Dataiku Reviews

“no-code workflows”

Pros

One of the strongest parts of the software is its no code capability which really helps team members who aren't comfortable with programming. It also supports Python and R libraries along with workflow playbooks which makes it useful for a wide range of users.

Cons

The onboarding is still quite expensive.

“Evolving features”

Pros

As a junior data analyst I've seen the tool completely change the way I build analytics projects from start to finish. The visual recipes made the whole workflow much more approachable at first and as I moved into more complex projects and grew my data science knowledge within my domain, I started uncovering just how broad its capabilities really are through its tools and APIs. It has played a big role in helping me move faster toward my career goals. Another thing I really appreciate is how regularly the platform keeps improving with new features and tools such as Data Quality Management, LLM Mesh and Agentic AI in the studio. Those additions motivate me to experiment and add extra steps into the ML flow that can create more business value in the projects I work on. I also joined its Academy which has been a nice extension of the learning experience.

Cons

While it is excellent for getting started especially as a beginner, once projects become more advanced some gaps start to show. As I began working more with the Python APIs I noticed that the documentation often lacks the level of detail I need. I would really like to see the Python API docs become clearer and more complete, similar to what you get with libraries like PyTorch, Scikit-learn, SciPy or Plotly. It would be much more helpful if each function included fuller parameter listings, explanations of optional arguments and a couple of practical examples showing what each parameter does and how to use it effectively. Right now the available documentation feels too limited to fully understand the range of what a specific function or API can do. Because of that I often end up relying on blog posts or answers shared by their team members. I say this with genuine appreciation for the product but stronger documentation would make a real difference especially for more experienced ML Ops developers.

“accessible tool”

Pros

Using the tool feels very accessible and it brings together a lot of features that remind me of a mix of SQL and Excel.

Cons

Things can get tricky when you want to implement more advanced tools like Python directly in the flow.

“Better risk and opportunity insights”

Pros

It really simplifies our daily work and helps us spot both opportunities and potential risks more effectively.

Cons

One thing that could be better is the limited information provided when new updates are released.

“Fast support”

Pros

Getting started with the software didn't take long at all. The interface is user friendly so learning the core concepts and building my own flow felt pretty easy. Also the customer support team is very responsive and answers my questions quickly.

Cons

It already keeps up well with current AI trends but it would be even better if it added integration between Copilot and VS Code allowing AI generated code to be created more smoothly.

“seems promising”

Pros

Demos did a solid job of showcasing the softwares approach to Agentic AI and it came across as a forward looking direction.

Cons

Something that felt missing was hands on labs on the website. For developers being able to try the Agentic approach directly would be really useful for spotting opportunities and areas for innovation.

“quick and efficient workflows”

Pros

The click and go approach makes workflows fast and efficient to handle.

Cons

I'd like the ability to extract the code behind the click-and-go operations we perform.

“Good experience”

Pros

Interface feels very accessible and setting things up to build intelligent models is easy.

Cons

At times it just doesn't work properly when the models are running.

“responsive support”

Pros

Being able to combine custom code with the platform's built In features is a big advantage and helps improve the final results. Another strong point is how quickly Customer Support responds whenever questions or blockers come up.

Cons

A downside is that several built in recipes rely on inferred data types which can create downstream problems when the detected type doesn't match the original data.

“everything in one place!”

Pros

Having everything in one platform really helps. It supports the full data project lifecycle from ingestion and preparation all the way to deployment and monitoring so we do not have to jump between a bunch of separate tools. It also works well for team collaboration with shared projects, comments and version control which makes it much easier for coworkers to stay aligned and contribute to the same work. The integration options are another big plus since it connects with many databases, cloud platforms and machine learning libraries, letting us keep using the infrastructure we already have. I also recently noticed a new R library was added which should be very helpful for data manipulation. Other than that the model management features are very solid with tools for performance tracking, versioning and compliance all of which are important for keeping our models reliable.

Cons

Some frustrations arise because of the running engine. There is not a single engine that handles everything so at times I have to test a few different ones before I can get something to run properly.

“Big data handling is amazing!”

Pros

Working with big data sets feels very manageable and the low code setup is also really convenient to use.

Cons

There's not much to complain about though I would appreciate a little more information when new updates are released.

“works really well!”

Pros

It's flexible and works well regardless of the supporting technologies around it which makes it a good fit in different environments.

Cons

One problem is that the tool still doesn't have the level of recognition it deserves across the broader tech industry.

“friendly visuals for analysts”

Pros

From the start the platform feels very user friendly. The data visualizations are easy to choose from and apply to the data which makes the whole process convenient.

Cons

That said while it works well for beginner data analysts, it's still obvious that having a basic foundation in programming and data analytics is important if you want to get the most out of the product.

“helpful learning resources”

Pros

The software is very user friendly and the learning materials are excellent.

Cons

A few parts of the interface feel a little unaccessible at first but they can be picked up quickly.

“Solid features”

Pros

Right away the tool felt approachable because the drag and drop interface makes a lot of tasks simple while still giving you the option to code whenever needed. That balance makes it useful for a wide range of users. The rollout went well too and the customer success team was very supportive throughout the onboarding. I use this tool regularly since it covers so much from data preparation all the way to model deployment. It also connects well with databases, cloud platforms and APIs which makes it a strong solution for end to end projects.

Cons

Overall the platform is excellent but some of the more advanced settings and options can feel a little overwhelming when you're new to it. On top of that the pricing model may be a challenge for smaller companies or teams with tighter budgets.

“a lot of good features”

Pros

Between the wide range of data source plug ins and the huge selection of built in recipes for EDA, data engineering and quick one off analysis, there's a lot to work with. On top of that it also includes code based recipes when we want to build our own logic. The collaborative setup is a big bonus too since it works well without us needing to manually manage that side of things.

Cons

I do wish some of the visual recipes offered more room for customization. Version control is another weak spot. Even though the tool supports it the workflow feels pretty unaccessible and it's hard to return to an older version or clearly see what changed between versions. It would be much better with commit comments and more git style features.

“AI learning hub”

Pros

A really solid place to learn about AI and quickly get up to speed on how to bring it into your tech environment.

Cons

For now I haven't run into anything I dislike.

“Visual workflows make things easier”

Pros

The web platform really lowers the barrier for people who might otherwise hesitate to start exploring. The visual recipes are also a big plus because they make it much easier to understand what's happening at each step.

Cons

Something that could be better is staying organized when moving from one project to another.

“ML powerhouse”

Pros

From turning silver layer data into gold to building and deploying projects this has been the best AI/ML engine I've used all within one centralized platform.

Cons

I honestly don't have any complaints, I love everything it offers!