in Q2 2023 🚀
After the launch of SAP Datasphere🔥in March 2023 we introduced and enhanced lots of great features in Q2 2023.
We introduced the parallel child tasks in task chains, enabling change remote table sources and definitions, import entities from SAP S/4HANA on-prem systems, enhanced the Analytic Model with Currency conversion after aggregation, introduction of new semantic type Fact, content network support for Business Builder objects, CLI updates and task framework support, and many more.
Interested to know more?
We will also run our quarterly What’s New in Q2 2023 session on July 26th, 2023 in case you are interested in more details. Join us for the update session and ask the product experts in the Q&A section live during the session.
👉 Sign up now or watch the recording afterwards.
Some key features are briefly explained in Tanja Wingerter‘s video for Q2 2023
SAP BW bridge 🌉
- How to Plan and Provision an SAP BW Bridge System Landscape
- How to Transport in an SAP BW Bridge System Landscape
- SAP BW Bridge: Just a SAP BW/4HANA in SAP Datasphere?
- Add BW Bridge and/or Embedded Data Lake to SAP Datasphere Service Instance created from SAP BTP Platform
Data Modeling, Catalog & Consumption ✨
SAP Datasphere Analytic Model Series
- Part 4: Calculated and Restricted Measures
- Part 5: Exception Aggregation
- Part 6: Using Variables in Analytic Model
- Part 7: Time Dependency for Dimensions and Texts in Analytic Model
- Part 8: Data Preview
- Part 9: User Experience and Navigation Paradigm
- Part 10:Design Multi Fact in Analytic Model
SAP Datasphere Intelligent Lookup Series
- Part 3: How to integrate an Intelligent Lookup in your existing data landscape
- Part 4: What is a fuzzy match and why should I care?
- Part 5: SAP Datasphere Intelligent Lookup Series – Up for a (Data) Challenge?
Further Modeling, Catalog & Consumption Blogs
- ⚠️Why are Analytical Datasets deprecated and what does it mean for your project?
- Creating Compound Keys in Datasphere
- Repointing System : SAC Stories with Datasphere Models as Data Source
- Extending SAC Planning – Importing calculation results from SAP Datasphere and E2E Workflow
- Understanding Time Tables & Dimensions In An SAP Datasphere World
- Catalog Features in SAP Datasphere
- Trigger HANA stored procedure from SAP Analytics Cloud – Multi Action
- Extending SAC Planning – Acessing planning data with SAP Datasphere
- SAP Datasphere View generation with Python and the Command-Line Interface
SAP Datasphere and SAP SuccessFactors 💙
- Building Analytic Models for SAP SuccessFactors KPI
- Modeling SAP SuccessFactors Data in SAP Datasphere
- Bring SAP SuccessFactors Data into SAP Datasphere
- Connecting SAP SuccessFactors and SAP Datasphere
Blogs for our Japanese Community 🎌
- SAP Datasphere モデリングTips:スタースキーマ設計時のE/Rモデルの活用
- SAP Datasphere での階層定義とドリルアップ/ドリルダウン
- Free TierではじめるSAP Data Warehouse Cloud
- はじめての SAP Datasphere Part 4 : ストーリー作成
- はじめての SAP Datasphere Part 3 : モデリング
- はじめての SAP Datasphere Part 2 : テーブル作成
- はじめての SAP Datasphere Part 1 : スペース作成
- SACからSAP Datasphereへの接続設定
- SAP Datasphere インスタンスの作成方法 – Free Tier編 –
- SAP Datasphere インスタンスの作成方法 – BTP編 –
- SAP S/4HANA CloudとSAP Datasphereのデータ連携
ML/AI topics 💥
- Building GenAI powered Data-Driven Applications on SAP HANA Cloud/SAP Datasphere- E2E Scenario
- Extending SAC Planning – Creating custom calculations or ML
- SAP Datasphere: Seamless extraction of business insights in multi-cloud environments with HANA Machine Learning and FedML
- Using FedML library with SAP Datasphere and Databricks
- Federating queries to Databricks from SAP Datasphere for real-time analytics in SAP Analytics Cloud
- Deploying HANA ML with SAP Kyma Serverless Functions
More blogs to check out 👇
- Datasphere: Consume a Generic OData Source to Create a Fact (Analytical Dataset)
- FAQ & Troubleshooting Guide for @sap/datasphere-cli
- Powering Efficient Supply Chains: SAP Datasphere’s Integration with Google Cloud’s Architecture
- @sap/dwc-cli becomes @sap/datasphere-cli!
- SAP Datasphere: HANA System Memory and CPU – Overall Consumption and Breakdown
- SAP Datasphere’s Feature Highlight: The Analytic Model
- SAP Community Spotlight: SAP Datasphere
- What qualifies as Enterprise(!) Data Integration – Performance
- SAP Datasphere – Implementation Best Practices
- The Universe of SAP BTP in a Nutshell – with PricewaterhouseCoopers and SAP Innovation Awards Winner – ESG reporting and products with SAP
- Data Strategy with SAP – Data Performance for Decision Making
- Unlocking Advanced Analytics: Bringing SAP Datasphere Views to Databricks with SAP HANA Cloud JDBC
- Point of View on Scope 3 Sustainability Solution
- 5 Reasons to Register for the Plan to Win: Get Active with Data Hackathon
- SAP Datasphere & Partnerships – Confluent
- Ingesting Confluent/Kafka data into SAP Hana
- SAP Datasphere-SAP Cloud Connector Setup
- Extend SAP Datasphere with SAP Open Connectors
- Considerations for utilizing planning logic with SAC data actions & multi actions and SAP datasphere SQL views
- Data Strategy with SAP – Data Governance
- Unlock the Power of Business Data for SAP RISE Customers: Mastering Data Management and Cultivating Insights
- Creating a Google Big Query connection in SAP Datasphere (DWC)
- SAP Datasphere integration with SQL server using Data Provisioning Agent
- SAP Road Map – SAP Analytics Cloud Session at #SAPSapphire
- Build SAP Analytics Cloud’s Dashboard for SAP SuccessFactors KPI
- Getting ESG metrics from SAP Sustainability Control Tower
- Walkthrough: Capturing Business Events in SAP Datasphere using SAP Integration Suite
- SAP Inside Track, Bengaluru
- sit#Hydrabad Event 13-05-2023 highlights
- Motivation and Key Artefacts of SAP Data and Analytics Advisory Methodology
- SAP Datasphere and Partnerships – DataRobot
- The SAP Datasphere Data Marketplace – New Home. New Scope.
- SAP Datasphere – Triggering a Stored Procedure using a Data Flow
- Improve Business Outcomes with SAP Business Technology Platform Use Cases at Sapphire 2023
- Unleash the Power of Business Data for Utilities: The Benefits of SAP Datasphere and SAP Analytics Cloud
- Native SAP HANA data warehousing
- SAP Analytics Cloud in learning.sap.com
- Top Picks: Innovations from SAP Business Technology Platform (Q1/2023)
- SAP Datasphere: Getting Started with SAP Datasphere for Free
- SAP Datasphere – Data Access Controls on hierarchy nodes
- SAP Datasphere – New Replication Flow
- SAP Datasphere – SAP Data Provisioning Agent Upgrade
- Access SAP HANA Cloud Underneath of SAP Datasphere
- SAP Datasphere in Q1 News
Find more information and related blog posts on the topic page for SAP Datasphere. You will find further product information on our Community with various subpages about Getting Started, Business Content, the SAP BW Bridge as well as content for Best Practices & Troubleshooting and the FAQ for SAP Datasphere.
when it is expected that it will be possible to share analytical models between spaces ?
We have this message when we try to share a AM
Sorry, something went wrong while sharing the models
We can provide the following information to assist either you or an SAP engineer to solve the issue:
Correlation ID: c90da97e-9ddd-4b84-4fd2-3ae19ebc4bf2
HTTP Status: 500 Internal Server Error
Error Code: unknownShareError
Technical Message: sap.dwc.analyticModel is not supported for sharing.
Thanks, Patrice
Hi Patrice Mathieu , we understand this is relevant and are looking into the details on how to provide this, but it'll take a bit of time since this type of sharing is outside of the standard concept of sharing. The way sharing works today for other objects of the data layer is that objects are shared so that users can subsequently build things on top of these objects. On the execution layer underneath, the respective HANA grants are being added to the respective users. With Analytic Models, this approach to sharing needs to be extended since a) today there's no building on top of Analytic Models (they are the end of the food chain; we are working on stacking of AMs and inherit their properties upwards, cp. this roadmap item on reuse of Analytic Models) and b) if the grant-based approach is used, then also ALL contained elements (i.e. the fact plus all dimensions/texts/hierarchies down to any required level) need to be shared. From discussions w customers this is not quite was is expected since then users of the target space could just as well build their own AMs based on these shared building blocks rather than use the AM as the only consumable object and all internals are shielded.
This means that the use case that you and other customers are voicing, namely that one team (e.g. IT) can build AMs and other teams (e.g. LoB groups who have only access to their respective spaces) can use the AM in a black-box fashion, needs some interesting engineering work to happen first. This is why it's really more than just a switch on AMs.
Br, Jan Fetzer (Product Mgmt Datasphere Modelling)
Thank you very much for your answer. Our point of view We understand that this is not in the current roadmap but our vision is to share also some industrialized AM by IT to the Business by Sharing in Business Space
We don't want the business to replicate table from sources system (Live connection HANA or BW/4HANA) This is why we only create connections in the shared SPACE IT. In addition, we don't want to recreate the connections for each space (Do by BASIS IT Team). We agree and we also share FACT and DIM views to Business Space. If we build the AM in our IT space, we must give access to the business in our space, which is inconceivable.
We tried to do a JSON export and import but that doesn't work either because when importing it looks for the connection object Best regards, Patrice