Reviews (1-16 of 16)
- It integrates well with our current ecosystem of SAP products, like HANA.
- It provides end-to-end machine learning operations, with tools for the complete model life cycle.
- It has a simple user interface for novice users, with complex tools also available for power users.
- It builds on SAP Data Hub, providing all the ETL functions of that tool with additional machine learning functionality.
- It can run in the cloud, no on-premise software management needed.
- Many programming languages are supported, it provides a sandbox environment for the user to develop in whichever style they prefer.
- SAP is very actively developing and improving it.
- The administration tools are still in development, currently SAP must be contacted for most admin tasks.
- Updates sometimes introduce unwanted instabilities.
- The documentation and examples use cases are still being written and need to be expanded.
- Data orchestration from multiple data sources
- Machine learning capabilities
- Containerize architecture to run on hybrid cloud
- Scaling requirement to handle large data
- Limited in-build machine learning features
- Need more stable product
- Powerful and very fast in processing big data under secure cloud servers
- Merging and refining and easy transformation data.
- Machine Learning performs very well but can still grow and deliver better results with the help of artificial intelligence
- It is still a fledgling platform that can grow well. I expect better and more professional integrations on this platform.
- 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.
- Can use a wide range of SAP support like SAP HANA and languages like Python
- Its easy and efficient to use
- Eases the whole process from data prep to creating model
- Too many load issues
- Can do better in terms of reporting functions; some features for data manipulation could be made better
- Can improve customer support
- Numerous packages to build business specific models
- It supports telemetry data to process and organize for analysis
- Well-performing batch processing pipelines and useful data cleansing tool
- Need more learning resources to dive deeper into the application to provide more business problem resolutions
- Application cost is high
- When it comes to bug fixes, it takes a long time to locate the issue
- Easy to pick up.
- Tools for data cleansing.
- Able to use existing learning models from the business.
- Operates on cloud.
- Not made for working with others.
- Debugging is needlessly complicated and obfuscates issues.
- Documentation could use more examples to highlight points.
- Integration with SAP sources
- Real time source data access
- State of the art UI and tooling
- Enterprise readiness features
- Overall solution stability
- Reusables for data lake management
- More detailed monitoring layers for external access
- Provides lots of standard operators and default pipelines
- Good Python notebook integration for data discovery
- Graphical UI to develop pipelines reduce amount of code written
- Better documentation with examples of how to use connectors (i.e. what input/output must look like)
- Debugging functionality required to understand where pipelines fail and how data looks at that point
- SAP libraries (e.g. python hana_ml) should be STANDARD and pre-installed to avoid dockerfiles and other workarounds
- Dockerfile execution takes long times without giving a status where it is failing/held up
- HANA Read/Write Table operator throws TLS errors which are unlikely when HANA connection generally works
- Easy handling even without prior experience
- Possibility to use Python and R libraries
- Debugging pipeline failures is not as easy and straightforward
- When working with SAP DI, new tabs get opened frequently when actions are performed. Having 10+ open tabs is not very handy
- The best thing about SAP is that we can track our daily expenses in very little time.
- This affects my business even more than Microsoft 365.
- It updates us on our expenses and sales daily.
- I wish it the acronyms made more sense.
- Sometimes it's functionality is little bit slow.
- Data integration services with other machine language tools were more complex. Workflows could have been simplified on this front.
- Data orchestration from diverse sources.
- Good inbuilt support to develop models (Jupyter Notebook).
- Data pipelines, container architecture for deployment.
- Tools for data cleansing.
- Ability to port existing ML models into DI.
- Product support documentation and blogs can be better.
- Debugging can take a bit of time to understand and pinpoint issues.
- Can be pricey.
- Data is integrated from different source systems, which is then analysed first. After the analysis data is transformed and harmonized.
- Further, machine learning algorithms are modeled in SAP Data Intelligence and run on the data set.
- What is more, the SAP Data Intelligence environment interacts with different on premise systems and the results of the machine learning processes are sent back to the on premise systems.
- Great machine learning capabilities
- Many possibilities to connect source systems and interact with it
- Operation on cloud reduces administration tasks to a minimum
- Great Governance capabilities
- Still fairly new product
SAP Data Intelligence Scorecard Summary
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 Data Intelligence Screenshots
SAP Data Intelligence Videos (5)
SAP Data Intelligence Integrations
SAP Data Intelligence Competitors
SAP Data Intelligence Pricing
- Has featureFree Trial Available?Yes
- Does not have featureFree or Freemium Version Available?No
- Has featurePremium Consulting/Integration Services Available?Yes
- Entry-level set up fee?Optional
|Subscription||$1.20||Per Unit Per Month|
SAP Data Intelligence Support Options
|Free Version||Paid Version|
|Video Tutorials / Webinar|
SAP Data Intelligence Technical Details
|Deployment Types:||On-premise, SaaS|
|Operating Systems:||Kubernetes & Docker|