We used Dataiku for a demand forecasting project where the objective is to forecast the demand for each product for the next three months.
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Low-code projects have empowered non-technical teams and now need better integration and visuals
What is our primary use case?
My main use case for Dataiku is data science and AI projects.
What is most valuable?
The best features Dataiku offers include the ability for users to use the node without having to code and the functionality related to low-code/no-code.
Dataiku has positively impacted my organization by allowing non-technical users to adapt a data science project and to maintain a part of a data science project.
What needs improvement?
I think a pain point related to Dataiku is the visualization, which is not straightforward, and the integration, which is also not straightforward for non-technical users.
To improve Dataiku, the company could enhance the capabilities related to integration and visualization.
For how long have I used the solution?
I have been using Dataiku for three years.
What do I think about the stability of the solution?
Dataiku is stable.
What do I think about the scalability of the solution?
Dataiku's scalability can be better.
How are customer service and support?
I have never tried Dataiku's customer support.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Before, we used a solution that I cannot mention, but the change is more related to using a more straightforward solution for non-technical users.
Before choosing Dataiku, I evaluated KNIME.
What was our ROI?
I have not seen any specific outcomes or metrics such as time saved, reduced costs, or improved project delivery.
I have not seen a return on investment with Dataiku in terms of time saved, money saved, or fewer employees needed.
What's my experience with pricing, setup cost, and licensing?
I am not the person involved in the process regarding pricing, setup cost, and licensing.
What other advice do I have?
My advice to others looking into using Dataiku is to use it principally to help and support non-technical users.
Dataiku is deployed in my organization on a public cloud on Amazon Web Services.
Amazon Web Services is our cloud provider.
I am not the person involved in the process of determining whether we purchased Dataiku through the AWS Marketplace.
My review rating for Dataiku is 7.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Flow-based demand forecasting has improved collaboration but still needs better visualization options
What is our primary use case?
My main use case for Dataiku is for data science and AI projects. I use Dataiku for a demand forecasting use case where the objective is to predict the demand for each product for the next four months. Demand forecasting is the primary focus where I use Dataiku.
What is most valuable?
The best features Dataiku offers that help me with my demand forecasting and data science projects include having a complete overview of the flow directly from the flowchart, allowing me to observe all the steps in a single overview, and the ability to use a no-code, low-code node.
Having that flow overview and the no-code, low-code nodes makes my work easier by allowing me to use a simple function without coding directly, meaning I can avoid using Python. In 80% of the project, we are using Python, but for very simple steps, we also use a low-code, no-code node, which can be simpler for users that are not technical and may want to do some preprocessing steps.
Dataiku has positively impacted my organization, but it is a tool that is very similar to others and it helps for what I mentioned before and not for other areas. The ability to use low-code or no-code nodes is more a convenience in that case, mainly for a non-technical user. We deliver this kind of solution for a client where the user is not so technical, and for this reason, it is better to have this kind of flow and tool.
What needs improvement?
To improve Dataiku, it could enhance its visualization features, as it is not possible in Dataiku to create direct visualizations or to integrate a web app directly or in a simpler way as it is possible for a preprocessing step. Visualization and integration are the main areas I would like to see enhanced.
In my experience, Dataiku can be more stable.
For how long have I used the solution?
I have been using Dataiku for two years.
What do I think about the stability of the solution?
In my experience, Dataiku can be more stable.
What do I think about the scalability of the solution?
Dataiku's scalability is not one of the best solutions to scale.
Which solution did I use previously and why did I switch?
We used a lot of other solutions before Dataiku and we switched only so that non-technical users can improve and maintain this kind of flow.
What other advice do I have?
My advice to others looking into using Dataiku is to use it for a simple flow in data science and to teach how to make a data science project or flow for non-technical users. I would rate this product a 7 out of 10.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Functionality
What do you like best about the product?
Strong version control, shared projects, and role-based access enhance teamwork across data and business teams
What do you dislike about the product?
Some powerful capabilities are only available in higher-tier or enterprise versions, which may not be cost-effective for smaller teams
What problems is the product solving and how is that benefiting you?
Covers the full data lifecycle: ingestion, preparation, modeling, deployment, and monitoring
Good
What do you like best about the product?
click and go approaches are easy and efficient
What do you dislike about the product?
I want to extract the code for the click and go operation that we conduct.
What problems is the product solving and how is that benefiting you?
increasing efficiency
Platform suitable for both - Advanced Data Scientists and Analysts as well as Beginners
What do you like best about the product?
The capability to use custom code besides its built-in features that help augment the outcome. Also, the timely response from its Customer Support to address questions and blockers is a huge plus.
What do you dislike about the product?
The use of inferred data-types in several of its built-in recipes - that may cause downstream issues when the original data type mismatches the inferred type.
What problems is the product solving and how is that benefiting you?
Custom coding in modular form augmented by its built-in features
An innovative approach to data analytics
What do you like best about the product?
The platform seems very user friendly. The data visualizations are easy to select and implement using the data.
What do you dislike about the product?
although it is user friendly for the beginner data analyst, it is clear that a basic or fundamental understanding of programming and data analytics is important for full benefit of the product.
What problems is the product solving and how is that benefiting you?
Text analysis from scanned documents and converting to data to be analyzed, predicting values given a set of conditions, creating data visualizations / dashboards
Good tool for ai and gen ai llm but not etl and enginer
What do you like best about the product?
Gen ai possibilities, llm mesh possibilities and the ai stuf
What do you dislike about the product?
ETL and Data Engenireering part, specifically with big data
What problems is the product solving and how is that benefiting you?
ETL Problems
Great Orchastation tool
What do you like best about the product?
It is a great tool to use as a data analyst.
Great to track data from different sources in a single place
Great to track data from different sources in a single place
What do you dislike about the product?
Will need more comprehensive material to train new programmers
What problems is the product solving and how is that benefiting you?
We are doing a poc to build a agent to help service center executived of one only our insurance client on different products they offer. Dataiku is being used as pri.arh platform to do the poc
Flexible, Usable but not perfect.
What do you like best about the product?
Dataiku has been a game-changer in democratizing data workflows. I love how intuitive it is for cross-functional teams to build pipelines, transform datasets, and collaborate all within a visual flow and massage the data. It’s the kind of tool that makes manipulation effortless, experimentation easy, and sharing work frictionless.
What do you dislike about the product?
As much as I appreciate how approachable and feature-rich Dataiku is, there are moments where its flexibility feels like a double-edged sword. Some use cases become surprisingly complex due to very recipe logic, and handling parameterized or reusable workflows can feel clunky.
It can also be slow as a cloud-based platform, with multiple users editing single workflows. It gets the job done—but sometimes you just wish it handled faster, cleaner, more tactile.
It can also be slow as a cloud-based platform, with multiple users editing single workflows. It gets the job done—but sometimes you just wish it handled faster, cleaner, more tactile.
What problems is the product solving and how is that benefiting you?
Dataiku is the backbone for new data warehouse. It's been essential in uncovering decentralized logic and hidden SQL ( tribal knowledge . the visual flows makes transformations traceable and reproducible. The tool succeeds in getting the many different teams working with a single source of truth and eliminating the several different versions that previously floated around the org and lastly, allows for faster delivery capabilities.
Reduction of time to value
What do you like best about the product?
Visual layout makes data transformation more clear. Built in tools speed up development time in particular with tools like the LLM recipes and ML model "battle"
What do you dislike about the product?
I have noticed inconsistencies with how code is executed in a python notebook vs a python recipe. Sometimes that works in a recipe doesn't work in a notebook. Refusal to add global dark mode.
What problems is the product solving and how is that benefiting you?
Simplifying the use and deployment of LLM projects in friendly interface.
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