Dataiku Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

What I like most about Dataiku is its user-friendly interface and strong collaboration features. It makes it easy for data scientists, analysts, and engineers to work together on the same projects. I also appreciate that it supports the full data lifecycle, from data preparation to machine learning and deployment. Review collected by and hosted on G2.com.

One thing I dislike about Dataiku is that it can be quite demanding on system resources, especially when I’m working with large datasets. In addition, some of the more advanced features come with a learning curve, so it can take time to fully understand how to use them effectively. Review collected by and hosted on G2.com.

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.

Its greatest strength is enabling true collaboration between data experts and business teams on a single platform. It seamlessly bridges technical work like coding and ML engineering with visual and no-code interfaces. This breaks down silos, accelerates project delivery and ensures AI solutions are built with crucial business context, making them more impactful and sustainable. Review collected by and hosted on G2.com.

For smaller teams or simpler projects, Dataiku will be premium. The platform's extensive features come with inherent complexity, which can lead to a steeper learning curve. Its pricing model is often seen as enterprise-focused, potentially making it less accessible for startups or individual users who don't need its full collaborative scale. 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.

I like that Dataiku lets me handle data projects and build machine learning models by pulling in data from different sources, cleaning and organizing it, and experimenting with models all in one place. The combination of a visual interface with coding options makes it accessible for both technical and non-technical team members, smoothing out data project management. I love how it reduces repetitive tasks, decreases mistakes, and keeps complex projects organized and running smoothly. It's great that everyone on the team can contribute, no matter their technical skills, making data work easier and less stressful. Review collected by and hosted on G2.com.

One thing I’ve noticed about Dataiku is that it can feel a bit overwhelming at first because there are so many features and options. Working with really large datasets or complex workflows can sometimes be a little slow. I also think it could be a bit easier for new users to get started. Overall, it’s a great tool, but a little more guidance and smoother performance would make it even better. 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.