Dataiku for Enterprise AI

What do you like best about the product?

I like that it keeps AI systems reliable, scalable, and seamlessly connected to the data they need to perform. It significantly improved collaboration between technical and business users, and it reduced tool fragmentation by bringing more of the AI lifecycle into one platform, making workflows easier to manage and coordinate. The governance capabilities also helped with centralized governance, documentation, and controls to keep AI usage organized and accountable.

What do you dislike about the product?

High costs, complex multi-cloud setups, and overly complicated access controls can make AI expensive, difficult to manage, and harder to secure. Also, the AI sometimes misinterprets the data, so I still need to verify its recommendations before making decisions.

What problems is the product solving and how is that benefiting you?

I’ve used the AI to quickly analyze campaign performance and identify trends. It also made workflows easier to manage and coordinate by reducing tool fragmentation, and it significantly improved collaboration. Governance and documentation provided enough oversight to keep AI usage organized and accountable.

What do you like best about the product?

No-code or low-code visual flows that are easy to connect to native servers.

What do you dislike about the product?

The license cost is expensive, and it also requires heavy infrastructure to handle heavy flows.

What problems is the product solving and how is that benefiting you?

Operational tracking and clinical trial analytics.

What do you like best about the product?

What I like most about Dataiku is that it combines data preparation, analysis, visualization, and machine learning in a single platform. The visual workflow makes it easy to build and follow data pipelines without needing to write code for every step, while still offering flexibility for people who prefer working in Python or SQL. I also find its integrations with different data sources helpful, and the interface makes it straightforward to collaborate with others and share workflows. Overall, it streamlines the data science process and reduces the need to switch between multiple tools.

What do you dislike about the product?

One area that could be improved is the learning curve for some of the more advanced features. Although the visual interface is helpful, it can still take new users a while to understand how the different components and workflows fit together. Some of the advanced integrations and AI features would also benefit from clearer documentation, along with more beginner-friendly examples that show how to use them in practice. Performance can occasionally vary depending on the complexity and size of a workflow, so more guidance on optimizing larger projects would be useful as well.

What problems is the product solving and how is that benefiting you?

Dataiku simplifies the end-to-end data science workflow by bringing data preparation, analysis, visualization, and machine learning into a single environment. Rather than switching between multiple tools at different stages of a project, I can keep workflows and experiments organized in one place. The visual interface is especially helpful for quickly exploring data and building workflows, and the option to use Python or SQL adds flexibility when I need more advanced analysis. Overall, it’s easier to experiment, reproduce workflows, and collaborate on data-driven projects.

What do you like best about the product?

The data ingestion, analysis, and interactive dashboard for visualising the data and presenting it to stakeholders are amazing. The value for money is great, so many data analysis capabilities made Dataiku a perfect solution fit for our problem.

What do you dislike about the product?

I tried integrating the Dataiku APIs into my Python project. It works flawlessly, but it still needs some improvements, and it isn’t that flexible.

What problems is the product solving and how is that benefiting you?

For my organization, Dataiku has helped us analyze well log data, and our decision-making has become much quicker. Reservoir pressure data analysis also helps onsite engineers make faster decisions.

What do you like best about the product?

What I like most about Dataiku is how straightforward it makes working with data and putting together machine learning workflows. After using it for a week, I found the visual interface particularly helpful because it let me explore, clean, and transform data without needing to write code at every step. At the same time, the option to switch to Python and SQL when needed adds a lot of flexibility. Overall, it feels like a solid platform that brings data preparation, analysis, and machine learning together in one place.

What do you dislike about the product?

What I like least about Dataiku is that it can feel a bit overwhelming at first. It offers a lot of features and options, so it takes time to get comfortable with the interface and to figure out the workflow that makes the most sense. For beginners, some tasks can also seem more complicated than they need to be. After using it for a week, I still think it has a lot of potential, but I wish the initial learning curve were smoother.

What problems is the product solving and how is that benefiting you?

Dataiku helps simplify the process of preparing, analyzing, and working with data by bringing everything into one platform. Instead of switching between different tools, I can manage data, create workflows, and experiment with machine learning in one place. This saves time and makes the overall data workflow more organized and easier to manage.

What do you like best about the product?

I like the fact that everything is in one platform.

What do you dislike about the product?

Advanced development requires a lot of learning, and there’s quite a bit to pick up before you feel comfortable with it.

What problems is the product solving and how is that benefiting you?

It solved the problem of fragmented data science workflows by bringing data preparation, analytics, machine learning, deployment, and governance together in one place.

What do you like best about the product?

I mainly use Dataiku for data preparation, reporting, and automation. I love the visual workflow as it shows how data moves from the source through various transformations and outputs, making it easier to understand and maintain. The prepare and join features are really useful for cleaning and combining data without having to write everything from scratch. I also appreciate being able to use Python for more flexibility. Dataiku significantly reduces my manual data work by allowing me to build a process once and reuse it, making workflows easier to track and troubleshoot. The initial setup was fairly easy, and once familiar with the interface and flow structure, it was straightforward to create datasets, build workflows, and start working with the data.

What do you dislike about the product?

I find some workflows can get a bit complex as the project grows, and troubleshooting errors isn't always straightforward. I also notice that performance can slow down with larger datasets, and it sometimes takes a little time to figure out exactly where an issue is coming from.

What problems is the product solving and how is that benefiting you?

I use Dataiku for data preparation, reporting, and automation. It reduces manual data work by letting me build reusable workflows, making it easy to track and troubleshoot data changes instead of doing everything manually in Excel or Python.

What do you like best about the product?

The interface is lightweight and enables me to quickly see the entire process (even the big ones). It allows me to connect to many data sources, from many distinct providers, having an unified repo for all the external connections. The client/designer performance is really cool, since it runs on the web/cloud, so it don't require a lot of resources from my machine, even when I am processing million of rows. The license pays itself after a few workflows, since tasks that usually would take weeks to be developed (or executed by an analist), could be deployed on production in just a few days. The Data Science team of the company uses it for forecasting, running LLM models, Machine Learning and all the cool stuff. I use it for data engineering and for running python code in the between, and it is really cool! I would recommend it for anyone. There are many tutorials on the platform, with starting demo projects that in a few hours you 'll feel empowered, or invited, to start your own projects.

What do you dislike about the product?

The interface for selecting fields of a datasource or maybe creating calculated fields should be simpler.

What problems is the product solving and how is that benefiting you?

It helps me validating ideas and creating data pipelines with just a few clicks. Developing all of that stuff by hand coded solutions would take 10x more time.

What do you like best about the product?

I like that Dataiku is intuitive. What I appreciate the most is when I conduct an experiment, whether testing different machine learning models at the same time, it offers the results in a simple and visual way. This provides an understanding of how the models are behaving and how well they performed.

What do you dislike about the product?

I find it complex to perform joins because first I have to change the data types to strings in order to join them, when I should be able to join the data if they are of the same type. It would also be good to have an option that allows joining all fields with the same names and another option to join by position. Additionally, it should allow joining by multiple data sources.

What problems is the product solving and how is that benefiting you?

With Dataiku, I centralize different data sources into a single tool, allowing me to work with Big Data quickly and perform effective ETL processes. It also helps me with data quality in migration flows.

What do you like best about the product?

It’s very user-friendly. New members of our team can quickly get started, whether they’re picking up pre-existing projects or creating their own. I also really like that you can mix different coding languages within the same flow.

Another strong point for me is the range of integrations with most major platforms: Databricks, AWS, Teradata, SharePoint, etc.

Performance-wise, it’s good. It’s not the best, although that may be related to our current cluster configuration. I’ve also noticed that training ML models can sometimes fail for different reasons, and it can be a bit daunting to figure out exactly why and debug the issue.

Dataiku support is quite good. I wasn’t a fan of customer support being limited to email, but I have to say the responses are fast and the attention has been solid.

Pricing is also quite competitive. Fixed pricing tied to licenses works well for our team.

Finally, I’ve been enjoying the latest AI features they’ve added. Being able to easily describe recipes or generate documentation is definitely a plus.

What do you dislike about the product?

Data prep tools can sometimes be too slow to run, which can be frustrating. This is a problem we haven’t seen with competitors like Alteryx. Also, training ML models sometimes fails even with a reasonable number of samples, and the behavior feels a bit inconsistent in that regard.

What problems is the product solving and how is that benefiting you?

I mostly work on ML projects—forecasting, classifiers, and similar tasks—especially when they’re tied to commercial problems. It's also pretty decent at version control amongst the team.