Streaming, cross-sectional data visualization in Jupyterlab with Perspective and Apache Arrow

Many data visualization libraries are built with static data in mind, where everything is known before the visualization is created. Analyzing data streams in real-time, however, is a crucial part of many industries. Combining JupyterLab’s ease-of-use and flexibility with Perspective, a high-performance streaming data visualization library, users can rapidly prototype, analyze, and visualize results from a multitude of data sources both live and static.

Using real-time stock quotes from the IEX Cloud API, Junyuan Tan demonstrates how Perspective can be used to visualize streaming data, combine real-time information with analysis and data points from various sources, and export data snapshots using Apache Arrow, without leaving the Jupyter Notebook.

Attendees will learn:

• How to accumulate data from a streaming datasource in a Perspective table
• Visualizing, charting and analyzing data in real-time using PerspectiveWidget
• Chaining multiple Perspective tables to create your own augmented, streaming data sources
• Streaming, exporting, and storing data in the Apache Arrow format, and reintegrating that data back into Perspective

A working knowledge of Python is recommended but not required.

Links:

Notebooks/Presentation Materials: https://github.com/sc1f/streaming-perspective-jupytercon-2020

Perspective: https://perspective.finos.org/

Apache Arrow: https://arrow.apache.org/