Dataiku vs. Vizlib

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

I have read reviews where Vizlib is performing well. That was not our experience integrating Vizlib with Qlik Sense cloud as a write back capability. So, my experience is that integrating Vizlib with Qlik Sense cloud as a write back capability is probably not an area where they perform well

• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

Incentivized

• Visualizations
• Forecasting
• Storytelling

• End product deployment.

Incentivized

• Vizlib promised a writeback capability that it just was not able to deliver. We bought additional developer licenses for QlikSense that were not planned in the initial Vizlib discussions, but even with they were not able to implement a writeback capability.
• I feel Support was horrible... very slow, and with inadequate expertise.

As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

Incentivized

We have figured out how to use Vizlib features on our own on many occasions. Whenever we do run into questions, the support team is very helpful and able to help us figure out how to do things quickly. I feel features are more advanced, but we know the depth is there when we’re ready to use it

The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

Incentivized

Vizlib support is awesome! We have quick turnaround times and regular updates when we open cases. It’s so good we often ask them Qlik Sense questions, rather than going to Qlik support

Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

Incentivized

I selected Vizlib based on several positive Qlik community forum comments. It would seem that they had a different use case than we did

• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

Incentivized

• We have had a negative experience with Vizlib... we paid for a capability that wasn't delivered, and that cost us time that could have been better spend on other priorities. Negative ROI.