Dataiku Reviews & Product Details

What I like best about Dataiku is its end-to-end data science and machine learning platform that brings data preparation, analysis, model building, and deployment into a single environment. The visual workflows combined with code-based options make it accessible for both technical and non-technical users. It also supports strong collaboration between data scientists, analysts, and business teams, which helps speed up model development and improve decision-making. Review collected by and hosted on G2.com.

While Dataiku is a powerful platform, it can feel complex for first-time users because of its extensive feature set. The initial setup and learning curve may take time, especially for non-technical users. In some cases, performance can slow down when handling very large datasets, and the pricing structure may not be ideal for smaller teams or limited use cases. Review collected by and hosted on G2.com.

I really appreciate how the graphical user interface handles paths and threads. It allows you to manage all your code and datasets visually, and everything is automatically aligned, which makes the experience very soothing to use. Review collected by and hosted on G2.com.

There isn't anything in particular that I dislike about Dataiku. However, one area for improvement would be better management of the history and recent code I've worked on. It would be helpful if this information were more easily accessible and visually highlighted. Review collected by and hosted on G2.com.

I started using Dataiku as a junior data analyst. The visual recipes have turned around how you built an analytics project from end to end. As I started tackling complex projects and started expanding my knowledge of data science and the domain I am working on, I started to discover the latitudes of capabilities that I can adopt from dataiku tools and api. It has immensely helped me to expedite my career goals. Another fantastic aspect would be the consistent upgradation of the features and tools like Data quality management, LLM mesh and Agentic AI in the studio which becomes an inspiration for me to tryout and implement additional steps (in the ML flow) that helps me increase business value in the projects I am working on. I enrolled in the dataiku academy too. Review collected by and hosted on G2.com.

As I described dataiku is fantastic to start with as a beginner but as the project gets more complex, as I started using dataiku apis in python I started feel a lack of detail in the documentation availability. For example, I wish that the dataiku apis for python to have a clearer documentation as we can observe in some libraries like pytorch, Scikit learn, Scipy or plotly. Details like all the parameters available for a specific function and additional parameters which can be used with an example or two explaining what each parameter mean for implementing of the function. The documentation currently available is highly limited in helping me understand the complete capabilities of a specific function or api. So, my best resource for referral often is the blog post answers that the dataiker provides. With gratitude I would request the team to improve the documentation to such an extent it would add value to an experienced ML Ops developer. Review collected by and hosted on G2.com.

Dataiku is excellent for managing the entire data pipeline from data preparation to machine learning and deployment. The best part is it easy to implement. The best part is how it allows both technical and non-technical users to collaborate on the same platform. Visual workflows make it easy to build projects without heavy coding, while advanced users can still dive deep with Python, R, or SQL. The integration with cloud platforms and version control is also very smooth. Review collected by and hosted on G2.com.

The platform can feel heavy for smaller projects, and the initial learning curve is a bit steep for beginners. Also, the licensing costs can be high for small companies or startups. Review collected by and hosted on G2.com.

End-to-end platform: From data ingestion and preparation to model deployment and monitoring, Dataiku covers the entire lifecycle of a data project. This eliminates the need for disparate tools and streamlines the entire workflow.

Collaborative environment: The platform fosters seamless collaboration through shared projects, commenting, and version control. This ensures everyone is on the same page and contributes their unique expertise. My coworkers share their projects with each other and work together on some projects.

Extensive integrations: Dataiku integrates seamlessly with a wide range of databases, cloud platforms, and machine learning libraries. This flexibility allows us to leverage our existing infrastructure and resources. I just learned that a new R library was added recently which could make our life easier on data manipulation.

Robust model management: The platform provides comprehensive tools for tracking model performance, managing versions, and ensuring compliance. This is critical for maintaining the accuracy and reliability of our data-driven decisions. Review collected by and hosted on G2.com.

The running engine could be tricky, there is no one engine can run it all. Sometimes I have to try different engines to make it work. Review collected by and hosted on G2.com.

Dataiku stands out for its ease of use — the drag-and-drop interface combined with the option to code when necessary makes it accessible to a wide range of users. Implementation was smooth and well-supported by the customer success team. I use Dataiku frequently because it offers a vast number of features, from data preparation to model deployment. Integration with other tools like databases, cloud services, and APIs is straightforward, making the platform even more powerful for end-to-end projects. Review collected by and hosted on G2.com.

While overall the platform is excellent, some advanced settings and options could be a bit overwhelming for new users. Additionally, the pricing structure can be a limiting factor for smaller companies or teams. Review collected by and hosted on G2.com.

The plethora of data plug-in sources, tons of options for ready-made recipes (for EDAs, data engineering, & quick-and-dirty analyses). Along with that, we have code-based recipes too to write our own codes. And cherry on top is the collaborative environment without us having to explicitly handle any of that! Review collected by and hosted on G2.com.

Wish there was more customization available to some of the visual recipes. Another thing is version control - although Dataiku does handle version control, it is very non-intuitive and difficult to go back to a previous version, or even understand the changes made between different versions. We need to have commit comments and other git-like features for that to work better. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I love the platform, it's intuitive and very useful. The llm recipes are especially useful. Overall I think its a great platform, it looks great, it makes sense, and it definetely allows me to do my work quicker. Review collected by and hosted on G2.com.

The actual support hasn't always been the best. I've often reached out for support and wasted a lot of time going back and forth without resolving a problem, only to be told that the person trying to help me doesn't know as much on the cloud version of dataiku. The documentation is never cloud-specific too so it's a little confusing. The process through which dataiku have been working out a use case for us has also had some difficulties, Review collected by and hosted on G2.com.

It's hard to highlight a single feature so I will have to mention several:

- The ability to serve different personas, such as "coders" and "clickers" means that Dataiku is well received by non Data Scientists. Yet for those that prefer to code they can code as well.

- The ability to integrate with so many technologies and compute and storage engines both for ingestion and parallel compute means there is no job too big to be done in Dataiku when you use the right technology.

- The Flow makes complex data pipelines simple to understand and design. It also makes it very esy to use.

- The integration of Jupyter Notebooks, built-in Git versioning and Python code environment management makes the creation of new projects and project management very easily.

- And finally I would like to specifically mention their incredible Support team. In my IT career I have dealt with a myriad of enterprise software vendors including all the large ones and I can honestly say that Dataiku Support is the best one I have dealt with by miles. Response speed is amazing even at weekends or out of hours. It's clear they run a 24x7 operation across the globe. The quality and quantity of the responses from Support es exceptional. Even when asking for code snippets to use Dataiku API, which most vendors will normally charge for under professional services, we have been surprised by their willingness to help and always achieved a outcome. Review collected by and hosted on G2.com.

The GUI is inconsistent at times on how certain actions need to be done. While we found Dataiku Support to be exceptional we had less luck in getting new features implemented. Bug fixing has also been slow in our view even though Dataiku has a good release schedule (they usually release a patch release every 2 weeks!). In our view core features and bug fixing should take more priority than LLM features and other new features.

It needs more work to improve ML Ops. For instance model drift is also available via an additional plugin and only on certain algorithms. This should be a core capability. Collaboration could also be improved as there are some concurrency issues that need to be fixed. Review collected by and hosted on G2.com.