Dataiku for Enterprise AI

What do you like best about the product?

What I like most about Dataiku is its ability to bring data preparation, analytics, machine learning, and deployment into a single collaborative platform. It enables both technical and non-technical users to work together, making it easier to build end-to-end data and AI workflows. Visual, low-code interface for building data pipelines and machine learning workflows. Support for Python, SQL, and R, allowing advanced users to customize projects when needed. Strong collaboration features with versioning and project sharing. Seamless integration with databases, cloud platforms, and big data technologies. Built-in tools for model deployment, monitoring, and governance. For me, the most valuable feature is the combination of visual workflows and code-based flexibility. I can quickly prototype data pipelines visually while still using code for advanced transformations or custom machine learning logic. The biggest benefit is improved productivity. Dataiku reduces the time needed to prepare data, develop models, and deploy AI solutions, while enabling better collaboration between data scientists, analysts, and business teams.

What do you dislike about the product?

The biggest drawback is the complexity of large projects. As workflows grow, managing dependencies, pipelines, and multiple collaborators can become challenging without careful project organization. Complex projects with many datasets and workflows can become difficult to organize and navigate. Some advanced capabilities require a solid understanding of data engineering or machine learning concepts.

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

Dataiku solves the challenge of managing the entire data and machine learning lifecycle in one place. Instead of relying on separate tools for data preparation, model development, deployment, and monitoring, Dataiku provides a unified platform that enables teams to collaborate more efficiently. Simplifies data preparation and transformation through visual workflows. Centralizes analytics, machine learning, and model deployment in a single platform. Enables collaboration between data scientists, analysts, engineers, and business users. Integrates with cloud platforms, databases, and big data ecosystems. Supports governance, version control, and monitoring for production AI models. In my workflow, Dataiku helps accelerate data analysis and machine learning projects by reducing the effort needed to build pipelines and manage data. Its visual interface allows quick prototyping, while the ability to use Python and SQL provides the flexibility needed for more advanced use cases. The biggest benefit is improved efficiency and collaboration. Dataiku reduces the time required to move from raw data to production-ready insights, enabling teams to deliver analytics and AI solutions faster while maintaining better governance and reproducibility.

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.

What do you like best about the product?

What I like the most about Dataiku is that it is mostly a no-code platform, and allows technical, and non-technical users to collaborate in an easy way. It is extremely easy to share workbooks between coworkers, easy to set-up (using the web version with any browser - Edge, Chrome, etc.).

The visual recipes make it easy to understand the flow of the pipeline, while also having the flexibility of adding Python or SQL when necessary. For data preparation, automation, and building repeatable workflows, I can say it's the best ETL platform I have used. We use Alteryx in our company, but we are starting to implement our workflows and apps inside Dataiku instead of Alteryx.

What do you dislike about the product?

The main thing I dislike about Dataiku is that the learning curve can be a little steep at the beginning. There are many features, menus, recipes, and project settings available, so it can feel overwhelming until you understand how everything is organized.

Some tasks that seem simple at first may require learning the Dataiku specific way of doing things, especially around flows, datasets, automation, and deployment. Once you get more familiar with the platform, it becomes much easier to use, but the onboarding phase could be smoother with more user-friendly examples and tutorials.

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

Dataiku helps solve the business problem of automating data workflows that would otherwise require manual work across different tools. This is useful when working with APIs, because we can extract data from external or internal systems, transform it, and schedule the process to run automatically.

In the airline industry, having a platform with schedulers is extremely necessary. Many processes depend on updated data, fixed execution times, and reliable automation. Dataiku makes it easier to organize these workflows in one place, reduce manual steps, and monitor the process when something fails.

What do you like best about the product?

Fast connection and migration of datasources

What do you dislike about the product?

complicated to lear recipes for a person without coding experience

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

is fast to build ML models

What do you like best about the product?

As a data analyst, it makes it easier to automate repetitive data processes and workflows. I also like the visual recipes/workflows. But I am looking forward to all the upcoming features as mentioned in the enablement session.

What do you dislike about the product?

A couple of downsides I’ve noticed with Dataiku are the lack of Gen AI integration. I understand it will be added soon, and I’m looking forward to that, but it’s still a gap for now.

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

As someone who works at a bank, a lot of my analysis involves financial data, especially in fraud management. Dataiku helps me work through and analyze fraud-related data, including calculating the impact and benefits of our data science models.

What do you like best about the product?

low-code option that is visually appealing that has easy collaboration between various teams of different technical levels

What do you dislike about the product?

it takes a while to get used to the UI and find due to some auto documentation can be misleading

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

Dataiku was a possible solution for our analytics migration out of Alteryx

What do you like best about the product?

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.

What do you dislike about the product?

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

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

Dataiku makes it easy to create and maintain pipelines, making them more understandable. It is intuitive and allows automatic changes in the flow, which improves organization.

What do you like best about the product?

I really like Dataiku's graphical interface. I'm a huge fan of that visual flow showing how joins happen and where data is moving. As a visual person, it helps me get a better map of our complex projects, making it easy to understand what I'm doing and where I'm going. I think the ease of setup was impressive too. I don't recall the implementation being a headache at all; it was pretty straightforward connecting to our Databricks. We love how easy it is to work with different data types in Dataiku. I also enjoy cleaning data there when I get the chance, even though I'm often in Databricks. We love how much our team enjoys using Dataiku, and we're really happy customers. The tag-ups and consulting services have always been really on point and helpful for us. Due to the enthusiasm at Santee Cooper, we've expanded our Dataiku licenses significantly.

What do you dislike about the product?

Sometimes working with your own custom code can be challenging. I will hit weird runtime errors when trying to run scripts I wrote.

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

Dataiku solves our shadow IT problem by allowing us to control and curate data access with role-based permissions. It uses our centralized hub (Databricks) and allows us to track data lineage and output validation.