Streaming data refers to real-time data which is continuously flowing from a source to a target. It includes audio, video, text, or numerical data that is generated by sources such as social media platforms, sensors, and servers. The data transmission is in a steady stream with no fixed beginning or end. Streaming data is important in fields such as healthcare, finance, and transportation, and it forms a key component of the Internet of Things (IoT).
The ability to handle streaming data is an important skill for a data scientist. In this Quick Success Data Science project, we’ll use streaming data to track the International Space Station (ISS) as it orbits the Earth. For coding we’ll use Python and Plotly Express in a Jupyter Notebook.
Telemetry is the in-situ collection and automatic transmission of remote sensor data to receiving equipment for monitoring. While there are numerous sources for ISS telemetry, we’ll use the WTIA REST API (WTIA stands for Where the ISS at?).
This API was written by Bill Shupp to include more features than are provided by typical ISS tracking/notification sites. At the end of the article, I’ll list some additional sources for ISS telemetry and tracking, in case you want to try them out or compare our results to theirs.
Plotly Express is a built-in part of the Plotly graphing library. As a simpler, higher-level version of Plotly, it’s the recommended starting point for creating most common figures.
Plotly Express contains more than 30 functions for creating entire figures at once, and the API for these functions was carefully designed to be as consistent and easy to learn as possible. This makes it easy to switch between figure types during a data exploration session.
While Plotly Express is easy to use and creates beautiful, interactive plots, they’re not as customizable as plots generated in lower-level libraries like Plotly or matplotlib. As always, you have to give up some control for ease of use.