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.
Automating end-to-end data pipelines has boosted team productivity and simplified analytics
What is our primary use case?
My main use case for Dataiku is general; I create ETL pipelines and then automate everything using that, along with ML modeling. These are the major use cases that I have for Dataiku. On a daily basis, we use Dataiku for ad hoc analysis for following the product lifecycle.
For one of my use cases with Dataiku, we are using it where the data resides in Snowflake and the expectation is to orchestrate and automate a complete CI/CD pipeline, with the final data residing on S3. In between, there are multiple logics and transformations that we have to build in. Along with that, we are supposed to do all the DQ checks, data quality framework, and data governance. We automated everything using Dataiku, and now the project is live, with overall efficiency being very good.
Automating that workflow with Dataiku increased the overall productivity of the team compared to the tasks that we used to do earlier using other ETL tools. Dataiku has optimized that, and data visualization became easy. The checkpoints that Dataiku provides, such as analyzing the data and finding the outliers, became easy, and sharing the data sets became easy as well. Now, with the visual recipes, even people who can't code can also do the transformation, so overall, it is a good tool.
We have created a few visual recipes that are not only limited to the project; we created a package so that they don't have to code that part of logic again and again. We have provided them as a recipe, which is a good thing.
What is most valuable?
The best features Dataiku offers include the data analysis part, the ETL, and the overall orchestration part. We can create a recipe and share it with others without having to code that again and again, and we can create an application and a dashboard in one single place. These are the very good features of Dataiku.
I find myself and my team relying the most on the data analysis part of Dataiku. We use it to visualize the data, find the outliers, and it helps us very well.
Dataiku has positively impacted my organization as most of our projects have been migrated to Dataiku, and now people are relying on it as a go-to tool for all our data use cases. This migration has led to measurable improvements, as most of the projects have been migrated and the overall efficiency has increased. Most people who used to do tasks manually are now working on automating that.
What needs improvement?
Dataiku can be improved from the dashboard perspective because right now it is very restricted, and I feel that can be improved. API integration and other aspects can also be enhanced, but I am pretty impressed with the rest of it.
For how long have I used the solution?
I have been using Dataiku for four years now.
What do I think about the stability of the solution?
What do I think about the scalability of the solution?
Dataiku's scalability is pretty good; I can scale the projects very easily, and clear guidance is given as well. I have no issues with that.
How are customer service and support?
I need to stress upon the part about customer support because there are some product issues we have identified and raised with customer support, but sometimes the response is delayed, so that can be improved.
Which solution did I use previously and why did I switch?
I previously used a different solution before Dataiku, and the other solution was not cloud-based; they were local, which made the license cost higher.
How was the initial setup?
My experience with pricing, setup costs, and licensing is good because that was managed by my IT team, and overall it was seamless with clear guidance given.
What about the implementation team?
We have a direct link with Dataiku; we did not purchase it through the AWS Marketplace.
What was our ROI?
I cannot share the numbers regarding return on investment.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup costs, and licensing is good because that was managed by my IT team, and overall it was seamless with clear guidance given.
Which other solutions did I evaluate?
Before choosing Dataiku, I evaluated other options, specifically Databricks.
What other advice do I have?
My advice to others looking into using Dataiku is to first understand the product, which is very important. You should first see what your use case is, what Dataiku is offering, and understand that it is a tool meant not only for coders but also for higher management, as they can do drag and drop to easily perform transformations without needing to write code. Dataiku is a tool for everyone. I would rate this product an 8.5 out of 10.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
