 Data scientists need accurate, up-to-date and complete data. Unfortunately, many data scientists must settle for inaccurate, stale, and incomplete data sets, simply because they do not have access to the data sources they need to build production-ready models. I'm Carl Eklund, a principal architect with Red Hat OpenShift Data Science. Today, I'd like to show you how we can pull the data you need without moving or duplicating it into a Red Hat OpenShift Data Science-powered Jupyter Notebook using Starburst Galaxy. The Red Hat OpenShift Data Science dashboard lets us view our currently enabled software solutions. On the Explore tab, we can navigate to Starburst Galaxy and select Get Started to deploy our own Starburst Galaxy cluster. Here, we can seamlessly navigate or submit SQL queries. But let's grab our connection information from the Warehouses tab so we can use it within our notebook. Go back to Red Hat OpenShift Data Science and launch a Jupyter Notebook. From here, we can select our notebook image, our preferred container size, and we can input any environment variables that we do not want to store and get. Once in our notebook, we can install any packages that may be required for this exercise. Let's import those environment variables we entered on the last page and connect to our Starburst Galaxy cluster. From here, we can submit SQL statements and access our data. Using a Jupyter Notebook allows us to create fairly complex SQL statements, connect to our cluster, and track our progress as we explore our data. Starburst Galaxy's real power is its ability to connect to multiple data sources and provide you with a single point of access to your entire organization's data. From there, simply leverage Jupyter Notebooks to pull your data in, explore it, and build your models. For the latest information and content, please check out Red Hat OpenShift Data Science at developers.redhat.com.