Data Visualization

Guidelines about data analysis and visualization with CrateDB.

Apache Superset / Preset

Product

• Introduction to time series visualization in CrateDB and Apache Superset (Blog)

• Use CrateDB and Apache Superset for Open Source Data Warehousing and Visualization (Blog)

• Introduction to time series visualization in CrateDB and Apache Superset (Webinar)

• Introduction to time series Visualization in CrateDB and Apache Superset (Preset.io)

Development

• Set up Apache Superset with CrateDB

• Set up an Apache Superset development sandbox with CrateDB

• Verify Apache Superset with CrateDB

Cluvio

• Data Analysis with Cluvio and CrateDB

Explo

• Introduction to Time Series Visualization in CrateDB and Explo

Grafana

• Visualize data with Grafana

hvPlot and Datashader

Display millions of data points using hvPlot, Datashader, and CrateDB

HoloViews and Datashader frameworks enable channeling millions of data points from your backend systems to the browser’s glass.

This notebook plots the venerable NYC Taxi dataset after importing it into a CrateDB Cloud database cluster.

🚧 Please note this notebook is a work in progress. 🚧

Time series visualization

Python HoloViews hvPlot Datashader

Metabase

• Visualize data with Metabase

• Real-time data analytics with Metabase and CrateDB

• https://github.com/paoliniluis/metabase-cratedb

pandas

• From data storage to data analysis: Tutorial on CrateDB and pandas

Plotly / Dash

CrateDB for Time Series Modeling, Exploration, and Visualization

Access time series data from CrateDB via SQL, load it into pandas DataFrames, and visualize it using Plotly.

About advanced time series operations in SQL, like aggregations, window functions, interpolation of missing data, common table expressions, moving averages, relational JOINs, and the handling of JSON data.

Time series visualization

Python pandas Plotly Dash

Alternatively, you are welcome to explore the canonical Dash Examples.

R

• CrateDB with R