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SAP Data Intelligence

SAP Data Intelligence

Overview

What is SAP Data Intelligence?

SAP Data Intelligence is presented by the vendor as a single solution to innovate with data. It provides data-driven innovation in the cloud, on premise, and through BYOL deployments. It is described by the vendor as the new evolution of…

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Recent Reviews

SAP Data Intelligence

8 out of 10
March 21, 2024
Incentivized
We have BW/HANA for Enterprise Finance data hub. We source data from multiple sources into this data hub. We build reports on top of this …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Reviewer Pros & Cons

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Product Details

What is SAP Data Intelligence?

SAP Data Intelligence aims to transform distributed data sprawls into vital data insights, to deliver innovation at scale. It is a data management solution that connects, discovers, enriches, and orchestrates disjointed data assets into actionable business insights at enterprise scale. It enables the creation of data warehouses from heterogeneous enterprise data, management of IoT data streams, and facilitates scalable machine learning.

SAP Data Intelligence aims to allow users to leverage business applications to become an intelligent enterprise and provides a holistic, unified way to manage, integrate, and process all your enterprise data.

With SAP Data Intelligence users can:

1. Discover and connect to any data, anywhere, anytime from a single enterprise data fabric

2. Transform and augment data across complex data types and curate a robust searchable data catalog

3. Implement intelligent data processes by orchestrating complex data flows enriched with scalable, repeatable, production grade machine learning pipelines


SAP offers an overview video, a product trial, and also allows readers to explore business use cases for SAP Data Intelligence.

SAP Data Intelligence Features

  • Supported: Data catalog
  • Supported: Data pipelines
  • Supported: Operationalize machine learning
  • Supported: Data Profiling
  • Supported: Self-service data preparation
  • Supported: Monitor data processes
  • Supported: Business glossary
  • Supported: Business rules
  • Supported: Data Quality
  • Supported: Data integration
  • Supported: Data orchestration
  • Supported: Python, R, Go, and other open source operators
  • Supported: Native operators for SAP solutions

SAP Data Intelligence Screenshots

Screenshot of Business GlossaryScreenshot of Example of data quality operatorsScreenshot of Data profiling fact sheetScreenshot of SAP Data Intelligence Jupyter lab notebook for machine learningScreenshot of SAP Data Intelligence data pipeline using PythonScreenshot of SAP Data Intelligence example ata quality dashboardScreenshot of SAP Data Intelligence connectionsScreenshot of SAP Data Intelligence Metadata ExplorerScreenshot of SAP Data Intelligence Example of Table Consumer Pipeline

SAP Data Intelligence Videos

SAP Data Intelligence Integrations

SAP Data Intelligence Technical Details

Deployment TypesOn-premise, Software as a Service (SaaS), Cloud, or Web-Based
Operating SystemsKubernetes & Docker
Mobile ApplicationNo
Supported CountriesGlobal

Frequently Asked Questions

SAP Data Intelligence is presented by the vendor as a single solution to innovate with data. It provides data-driven innovation in the cloud, on premise, and through BYOL deployments. It is described by the vendor as the new evolution of the company's data orchestration and management solution running on Kubernetes, released by SAP in 2017 to deal with big data and complex data orchestration working across distributed landscapes and processing engine.

Reviewers rate Support Rating highest, with a score of 7.3.

The most common users of SAP Data Intelligence are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(101)

Attribute Ratings

Reviews

(1-2 of 2)
Companies can't remove reviews or game the system. Here's why
Holger Zecha | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
SAP Data Intelligence validation on NetApp storage technology in compination with RedHat OpenShift and SUESE Rancher Kubernetes Cluster. Validation was done for all NetApp solutions which are supported from NetApp Trident and are able to provide iSCSI LUNs. Goal of the validation has been, to enable NetApp customers a seamless usage of NetApp storage for mixed SAP workloads.
  • It runs in Kubernetes
  • Easy deployment with Installer also running in Kubernetes
  • Integration with S/3 compatible object storage on-prem and in public cloud
  • In case of failures, identifying the errors in the Kubernetes cluster is a mess
  • The certificate handling could be made easier to run SAP Data Intelligence with self signed certificates for non-prod environments
I just used Data Intelligence from in infrastructure point of view to validate NetApp storage for SAP Data Intelligence. Therefore I run only test scenarios which need to be run, to pass the SAP Data Intelligence validation. The scenarios need for passing the validation are just used to validate that the underlying infrastructure is fulfilling the SAP requirements.
  • Unfortunately this question did not apply with regards to the NetApp validation
RedHat OpenShift and SUSE Rancher are the two Kubernetes technologies for running SAP Data Intelligence on-prem.
Support from SAP and collaboration with RedHat and SUSE was very good to achive the validation within a short time frame!
A better error logging and tracing would simplify troubleshooting. A lot of details regarding errors and how to solve them are only visible within the Kubernetes pods itself. I am not sure if SAP Data Intelligence is able to improve this situation, because it may be related to the Kubernetes architecture itself and improving things might require changes inside Kubernetes.
We only used the connectors which are required to pass the validation.
Shell integration for executing shell scripts and the machine learning integration for testing the S/3 compatible object storage NetApp StorageGrid
1
Solutions Architect SAP
1
Infrastructure knowledge in LAN, storage technology, iSCSI, Kubernetes especiallyRed Hat OpenShift and SUSE Rancher. In addition knowledge in certificate handling for setiing up the necessary prerequisites like a docker repository. In addition it is also necessary to understand how the S/3 protocol works.
  • Connecting NetApp storage
  • Deliver customers a better ROI when using NetApp storage
  • Supporting customer for hybrid use cases
  • Not applicable, since we only did the validation for NetApp storage
  • Not applicable, but we will validate subsequent versions of SAP Data Intelligence on NetApp storage
We re-validate subsequent version of SAP Data Intelligence, for our customers!
No
  • Other
We did not purchase SAP Data Intelligence, we used the included 30 day license to finish the validation of SAP Data Intelligence for NetApp storage
Not applicable!
Using Kubernetes made the configuration and installation quite easy - if everything works well. If issues arise it is difficult to do a proper analysis, because of the distributed nature of Kubernetes
Not applicable
No - we have not done any customization to the interface
No - we have not done any custom code
Not applicable
  • Using container technology and running SAP Data Intelligence inside a Kubernetes Cluster has been a good choice
  • Deployment using the new installer is easy
  • Error analysis when some pods do not start
Cooperation with Red Hat for Red Hat OpenShift validation, with SUSE for SUSE Rancher validation and also with SAP was very good!
SUSE, Red Hat and SAP are NetApp partners.
Not applicable!
Not applicable!
Manoj Kumar Das | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
SAP BI/BODS, SAP LUMRIA, SAP CRYSTAL REPORTS
  • AI-powered data extraction Software, Powered ML Engines that can transform your structured, unstructured, and streaming data into useful business insights.
  • GPU support for inference on ML models for your one premise installation.** BYOM**
  • The mode-option is for storage types that supports ADL or HDFS. Object stores such as S3 and SDL are not supported to append operation on storage level.
  • It gives opportunity to utilize OCR, ICR, IDR technology to support the business needs
  • Enhance ML capability between on-premise and cloud version.
  • The capturing of pure voice records and voice forms similar to other businesses forms of communication such as (email, web forms, fax).
  • Data integration services with other ML tools are more complex. Workflows could have been more simplified.
We focus on standard dashboards, but the issue was that [there was] no proper visibility and monitoring available for daily reports and dashboards during deployment phase. As this little expense tool so very much focused on the limited dashboard, and is mainly for higher management.
  • Customizing the application rather than the less available dashboards.
  • All the Management Reports, Performance Charts & Indicators, Product review information is retrieve[able] with a single click.
  • Implementation Strategy worked with 3rd-party service providers also.
SAP Data Intelligence tool could serve all our requirements out of a data intelligence tool,
Business analytics are well managed and also helps in building better business insights from a future perspective.
Integrates well with other Data Management tools already in place and helps segregate and group data from Multi-Sources and Domains using metadata catalogs.
130
  1. General management
  2. Public Relations
  3. Purchasing/Procurement
  4. Human Resources
  5. Production & Development
  6. Administration & IT Customer Care
  7. Marketing/Finance
8
ISO/IEC 27001 certification
Experience in leading a team of HANA developers in design and development
Experience in SAP SLT and Business Object Data Services BODS to acquire the data into SAP
Experience in SAP HANA XSJS to expose data to other clients
Experience in interacting with business users for coordinating with them for requirement gathering

Knowledge of C++/object oriented programs, Java, web application development, security and open source technologies

• Skills for developing, deploying & debugging cloud applications
• Skills in API usage, command line interface and SDKs for writing applications
• Ability to use continuous integration and distribution pipelines to deploy applications
• Ability to code to implement essential security measures
• Familiarity with HTML/CSS, JavaScript and UI/UX design
• Excellent knowledge of UML and other modeling methods
• Experience working with databases: SQL, NoSQL, Key-value stores, etc.
• Master’s degree in Computer Science/ Computer Engineering or equivalent on-the-job experien


  • Metadata extraction for SAP S/4HANA & SAP Business Suite systems
  • Build and deploy SAP HANA Machine Learning pipelines in SAP Data Intelligence
  • Self-service and data-driven data preparation for business users
  • Transform, shape, harmonize, curate, enrich the data by a simple click
  • Create new data sets based for scenario and project requirements
  • Show sources and transformation of datasets
  • graphically from target
  • Connect lineage in data discovery (SAP LUMIRA/SAP BI/BO)
  • Tied with the connectivity and discovery from Metadata explorer
  • Define Data Quality (DQ) thresholds and monitor using
  • scorecards for key DQ KPI
  • Roadmap to tie in with data preparations
Allow collaborations among various personas with insights as ratings and comments on the datasets
Reuse knowledges on the datasets for new users
Next-Gen Data Management and Artificial Intelligence
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