Dataiku Reviews

“all-in-one workflow boost”

Pros

Having this tool in my stack keeps everything in one place which makes my workflow feel much more organized and a lot less scattered. It helps me stay focused and on track throughout the day. There are definitely moments when the interface feels a little overwhelming because it packs so much into one space but overall it still makes getting my work done easier.

Cons

The toughest part is that it doesn't always feel as accessible as I want it to especially when I move into the more advanced features. Sometimes I spend more time figuring out how to do something than actually doing the task itself and that gets frustrating pretty quickly.

“Helpful templates for faster rollouts”

Pros

The ready made templates help speed up applications and make it quicker to get projects moving.

Cons

One downside is that the templates don't show the underlying code so it can be difficult at times to understand how certain features actually work especially since the documentation doesn't go into enough detail.

“powerful integration options”

Pros

Really stands out for how powerful all the integrations you can add are.

Cons

I'm not actually a user of this tool but I do have an interest in it.

“easy to use”

Pros

The recipes are easy to use and setting up scenarios feels simple and uncomplicated.

Cons

There is not really anything I dislike but I would love a more beginner friendly way to work with AI along with step by step tutorials.

“time saving”

Pros

Working with this software makes analyzing data and reviewing results much more easy and it saves a lot of time compared with doing everything in Python inside a traditional IDE.

Cons

Lately though adding input files to managed folders has been frustrating and time consuming. I wasn't able to upload subfolders for example and when I uploaded a large batch of files some of them were missing.

“flexible no-code AI platform”

Pros

Something I really appreciate about the software is how approachable the interface is. Even without a deep background in data science or data engineering, it's still possible to build workflows just by connecting boxes which makes the platform accessible to a much wider group of users. At the same time it doesn't feel limiting because you can still customize solutions with your own code when there are specific business requirements to address. Another big plus is how quickly the product evolves. It keeps releasing updates and adding newer AI capabilities which is a huge benefit. The graphical no code environment has also cut down my development cycle a lot easily saving me around 50% of the time. On top of that collaboration with end users is much simpler since they can enter the development environment, follow the progress and share feedback quickly.

Cons

There are still a few limitations that stand out. For example when it comes to displaying and visualizing very large datasets, the platform seems to hit some constraints. The built in dashboards available through the API also don't feel especially polished or very user friendly. When compared with tools like Power BI or Tableau the visualization side definitely seems like an area that could be improved. Another challenge for us has been deployment. Connecting different development requirements and figuring out how to deploy AI solutions especially to other cloud platforms such as AWS or Azure could use more support and clearer guidance.

“Visual workflows with custom options”

Pros

Working with this software makes data analysis feel much more visual and a lot less frustrating. I really appreciate how flexible the platform is especially being able to host custom Python web apps, use Python filters, build custom pipelines and set up custom scenarios. After going through the training the initial setup was very easy.

Cons

Managing code for web apps can be difficult since everything has to be handled in one large file and the limited Python API calls feel restrictive.

“user friendly”

Pros

Platform is very easy to use and works well across a wide range of use cases.

Cons

There really isn't much I dislike about it at this point so nothing specific to mention here.

“Good performance”

Pros

The visual recipes are a real highlight and the platform is very user friendly overall.

Cons

I don't exactly like that insights can sometimes be just a point in time snapshot instead of something reusable later.

“complete analytics powerhouse”

Pros

Having the full data workflow in one platform is the biggest advantage of the software. Teams can prepare data, create machine learning models and deploy them without bouncing between different tools. The visual interface makes data pipelines much clearer to follow and when more flexibility is needed advanced users can still jump into code. That mix of visual features and coding options makes it much easier for data scientists, analysts and engineers to work together. It helps teams go from raw data to useful insights and production ready models much faster.

Cons

It can feel somewhat heavy and complicated especially when you're dealing with very large datasets or a lot of workflows at once. There are times when the interface slows down and keeping track of multiple projects can become a little messy. The visual tools are useful but some advanced customization still depends on coding which can be tough for non technical users. In the end it's a powerful platform but getting comfortable with it does take some time.

“Great platform!”

Pros

Right away this tool feels very approachable thanks to its user friendly interface and solid collaboration features. It works really well for data scientists, analysts and engineers who need to contribute to the same projects together. Another thing I appreciate is that it covers the entire data lifecycle from data preparation and machine learning all the way through to deployment.

Cons

One downside is that it can be fairly heavy on system resources especially when working with large datasets. On top of that some of its more advanced capabilities take time to learn so getting fully comfortable with using them effectively can require a bit of patience.

“collaborative ai powerhouse”

Pros

One of its biggest advantages is how well it brings data specialists and business teams together in one shared platform. It connects highly technical work like coding and machine learning engineering with visual tools and no code options in a very natural way. That helps remove the usual gaps between teams, speeds up project delivery and makes sure AI solutions are built with the business context they actually need which makes the results more valuable and easier to sustain over time.

Cons

That said the tool can feel like a premium option for smaller teams or more easy projects. Because the platform includes such a broad set of capabilities it also comes with a certain level of complexity that requires some time to get used to. The pricing is often geared more toward enterprise customers so it may be harder to justify for startups or individual users who don't need collaboration at that scale.

“fitting for team analytics”

Pros

Mananaging data projects and building machine learning models from start to finish is much easier with the needed features in one place. I can bring in data from multiple sources, clean and structure it and test different models without constantly switching tools. The mix of a visual interface and coding flexibility works really well because both technical and non technical teammates can take part. It cuts down on repetitive work, helps prevent errors and keeps even complicated projects organized and moving forward. I also appreciate that the whole team can contribute regardless of skill level which makes data work feel more manageable and far less stressful.

Cons

At the beginning it can feel a little intimidating simply because it offers so many features and settings. When dealing with very large datasets or more complex workflows, performance can occasionally slow down. It also seems like the onboarding experience could be more beginner-friendly for new users. Even with that it's still a very strong tool and a bit more guidance along with better performance would make it even more valuable.

“very organized”

Pros

Using the software feels very comfortable because the graphical interface does a nice job of organizing paths and threads. Being able to handle code and datasets visually in one place is something I really appreciate and the automatic alignment of everything makes the whole workflow feel calm and well structured.

Cons

I don't have any major complaints about it. If I had to point to something that could be improved it would be the way history and recently used code are managed. It would be much better if that information were easier to find and more clearly highlighted visually.

“quick MVP prototyping boost”

Pros

Getting a prototype or MVP out the door feels much faster with this platform because it's very easy to use. The plug and play setup also helps a lot so putting things together and moving quickly is pretty painless.

Cons

The documentation feels a bit lacking and there aren't many tutorials to rely on. When I run into issues and need guidance, tracking down the right answer can be frustrating.

“worth it!!”

Pros

It's mostly driven by AI so there isn't much manual work to do which makes the whole experience really convenient.

Cons

Honestly I don't have any real complaints. It's a really solid app overall though it can be a bit expensive. That said it still feels worth the price.

“flexible platform”

Pros

Using this software has been easy and it scales well as needs grow. It's flexible and fits nicely into almost any infrastructure which makes adoption much easier.

Cons

The downsides are mostly the cost some scalability limits, integration complexity, occasional performance concerns and the fact that users need training to get the most out of it.

“excellent results”

Pros

From data prep to machine learning and deployment the tool does an excellent job of covering the full data pipeline. One of the biggest advantages is that it's easy to get up and running. It also does a really good job of bringing technical and non-technical users together on the same platform. The visual workflows make project building approachable without requiring a lot of coding while more advanced users can still work deeply with Python, R or SQL. Its integration with cloud platforms and version control also works very well.

Cons

It can feel a bit heavy if you're only using it for smaller projects and beginners may need some time to get past the initial difficulty. On top of that the licensing can be expensive for small businesses or startups.

“all data teams in one place”

Pros

Having analysts, engineers and data scientists working together on a single platform is a big plus. It really helps bring all the data people into one shared space.

Cons

A drawback is that it can feel a little heavy and slow especially on bigger projects with lots of visual recipes or datasets.

“Collaboration features shine”

Pros

Between the strong version control, shared project spaces and role based permissions collaboration works really well across both data teams and business users.

Cons

Some of the more advanced capabilities are locked behind higher tier or enterprise plans which can be hard to justify budget wise for smaller teams.

“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!