COZYROC vs. SAP Data Intelligence

[COZYROC] is a powerful tool and [extends] what you are able to do when it comes to ETL. It allows you to accomplish a lot of things that are very common now, but very difficult to implement with the out of the box functionality of SSIS.

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

• Dynamic SOQL is very advantageous with Salesforce source task.
• Connecting to SharePoint is simple and seamless.
• Script Component Plus is also a very good task that we can do a lot through, that what we can't do with Script Task.

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

• Smaller set of useful transform tasks than some competitors
• Upgrade process could be smoother (though similar to others in this space)

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

Easy to use and easy to implement into ETL solutions with Microsoft SQL Server Integration Services.

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

Their support is fast and responsive. Their devs work on resolving the issues quickly. We usually will have a release of a fix within a few days.

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

We tested several other SSIS add-on packages as well. Only a few had DB2 support. And from those few COZYROC's support was the most responsive and helpful. They practically added the functionality we needed if it was not already there.

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 is my belie that I have thrown away $3.7k

• 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