Dataiku vs. IBM InfoSphere Information Server

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

Incentivized

You can use infosphere: -If you have multiple targets and source systems and they are different than each other. -If your infostructure is so big and unplaned well so you can't find what you want to see. -If your databases not so strong to process your data

Incentivized

• Low-code platform.
• Open source version includes most valuable modules.
• User friendly documentation.

Incentivized

• It is very strong to make transformations/data derivations
• It is very easy to connect to various external data sources. It has an interface (stages) for each connection that simplifies the task
• It is a stable platform. And that parallelism helps make it fast for loading, if the process is well designed

• The visualization feature of flow still has a lot room to improve, when the flow is complex.
• The "non-coding" template/building block for deep learning lack of many important configurable parameters.
• Lack of the unified way to allow applying the "design pattern" on the Python codes (if we want to develop our own module or building blocks.

Incentivized

• Lack of a strong web development environment.
• Metadata propagation in Jobs is somewhat complex.
• The possibility to develop jobs in Parallel and/or Server Engines is confusing.

• Scale of implementation
• IBM techsupport

Incentivized

The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level

Incentivized

The amazing part of Dataiku DSS is their customer service. Based on urgency and technical level, you get a reply from the Dataiku engineer when you raise a query. So far, my queries have been pretty complex to solve, so I have received solutions even from the CTO of the company as well, which is why I would describe their customer support as very good.

Incentivized

Dataiku was selected for me, but I am happy about that. I like Dataiku for the user experience, it feels less code-y and I like to demo things to non technical stakeholders because they can still follow along. When you open some other notebooks, you can see that peoples eyes start to glaze over

Incentivized

I particularly believe that Information Server, especially DataStage, is superior in many aspects to the Oracle Data Integrator tool. Several market analysts such as Gartner and / or Forrester better position DataStage on the Oracle solution.

• So far it has had a positive impact. Multiple departments are coming to us with their business problems.
• I can't specifically say about ROI as I'm a developer, though I have heard this solution is economical compared to other AI/ML enterprise tools.
• By using this tool, my client has let go of software that was used earlier, and we have created a simpler framework to replace that software.

Incentivized

• Information Server can positively impact the costs of companies by increasing the productivity of development and therefore reduce their time and costs. It is estimated that DataStage can increase a developer's productivity by 40% on average.
• Better data governance
• Improve data quality and reduce bad data impacts

Incentivized