Oracle Data Service Integrator vs. SAP Data Intelligence

Once Oracle Data Service Integrator is set up properly, it is quite simple to snatch data from a myriad of disparate sources and put them into a single repository. Due to cost factors, it is not for the faint of heart; however, if you can handle it, the benefits will quickly outpace the initial investment. To take full advantage of the power of ODSI, I would recommend the training that Oracle provides.

Incentivized

If you have an SAP products ecosystem in your IT landscape, it becomes a no-brainer to go ahead with an SAP Data Intelligence product for your data orchestration, data management, and advanced data analytics needs, such as data preparation for your AI/ML processes. It provides a seamless integration with other SAP products.

Incentivized

• Very easy to use. Very easy to just grab data when needed.
• Very robust and has so many features to move and transform data.
• Functionality is very easy to implement with other Oracle data products.

Incentivized

• 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.

Incentivized

• If not bundled with other products, the price can be prohibitively expensive
• SAP connectors tend to be a little expensive and slightly difficult to use
• The audit module had some difficulties debugging failed instances

Incentivized

• Data transfer speed tends to be slow when there is poor internet connection since SAP Data Intelligence don’t synchronize data while offline. However, this is not vendor fault, that’s why we have implemented robust wireless technology internet connection in our organization.

Incentivized

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

Incentivized

I think the troubleshooting process might be streamlined with improved error recording and tracing. A lot of information about issues and how to fix them is hidden away in the Kubernetes pods themselves. I'm not sure whether SAP Data Intelligence can fix this problem it may be connected to Kubernetes's design, in which case fixing it could need modifications inside Kubernetes itself.

Incentivized

Initially we struggle to get help from SAP but then dedicated Dev angel was assigned to us and that simplify the overall support scenario. There is still room of improvement in documentation around SAP Data intelligence. We struggle a lot to initially understand the feature and required help around performance improvement area,

Incentivized

While we tested and evaluated multiple data service providers, we came to choose Oracle Data Service Integrator since our infrastructure was so heavily dependent on Oracle software. The implementation would be easier and the difference in the number of features and performance wasn't enough to sway us in another direction.

Incentivized

One of the reasons to pick SAP Data Intelligence is the speed and security it provides, in addition to the excellent support it provides. It is also compatible with all popular databases, which is another reason to choose it.

Incentivized

• It provided terrific bulk data movement and data transformation services which we desperately needed
• It allowed to reduce development costs and lower the total cost of ownership among disparate architectures
• The enterprise edition's ELT allowed us to generate native code for wildly varied RDBMS

Incentivized

• Automation of data management slashed tasks by over 60% in most departments for the first 8 months.
• Metadata catalogs have enabled us to categorize data from disjointed sources in one place.
• It runs multiple ML models which enhances flexibility when managing data.

Incentivized