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.

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.

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.