Having a single pane of glass for data and AI agents, with built-in observability, makes it much easier to monitor everything in one place. Review collected by and hosted on G2.com.

It could be an overhead to maintain, especially if the customer is already migrating to Databricks. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Webapp code management is challenging because it involves working with one big file, and the limited Python API calls are restrictive. Review collected by and hosted on G2.com.

I appreciate Dataiku for its interface and ease of use, making the initial setup very easy. I also found the low code data pipeline setup beneficial when I used it in my past job. Review collected by and hosted on G2.com.

Customized pipelines or use cases, like dealing with semi-structured or unstructured data, are hard to tackle. Review collected by and hosted on G2.com.

The interface allows people with little experience to get up to speed and to start to deliver quickly. Complex solutions and capabilities are there, but are also insulated from over whelmed new users. Review collected by and hosted on G2.com.

The data iku environment is very self contained and works seamlessly with its own components, however we struggled when we wanted to integrate with some of our other existing orchestration tools. 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.

Fast connection and migration of datasources Review collected by and hosted on G2.com.

complicated to lear recipes for a person without coding experience Review collected by and hosted on G2.com.

Having easy to use recipes with an easy and simple way to setup scenarios Review collected by and hosted on G2.com.

It’s not that I dislike this, but I want to have easier tool to use AI with step by step tutorials Review collected by and hosted on G2.com.

Automation, data, AI, cataloging, governance Review collected by and hosted on G2.com.

Takes time for non tech user to learn and adapt. Help is not great. Documentation is basic. Review collected by and hosted on G2.com.

It’s very intuitive UI and recipes are easy to understand Review collected by and hosted on G2.com.

Should be integrated with DG tools well so they can be easily visualized within 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.