Azure Data Catalog vs. SAP Data Intelligence

Azure Data Catalog helps us solve data discovery problems
brilliantly. Our organization created a single source of truth for our data,
which allowed us to develop BI and data science solutions. We can drag tables
or data sources to manage workflow. Some of the more complicated data normalization
or standardization issues need to be simplified, and I'd like to see that
happen.

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

• It's easy to use and implement.
• Ownership of the data.
• It's helpful for all businesses.
• Policy on data preservation.
• It can utilize various data sources.
• Data management is secure and simple.

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

• Maintenance and creation of documentation for data sources takes a lot of time
• One cannot connect to data using a client's app unless they know the locus of the data source.

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

Azure Data Catalogue was chosen for

• Microsoft's customer service and expertise
• Experience with Azure Cloud Solutions
• Reviews from peers on functionality and performance
• Product roadmap
• Overall costs

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

• Azure platform makes it easier for us to register & catalogue all available information easily. This way when the customer actually made a sudden request or they want to access sensitive information on their own, the data is readily presented to them. Needless to say we use this mined and catalogue data for a variety of inhouse training and other improvement initiatives.

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