Dataiku vs. Toad Data Point

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

Toad Data Point is the clear tool of choice if the end-user is interested in reports that are relatively simple to build using SQL code and export to Excel. It is less useful if the analyst also needs to run statistical models on the data and visualize the data for those functions I usually use RStudio or Jupyter Notebook which incorporates those features much more seamlessly.

Incentivized

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

Incentivized

• Ease of use. It is a very easy product to use without any particular training.
• Multiple data source connection. It easily connects to multiple data sources which allows you to use different data sources to run reports.
• Seamless application. Once you set everything up-- whether it is an FTP or export of data-- you can set it and forget it!

Incentivized

• 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

• The program can error out when reconnecting to a database from a timeout but that is easily fixed by restarting the program.
• If you aren't familiar with database tools the UI can be overwhelming to some.

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

I find Toad Data Point easy to use for both the novice and the experienced business analyst. If all you desire is to access data and create spreadsheets...this is a snap. Toad Data Point actually has cool data analysis features built into it. The newer workflow interface makes automating steps a snap

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

TOAD excels at connecting to divergent data sources, but appears geared more to DBAs than to regular query users. Microsoft's offerings excel against Microsoft SQL Server, but sometimes struggle with other data sources. However, SSMS and vs code excel at many developer productivity/workflow enhancements. vs code, in particular, has a lively extension system that allows it to be tailored for development/querying/model building/etc. That flexibility comes at a cost - the learning curve is steep for new users. The tradeoff between complexity and power may not be good for some environments/users/situations.

Incentivized

• 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

• It is the least common denominator - not particularly optimized for our environment or workflows.
• Hangs or slowdowns add anywhere from 5% - 7% for projects utilizing large/complicated data setts. (This could be due to other IT-imposed constraints and not entirely due to TOAD.)
• Trying to perform some operations requires reading documentation and experimenting in order to figure out the TOAD-specific approaches and commands.
• It just works (when we understand it). Updates don't break things and things don't suddenly start behaving differently. Best of all, we don't mysteriously lose functionality.

Incentivized