Dataiku Reviews & Product Details

Dataiku demonstrates a satisfactory environment where data is centralized and organized

The program supports both coders and non coders, allowing them to use data in their different levels

Dataiku has a successful data lifecycle, something that collects, ingest, prepare and even analyze data

The program consists of an inbuilt Auto ML tools that speed u most of the operations

Dataiku has extensible APIs and plugins, all supporting success integrations Review collected by and hosted on G2.com.

Dataiku has challenges in cost management and estimating, where small companies fail to secure the app

The app demands extensive computer resources, something that amplifies the infrastructure costs Review collected by and hosted on G2.com.

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.

Dataiku streamlines building, training, and deploying AI models at scale, offering end-to-end automation, seamless orchestration of compute resources, and collaborative pipelines that make generative AI projects faster and more reliable. It unifies the entire data workflow from preparation and analysis to model deployment, into a single, collaborative platform that’s surprisingly intuitive for both data scientists and business users. Review collected by and hosted on G2.com.

Full capabilities can be overwhelming for new users, and scaling extremely large generative AI models sometimes requires complex customization. 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.