SAP Data Intelligence vs Talend Data Integration

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.

The same way you design data integration job can be used to design services. It is easy to enhance by custom components and can adapt to all requirements. Talend Data Integration connects to [a] multitude of data sources and streaming service. Very easy interface to design complex applications without spending much time on coding. Easy to learn and master. Talend constantly strives to better itself by adding more features and functionalities.

• 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 speed of migration is very good for a Free ETL tool.
• The amount of components or connectors we have is impressive.

• 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

• Syncing with Git should be made easier
• Can face issues with certain big data spaces
• Support find it difficult to resolve complex issues

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datasets Reuse knowledges on the datasets for new users Next-Gen Data Management and Artificial Intelligence

- 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

We use Talend Data Integration day in and day out. It is the best and easiest tool to jump on to and use. We can build a basic integration super-fast. We could build basic integrations as fast as within the hour. It is also easy to build transformations and use Java to perform some operations.

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,

Good support, specially when it relates to PROD environment. The support team has access to the product development team. Things are internally escalated to development team if there is a bug encountered. This helps the customer to get quick fix or patch designed for problem exceptions. I have also seen support showing their willingness to help develop custom connector for a newly available cloud based big data solution

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.

Talend is much versatile in assimilating various different business use cases. It covers more functionality and is packed with tons of features to explore. It has the ability to fine tune to each project, and not be a one-fits all solution. Problems with Excel, is that it is not suited for large projects, collaboration work. Alteryx is a very costly alternative.

• 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.

• Easy to build complex data transformations.
• Licensing model isn't that flexible.
• Memory mangement for huge volumes of information. You have to modify ETL designs to handle it properly.