Chart Gallery¶
VAST Orbit offers an extensive selection of interactive visualizations that play a pivotal role in extracting valuable business insights. These versatile visualization tools empower you to effectively analyze and communicate data trends, enabling informed decision-making and enhancing data-driven strategies.
Visualization Backends:
Plotly: Interactive web-based visualizations with hover effects and zoom capabilities
Matplotlib: Publication-quality static plots with extensive customization
Graphviz: Specialized tree and graph visualizations for ML models
Tip
Every chart below is available in Plotly and Matplotlib, driven by Python or SQL, unless a card notes otherwise.
Key Features:
✓ In-Database Execution: Charts render from VAST DataBase without moving data to Python
✓ Smart Sampling: Automatically samples large datasets for responsive visualizations
✓ Federated Queries: Visualize data from multiple sources (VAST, S3, PostgreSQL) in one chart
✓ Export Options: Save as PNG, SVG, HTML, or interactive web dashboards
Note
For detailed documentation on chart customization, parameters, and advanced examples, see the Chart Gallery User Guide.
Basic Charts¶
Fundamental visualizations for exploratory data analysis and presentation.
Single Stacked Fully Stacked
1D 2D Stacked Fully Stacked Negative
Single Multi
Candlestick
Contour
Matrix Vector
Single Multi
Single Multi
Single Multi
Regular Donut Rose 3D Nested
Pivot
Single Multi
1D 2D 3D Bubble
Single Multi
Machine Learning & Analytics¶
Advanced visualizations for model evaluation, time-series analysis, and statistical insights.
Bar Heatmap
Model Comparison
ROC PRC Lift Chart
1D 2D 3D Logit
Matrix Vector
Efficiency Scalability Performance
Efficiency Scalability Performance
K-Means Clustering
Matplotlib only
K-Means Clustering
1D 2D 3D
1D 2D
Linear Regression Random Forest Residual Plot
Trend Seasonal Residual
Forward Backward
Graphviz
Tree Visualization Rules
Prediction Plot Confidence Intervals
Geospatial Visualization¶
Map-based visualizations for location data and geographic analysis.
Plotly · Matplotlib
Choropleth Scatter Bubble Heat Map
Animated Visualizations¶
Dynamic, time-based visualizations for presentations and storytelling.
Matplotlib only
Bar Pie Bubble Time-Series
Quick Start Examples¶
Basic Visualization:
import vastorbit as vo
# Connect to VAST
vo.new_connection({
'host': 'vast-cluster.com',
'catalog': 'vast_catalog'
})
# Load data
vdf = vo.VastFrame('sales_data')
# Create interactive scatter plot
vdf.scatter(
columns=['revenue', 'profit'],
by='region',
max_nb_points=10000
)
Machine Learning Visualization:
from vastorbit.machine_learning import LogisticRegression
# Train model
model = LogisticRegression()
model.fit(vdf, 'churn', ['age', 'tenure', 'spend'])
# Plot ROC curve
model.roc_curve()
# Plot classification boundaries
model.plot()
Federated Query Visualization:
# Query VAST table + S3 file in one visualization
customers = vo.VastFrame('vast_catalog.customers')
transactions = vo.VastFrame('hive.s3_bucket.transactions')
# Join and visualize
result = customers.join(transactions, on='customer_id')
result.bar(columns=['region', 'total_revenue'])
Tip
All visualizations support:
Automatic sampling for large datasets (configurable via
max_nb_points)Export to HTML for embedding in reports and dashboards
Interactive legends for filtering data on-the-fly
Customizable color schemes to match your brand
For complete API documentation and advanced customization options, visit the API Reference reference.