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.

In our mid-sized company, we use Dataiku as the central hub for all our machine learning and advanced analytics projects. Before this, our data workflows were a mess of isolated Python scripts and disconnected spreadsheets. Now, we use it to build end-to-end pipelines, specifically focusing on customer churn prediction models and automated inventory forecasting. It acts as the bridge where our data engineering, data science, and business analytics teams actually collaborate on the same exact projects without stepping on each other's toes.As a senior data scientist, the biggest win for me is the flexibility between visual tools and actual coding. I do not want to be boxed into a pure drag-and-drop interface, and Dataiku gets that. I can dive into a code recipe and write custom Python or SQL when I need to do something highly complex, while our business analysts can use the visual recipes to handle basic data joining and cleaning. This hybrid approach saves me countless hours of doing repetitive data prep. I also really appreciate how straightforward it makes model deployment. Pushing a model into production used to require a massive handoff meeting with IT, but now we can package and deploy our models with just a few clicks, making our iteration cycles much faster. Review collected by and hosted on G2.com.

Despite being marketed heavily as a tool for everyone, the learning curve for completely non-technical users is still pretty steep. When I onboard new business analysts, it takes them a solid few weeks to truly get comfortable navigating the flow and understanding the logic of the visual recipes. Another frustration is that the version control, while functional, feels a bit clunky and restrictive if you are used to a traditional Git workflow. Lastly, when you are dealing with very heavy, complex flows with dozens of datasets, the visual interface can sometimes lag or feel a bit cluttered to navigate. Review collected by and hosted on G2.com.

What I like most about Dataiku is how much faster it lets me move on marketing data projects for clients. In my day-to-day work as a digital marketing consultant, I often have to bring together data from multiple sources—CRM exports, campaign performance reports, website analytics, sales data, and sometimes offline datasets. Dataiku gives me a structured environment to clean, connect, and analyze everything in one place, without having to rebuild the entire process from scratch each time.

I use the visual workflows regularly because they make the end-to-end process far more transparent. Rather than working only in spreadsheets or in isolated scripts, I can see every step of the data preparation flow and explain it clearly to clients or internal teams. This is especially helpful when I need to show exactly how a lead scoring model, a customer segmentation analysis, or a campaign performance dataset was created.

Another thing I really value is the balance between no-code and code options. For everyday consulting work, it’s practical: I can move quickly with visual recipes for common tasks, and then go deeper with SQL or Python when the analysis needs more flexibility. That saves time and makes it easier to adapt the workflow to the complexity of each project.

Dataiku also improves collaboration with non-technical stakeholders. When I’m working with marketing managers or sales teams, they don’t always need the technical details, but they do need to trust the output. Having a clear, documented workflow makes conversations smoother and helps translate analysis into concrete marketing decisions.

Overall, the biggest benefit for me is that Dataiku turns complex data preparation and analysis into a repeatable consulting workflow. It helps me spend less time on manual data cleaning and more time interpreting results, spotting opportunities, and recommending actions to improve campaign performance, customer targeting, and ROI. Review collected by and hosted on G2.com.

What I dislike about Dataiku is that it can feel a bit heavy at the beginning, especially if the team is not already familiar with data workflows, data preparation logic or machine learning concepts. As a consultant, I can usually navigate the platform quite well, but when I involve clients or marketing teams who are less technical, there is sometimes a learning curve before they feel comfortable using it independently.

The pricing can also be a limitation, especially for smaller clients or companies that are still at an early stage in their data maturity. Dataiku can deliver strong value when it is used regularly across multiple projects, teams and data sources, but for a smaller marketing team that only needs occasional analysis, it may feel like a significant investment. The ROI is much clearer when the company is ready to operationalize data workflows, not just run one-off reports.

In terms of onboarding, I think the platform requires a structured introduction to get the most out of it. There are many features, which is a strength, but it can also be overwhelming at first. For some clients, I need to spend extra time explaining not only how the tool works, but also how to think in terms of reusable data pipelines instead of simple spreadsheet-based analysis.

Regarding AI and machine learning, the capabilities are powerful, but they still require good data quality and a clear business objective. Dataiku can help a lot with automation and predictive models, but it does not replace the strategic work of defining the right question, selecting the right variables and interpreting the results correctly. In my daily work, I still need to guide clients carefully so they do not treat AI outputs as automatic answers without proper validation.

So overall, my main dislike is not about a single missing feature, but about the complexity that comes with such a complete platform. It is very useful, but it needs the right level of adoption, training and business commitment to fully justify the investment. Review collected by and hosted on G2.com.

I like how intuitive Dataiku is, how easy it is to learn to use, and the fluidity of the software. It makes pipelines much more understandable, facilitating their creation and maintenance. I also like that the changes I make mostly propagate throughout the flow; for example, if I change the name of a table, the recipe that uses it updates automatically. Additionally, if I connect a table somewhere else in the flow, the flow adjusts automatically to maintain a certain order. Review collected by and hosted on G2.com.

I think the speed. Many times I have to wait a long time for a simple recipe to even start executing. 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.

I love the parameter analyzer tool, we use it as a first step in problem solving to help narrow down root cause analysis Review collected by and hosted on G2.com.

I would like there to be a global env for secrets, and more inter connectivity between separate instances. If you needed to use something on one instance on another instance it’s a pain to use the api to attempt to retrieve secrets Review collected by and hosted on G2.com.

I really like how Dataiku brings everything together in one place. It makes my workflow feel more organized and less scattered, which helps me stay on track. That said, there are times when it can feel a bit overwhelming, especially with so much in one interface, but overall it still makes my work easier. Review collected by and hosted on G2.com.

For me, the biggest downside is that it doesn’t always feel as intuitive as I’d like, especially once I get into the more advanced parts. At times, I end up spending more time trying to figure out how to do something than actually doing it, and that can be pretty frustrating. Review collected by and hosted on G2.com.

I really appreciate the AI success with Dataiku. It's something I'm genuinely interested in, and it has been a great experience. The tool resolved our data governance issues, made our AI projects successful, and its orchestration along with people integration into our data systems has been great. The initial setup was pretty easy, thanks to a support team that helped us. Overall, my experience with Dataiku has been very positive, and I would definitely recommend it. Review collected by and hosted on G2.com.

I think we can add agent tech and expand it to white coding. Also, adding some data intelligence BI would be beneficial. Review collected by and hosted on G2.com.

I think the user interface of Dataiku is very user-friendly. Even if you don't have a strong data science or data engineering background, you can still use it by drawing boxes, which makes it accessible for many people. I also like that you can customize your solutions by writing your own code to cater to specific business needs. Additionally, with its fast-paced development, Dataiku regularly updates and upgrades the system to include the latest AI features, which I find awesome. The graphical, no-code environment significantly reduces my development life cycle, saving at least 50% of my time. It also makes interaction with end users easy because they can access our development environment to see progress and give quick feedback. Review collected by and hosted on G2.com.

So first of all, I think I got some limitation that you to be honest with you, because let's say, if you want to display and visualize a large dataset, it always has some limitation. And, also, I find out the dashboard in built by the API is not super fancy and super user friendly. Comparing to Power BI or the other visualization tools like Tableau, I think that's something that you can improve as well. Other main pinpoint for us is about the deployment. Because, you need to link to the different development, the requirements, how to deploy our AI solution, particularly to another cloud form. For example, AWS Azure, I think that we need a little bit more support on this. Review collected by and hosted on G2.com.

I find Dataiku to be incredibly user-friendly, which is essential for my work in building apps and cleaning data. I really enjoy the academy because the lessons are so easy to follow, making learning a breeze. The web apps are a fantastic feature for me too; I love that I can create apps without needing to purchase third-party software. The initial setup for our team was also very easy and only took a few months. Review collected by and hosted on G2.com.

Needs a dark mode Review collected by and hosted on G2.com.