Dataiku for Enterprise AI

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?

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.