BigQuery natively supports GIS, or Geographic Information System functions. For gleaning insights from your geographic data points, like longitude and latitude. Let's examine how you can put these into practice. In this SQL query that you see here, we are plotting the path of a hurricane using SQL and GIS functions. We first create a geographic point based on lat long data. We also bring in other useful fields, like wind speed, the distance the hurricane is to land or landfall, and the radius of the hurricane. We query all this raw data from the BigQuery public dataset from NOAA on hurricanes, and filter for one hurricane in particular. You can see that in the Where clause, that's hurricane Maria in 2017. Then, we geographically bound the points that we care about with the "GIS within" function to ensure that it'll fit on the map or the area of focus. So we can visualize these points. Lastly, we explore our points using Geo Viz, which is a web tool for visualization of geospacial data in BigQuery, but it uses the Google Maps APIs. Here you can see and track the hurricane making landfall in the US. One of the best ways to get better at data analysis is by practicing in a variety of data sets. The BigQuery Public Datasets program partners with companies and organizations to host their datasets in BigQuery, and then make them available for analysis by the public. Currently, there are well over a 100 different datasets for you to explore, and you can find them all in the BigQuery web UI under Explore Data, which is located right above your own datasets. So let's do a quick activity. Explore a BigQuery public dataset that interests you. Then think of the following questions to answer. What's the name of the dataset? How many records are in some of the tables? Are there any data quality concerns after you previewed that data? After analyzing this schema, what types of insights do you think you could find? Lastly, are there any other datasets that you can potentially join against this one for additional insights? Take a moment to find and explore your dataset, and then we'll bring in the head of Google Cloud BigQuery Public Datasets Program, Shane Glass, and he'll explore one of his favorite BigQuery public datasets in a demo.