Dataiku has announced the release of DSS 2.3, improving the user experience and efficiency of their data analysis software suite – with a strong focus on enhancing and facilitating data preparation features for both expert and beginner analysts. The new version of Data Science Studio also incorporates Flow Views, a Refreshed SQL Notebook, Onboarding / Contextual Help, and a new global search feature to find all instances of an object inside and across all DSS projects.
The new features in DSS 2.3 include:
Data wrangling improvements for increased efficiency and improved user experience
Group steps together in complex preparation scrips, comment, add colors, and mass actions (mass delete, mass group, etc.) for visual data preparation.
Quick column view: new right panel allows quick analysis on your datasets (histograms, distributions, etc.).
New color coding feature: automatic color by value allows you to quickly see correlations between columns and see blocks of values.
Meanings & types: both meaning (rich semantic type) and storage type are now displayed and can be defined by the user. The user can also define his or her own meanings.
Global Search: DSS Data Catalogue
Search all objects within a DSS instance across projects.
Flow Tools and Views
The new flow views system — which provides additional “layers” into the data flow — is an advanced productivity tool especially designed for large projects:
- Recursively check and propagate schema changes across a flow.
- Check the consistency of datasets and recipes across a project.
- Color by tag name and easily propagate tags.
Refreshed SQL Notebook
No more juggling – instead of having one query and one history in one SQL notebook, users are now able to work on several queries within a single notebook.
Onboarding / Contextual Help
DSS now includes helper tooltips to guide users through the UI. The Help menu now also includes contextual search to Dataiku documentation.
Dataiku’s Data Science Studio provides an end-to-end solution for developing predictive analytics solutions for business. It can be used to quickly build data products that transform raw data into business impacting services including:
- Churn analytics
- Fraud detection
- Graph analytics
- Data management
- Demand forecasting
- Spatial analytics
- Lifetime value optimization
- Predictive maintenance
- An analytical CRM and much more.
Bigdata and data center