These end-to-end walkthroughs demonstrate the steps in the Team Data Science Process for specific scenarios. They illustrate how to combine cloud, on-premises tools, and services into a workflow or pipeline to create an intelligent application. The walkthroughs are grouped by platform that they use. The following menu links to these examples:
Here are brief descriptions of what these walkthrough examples provide on their respective platforms:
- HDInsight Spark walkthroughs using PySpark and Scala These walkthroughs use PySpark and Scala on an Azure Spark cluster to do predictive analytics.
- HDInsight Hadoop walkthroughs using Hive These walkthroughs use Hive with an HDInsight Hadoop cluster to do predictive analytics.
- Azure Data Lake walkthroughs using U-SQL These walkthroughs use U-SQL with Azure Data Lake to do predictive analytics.
- SQL Server These walkthroughs use SQL Server, SQL Server R Services, and SQL Server Python Services to do predictive analytics.
- SQL Data Warehouse These walkthroughs use SQL Data Warehouse to do predictive analytics.
For a discussion of the key components that comprise the Team Data Science Process, see Team Data Science Process overview.
For a discussion of the Team Data Science Process lifecycle that you can use to structure your data science projects, see Team Data Science Process lifecycle. The lifecycle outlines the steps, from start to finish, that projects usually follow when they are executed.
For an overview of topics that walk you through the tasks that comprise the data science process in Azure, see Data Science Process.