Dataiku for Enterprise AI
DataikuReviews from AWS customer
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Template Solutions That Speed Up Implementations
What do you like best about the product?
Template solutions to speed up implementations
What do you dislike about the product?
The template solutions don't expose the code behind so it's sometimes hard to understand how the features work (documentation is not sufficient)
What problems is the product solving and how is that benefiting you?
Quick prototyping of solutions
Powerful Dataiku Integrations, Though I Haven’t Used It Yet
What do you like best about the product?
All the integrations you can add in Dataiku are really powerful.
What do you dislike about the product?
Im not a user but i have an interest in dataiku
What problems is the product solving and how is that benefiting you?
As a data scientist, I feel the next update in June 2026 would transform my job and make it much easier and more efficient, especially for doing PoCs.
Easy-to-Use Recipes Make Scenario Setup Simple
What do you like best about the product?
Having easy to use recipes with an easy and simple way to setup scenarios
What do you dislike about the product?
It’s not that I dislike this, but I want to have easier tool to use AI with step by step tutorials
What problems is the product solving and how is that benefiting you?
Everything is consolidated into one environment. I have the ability to do so much things in Dataiku
Simple Data Analysis That Saves Time
What do you like best about the product?
The simplicity to analyse data, results, and the gain of time compared to doing all that in python in a classic IDE
What do you dislike about the product?
Recently I did not like how difficult and long it was to add input files in managed folders. I could not upload subfolemders for exemple. Also multiple times when I uploaded large number of files, some were not uploaded.
What problems is the product solving and how is that benefiting you?
I work in data science and we do everything in dataiku
Flexible AI Platform with Stellar UI, Needs Better Visualization and Deployment Support
What do you like best about the product?
I think the user interface of Dataiku is very user-friendly. Even if you don't have a strong data science or data engineering background, you can still use it by drawing boxes, which makes it accessible for many people. I also like that you can customize your solutions by writing your own code to cater to specific business needs. Additionally, with its fast-paced development, Dataiku regularly updates and upgrades the system to include the latest AI features, which I find awesome. The graphical, no-code environment significantly reduces my development life cycle, saving at least 50% of my time. It also makes interaction with end users easy because they can access our development environment to see progress and give quick feedback.
What do you dislike about the product?
So first of all, I think I got some limitation that you to be honest with you, because let's say, if you want to display and visualize a large dataset, it always has some limitation. And, also, I find out the dashboard in built by the API is not super fancy and super user friendly. Comparing to Power BI or the other visualization tools like Tableau, I think that's something that you can improve as well. Other main pinpoint for us is about the deployment. Because, you need to link to the different development, the requirements, how to deploy our AI solution, particularly to another cloud form. For example, AWS Azure, I think that we need a little bit more support on this.
What problems is the product solving and how is that benefiting you?
Dataiku helps me build AI solutions like multi-agent systems, handling both test images and numerical data. It significantly reduces my development life cycle by 50% and enhances collaboration by allowing quick user feedback, leading to faster project iterations.
Flexible and Visual, But Could Improve Code Management
What do you like best about the product?
I like that Dataiku makes data analysis more visual and less painful. I appreciate the flexibility of the solutions available, such as the ability to host custom Python webapps, use Python filters, build custom pipelines, and create custom scenarios. The initial setup was super easy after doing the trainings.
What do you dislike about the product?
Webapp code management is challenging because it involves working with one big file, and the limited Python API calls are restrictive.
What problems is the product solving and how is that benefiting you?
I find Dataiku makes data analysis more visual and less painful.
Very Easy to Use with Numerous Use Cases
What do you like best about the product?
Very easy to use and numerous use cases.
What do you dislike about the product?
I don’t dislike that much - nothing to declare here
What problems is the product solving and how is that benefiting you?
Data transformation, reconciliation, machine learning
Visual Recipes and Ease of Use Make This a Joy to Work With
What do you like best about the product?
I do enjoy greatly the visual recipes and ease of use
What do you dislike about the product?
I dislike the fact that insights sometimes are just a snapshot in time, not re-usable
What problems is the product solving and how is that benefiting you?
It is solving data and analytics problems
A Tool That Brings Everything Together
What do you like best about the product?
I really like how Dataiku brings everything together in one place. It makes my workflow feel more organized and less scattered, which helps me stay on track. That said, there are times when it can feel a bit overwhelming, especially with so much in one interface, but overall it still makes my work easier.
What do you dislike about the product?
For me, the biggest downside is that it doesn’t always feel as intuitive as I’d like, especially once I get into the more advanced parts. At times, I end up spending more time trying to figure out how to do something than actually doing it, and that can be pretty frustrating.
What problems is the product solving and how is that benefiting you?
Dataiku helps me bring everything into one place. Before, I had to jump between different tools for data prep, analysis, and modeling, which made the whole process feel scattered and inefficient. Now my workflow feels much more organized and streamlined, and I can spend more time focusing on the actual problem I’m trying to solve instead of constantly managing and switching between tools.
Dataiku:A plug in tool for Data Science
What do you like best about the product?
What I like most about Dataiku is how it brings the entire data workflow into one place. It allows teams to easily prepare data, build machine learning models, and deploy them without switching between multiple tools. The visual interface makes it easy to understand data pipelines, while still allowing advanced users to write code when needed. This balance between visual tools and coding flexibility makes collaboration between data scientists, analysts, and engineers much smoother. It helps teams move faster from raw data to real insights and production-ready models.
What do you dislike about the product?
One thing I dislike about Dataiku is that it can feel a bit heavy and complex, especially when working with very large datasets or many workflows. Sometimes the interface becomes slower, and managing multiple projects can get confusing. Also, while the visual tools are helpful, certain advanced customizations still require coding, which might be challenging for non-technical users. Overall, it’s a powerful platform, but there is a bit of a learning curve when you first start using it.
What problems is the product solving and how is that benefiting you?
Dataiku helps solve the problem of managing the entire data and machine learning workflow in one platform. Instead of using separate tools for data preparation, analysis, model building, and deployment, Dataiku brings everything together. This makes it easier to organize projects, track data pipelines, and collaborate with other team members.
For me, it has been helpful because it simplifies the process of turning raw data into useful insights and models. It also improves collaboration between technical and non-technical teams, since analysts can use the visual interface while data scientists can still write code when needed. Overall, it helps speed up the development process and makes data projects more structured and easier to manage.
For me, it has been helpful because it simplifies the process of turning raw data into useful insights and models. It also improves collaboration between technical and non-technical teams, since analysts can use the visual interface while data scientists can still write code when needed. Overall, it helps speed up the development process and makes data projects more structured and easier to manage.
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