The technique used to collect data often determines the type of models that can be utilized. If a seismograph only reported the current reading of seismic activity once an hour, it would be meaningless. The data would not be high fidelity enough to predict earthquakes. The job of a data scientist in an IoT project does not start after the data is collected but rather, the data scientist needs to be part of the building of the device. When a device is built, the data scientist needs to determine whether the device is emitting the type of data that is appropriate for machine learning. Next, the data scientist helps the electrical engineer determine whether the sensors are in the right places and whether there is a correlation between sensors, and finally, the data scientist needs to store data in a way that is efficient to perform analytics. By doing so, we avoid the first major pitfall of IoT, which is collecting and storing data that is, in the end, useless for machine learning.

This chapter examines storing, collecting, and analyzing data to ensure that there is enough data to perform effective and efficient machine learning. We are going to start by looking at how data is stored and accessed. Then, we are going to look at data collection design to ensure that the data coming off the device is feasible for machine learning.

This chapter will cover the following recipes:

• Storing data for analysis using Delta Lake
• Data collection design
• Windowing
• Exploratory factor analysis
• Implementing analytic queries in Mongo/hot path storage
• Ingesting IoT data into Spark