TrustRadius Insights for Dataiku are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Versatile Data Handling: Users have praised Dataiku DSS for its versatility in handling various data sources, including Python, R, SQL, and built-in tools. Some reviewers found this ability to transform unorganized data into valuable information through intuitive dashboards to be a crucial feature.
Manageable Data Pipelines: The presence of inbuilt recipes in Dataiku DSS has made data pipelines more manageable for users. This modular approach to pipeline creation and the availability of pre-built recipes for data transformation have been appreciated by several reviewers.
Ease of Use: Many users have highlighted the ease of use of Dataiku DSS. The platform's inclusion of all majorly applied operations as direct 'recipes' and the visual flow element that helps users keep track of their work intuitively are some factors that contribute to its user-friendly nature.
Dataiku DSS is being used in my team to perform various tasks which ranges from data preprocessing to machine learning model creation. It provides a one-stop solution to fetch data from different sources such as Amazon S3, SQL Server databases, etc. and merge them onto a single platform. We use Dataiku DSS to perform data imputations, data cleaning and feature engineering to prepare datasets for creating machine learning models. We also extract business insights (data analytics) using various statistical methods and visual representations such as scatter plots, histograms, boxplots, etc. Furthermore, optimized ML models are created which are used to predict/forecast target variables and drive business decisions.
Pros
Allows users to collaborate and monitor individual tasks
Caters to both types of analysts, coders and non-coders, alike
Integrate graphs and plots with visualization tools such as Tableau
Cons
Its community support is very limited at the moment
Complex to integrate with automation tools such as Blue Prism
Likelihood to Recommend
Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
VU
Verified User
Analyst in Customer Service (Consumer Goods company, 5001-10,000 employees)