data.world vs. SAP Data Intelligence

Well suited

• For quickly discovering, preparing, and sharing relevant data from a wide range of
  sources.
• Easily managing numerous file formats.
• For contributing and discussing data in one place to aid fast, efficient collaboration.
• For joining datasets from many sources.
• Integrating datasets with other platforms.

Less appropriate

• For full data management.
• For those unfamiliar with database concepts and SQL.
• Very limited chart builder

Incentivized

In the two organizations that I have used SAP Data Intelligence, it has been effective in managing data operations. SAP Data Intelligence is the most robust data integration and management solution that I have ever come across. I would highly recommend this service to all prospects that are looking to create, execute and reuse data models at ease.

Incentivized

• Ingest data from almost any source
• Join disparate datasets
• Integrate with other platforms
• Create subsets of data and share across teams

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

• Organize queries/datasets by categories
• Allow data edits for admins
• Requires knowledge of SQL - visual SQL builder would help with accessibility

Incentivized

• 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

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

It's a very user-friendly platform for those familiar with database concepts and SQL. Documentation is extensive but for those with no prior knowledge it can be daunting. The lack of ability to organize queries and datasets by category can also be frustrating and sometimes difficult to navigate.

Incentivized

- Difficult debugging, sometimes it does not show where an error occurred so the error finding is difficult - Lot of tabs get opened when working with the tool which is not very user friendly - Pipelines and notebooks were sometimes deleted and could not be restored - Frequently used libraries (such as hana_ml) could not be installed via the dockerfile and should potentially be pre-installed for better usability

Incentivized

Support has always been excellent.

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

I haven't looked at any other products.

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

• Minimized the organizational reliance on Excel & email
• Provides more effective validation & standardization
• Allows staff easy access to data
• Slow adoption across the organization due to learning curve

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