Step 1: Understand how Sift organizes telemetry data

Overview

Before we discuss and import the preprocessed dataset, let's review how Sift organizes time-series data.

Assets, Channels, and Runs

Sift uses three core building blocks: Assets, Channels, and Runs. These building blocks help structure sensor data, making it easier to explore, analyze, and reuse it across different experiments or observation periods.

• Assets: These represent physical or virtual systems that generate data. In this tutorial, the Asset is the Mars Environmental Monitoring Station (REMS), which collected environmental telemetry on the Martian surface.
• Channels: These represent individual time-series signals recorded from an Asset. In this tutorial's dataset, each environmental measurement such as temperature, atmospheric pressure, and ultraviolet index is represented as a separate Channel.
• Runs: These represent time windows or observation sessions during which data is collected from one or more Channels. The preprocessed dataset used in this tutorial captures one year of telemetry collected throughout 2021, which will be treated as a single Run.