Dataiku Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The interface for selecting fields of a datasource or maybe creating calculated fields should be simpler. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

What I like most about Dataiku is how it brings data, analytics, machine learning, and AI together in one collaborative platform. The biggest upside for me is that it can significantly accelerate the journey from an AI idea or experiment to a governed, production-ready solution. It helps technical and business teams collaborate more effectively, reduces the need to stitch together multiple disconnected tools, and provides strong capabilities around governance, scalability, and monitoring. I also like that it supports both traditional analytics/ML and newer generative AI use cases, making it easier for enterprises to adopt AI in a practical and responsible way. Review collected by and hosted on G2.com.

The main challenge is the learning curve, especially for teams new to the platform. Its broad range of capabilities can also require some time for setup, governance, and integration with existing enterprise tools. These are more adoption considerations than major limitations. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The best thing about Dataiku is how easy it is to go from an idea to a working model. I can clean the data, create variables, test models, and deploy them without having to switch tools. Additionally, it keeps everything well organized and helps the team move faster. Review collected by and hosted on G2.com.

What I like least about Dataiku is that some heavier workflows can feel slow, especially when several users are working simultaneously with large datasets. Additionally, some functions require more clicks than I expected to get to what I need. It's nothing critical, but there is room to simplify and streamline the experience. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Right now, I am really enjoying the AI tools such as Cobuild. I recently learned about agents as well which has made me very excited to try using them myself. I also appreciate the simple user interface, it has made a new Dataiku user lik Review collected by and hosted on G2.com.

Something I dislike is the management side of some of the code environments. It is a very manual process for us to have to delete and create new python environments when there are new Dataiku upgrades. Review collected by and hosted on G2.com.