Streaming analytics can be a difficult to set up, especially when working with late data arrivals and other variables. In this course, Modeling Streaming Data for Processing with Apache Spark Structured Streaming, you’ll learn to model your data for real-time analysis. First, you’ll explore applying batch processing to streaming data. Next, you’ll discover aggregating and outputting data. Finally, you’ll learn how to late arrivals and job failures. When you’re finished with this course, you’ll have the skills and knowledge of Spark Structured Streaming needed to combine your batch and streaming analytics jobs.
Starting out as an accidental DBA and developer, Eugene Meidinger now focuses primarily on BI consulting. He has been working with SQL Server for five years now, and is certified in Querying and Administering SQL Server 2012. He regularly presents at various SQL Saturday events.
Course Overview Hi, everyone. My name is Eugene Meidinger, and welcome to my course, Modeling Streaming Data for Processing with Apache Spark Structured Streaming. I'm a business intelligence consultant working for myself, and in this course, we're going to learn how to model our data to support Stream Analytics. Some of the major topics that we will cover will include how is streaming data different, what is Ppark structured streaming, grouping and aggregations, and handling late data. By the end of this course, you'll know how to define optimal streaming queries. Before beginning the course, you should be familiar with Spark and Spark SQL. I hope you'll join me on this journey to learn streaming analytics with the Modeling Streaming Data for Processing with Apache Spark Structured Streaming course, at Pluralsight.