Data Exploration

  • Access up-to-date data from a unified data model across the organization. Loaded and virtualized data sources are available without writing complex joins, regardless of the persistence technology.​
  • Quickly access and explore raw data files uploaded to a Notebook or file system like Azure Blob storage, AWS S3, or on-premise SSD.​
  • Use pre-build no-code interfaces for viewing data, or code your own visualizations and data quality checks, productize your work as a reusable API that the whole team can access from Jupyter or any client.


  • Identify patterns and trends by visualizing data in tables and charts.​
  • Generate insights by profiling data with histograms, identifying relationships with correlation matrices, and looking at scatter plots, line charts, bar charts, and area charts.​


  • Use Jupyter Notebook to view data stored in a unified data model.​
  • Easily fetch instances of a BHC3 Type, and view as tables in a notebook.​
  • Readily visualize and plot time series data.​
  • Use your favorite libraries to visualize and explore data.


  • Accelerate machine learning prototyping through high- performance, distributed, and in-memory exploration of large datasets from enterprise data sources.
  • Auto-record selective data science experimentation steps and metadata for seamless AI application development and deployment.
  • Enhance loading, organizing and profiling for unstructured and mixed data including images and text.​

Datasets screen-shot