Overall experience is great because it is a good platform for Data Scientists, Data Analysts and Researchers. It has ML and AI functionalities.

The flow in the Dataiku describes the process flow of the project easily. Scenarios and triggers are there for the automation process. Modeling is easier and shows the validations along with the results. Basic Visualizations can also be done in Dataiku.

Sometimes it has complexity in creating web applications and to integrate with other tools.

As a non - data scientist, i was curious to see how DSS could help me with the data preparation (cleaning and combining data), feature engineering and predictive modelling phases of a data analysis project My goal was to make 2 submissions on Kaggle challenges in under 1 hour and without 1 line of code using the Data Science Studio (Titanic and Otto Product Classification datasets). First, I was really impressed with the overall ease of use and ergonomy of the studio. Building "recipes" for data preparation mostly uses visual processors and the operations are visible directly on a sample of the data, facilitating validation of preparation steps. In a train / test scenario, i especially enjoyed being able to replicate my recipes on both datasets very easily. I used the Data Visualization tool to build a few exploratory charts, which can be done quite easily, though it is not as powerful as specialized tools (namely Tableau or Qlik). For the machine learning part, I restricted myself to visual machine learning in the studio, which already packs the most common algorithms (random forest, logistic, svm, gradient-boosting...). I found the ability to benchmark and compare algorithms performance quickly a great time saver, allowing me to reach a first score in under half an hour on each dataset. Once I chose the best model, I only needed a few clicks to use the model to prepare and score the Test Dataset and make my submissions. Both times I was in the lower half of the rankings but above Kaggle algorithmic benchmarks. For "real" Data Scientists and engineers, the Studio allows them to go much further by building recipes and models in R, Python, SQL, Hive, Pig etc...but even as a business analyst, I felt empowered by the software that enabled me to prepare, analyse and build simple predictive models with my data.

The breadth of choices that one gets with the product is esstial for anyone transioning to get more data science enablement. This platform is great to have a streamlined and operational portfolio of all your data requirements. There are outages that might occur once in a while but the quick turnaround time ensures that work is not compromised

The ease of onboarding people onto the platfrom is seemless. In addition, Dataiku provides their own modules that help in getting accustomed to the product. The width of options that you get for analysis is also vast and might be a one stop solution for all data science needs

The least enjoyable expereince of this product has to be the outages that become frequent with each version revision.

Overall the software is good and can be used for repetitive tasks with high accuracy. Only downside is the new data source connection and editing after the workflow is created

Data flows Automation is easy and efficient Data Discovery

Editing or connecting to a new data source is difficult Low visibility inside the flows Sometimes slow to work due to heavy workflow

Whether the project requires data accumulation, or preprocessing, or data manipulation, or extracting business insights from data, Dataiku is the go-to platform. It covers end-to-end project execution and deployment along with providing API support.

time saving, useful for both coders and non-coders, allows for multi-user collaboration and monitoring, creating flowcharts help in maintaining granularity

It is in the early phase of its launch, 8 years old precisely. Hence, the customer support is not as widespread as other customer supports like stackoverflow, etc.