Windowing and Join Operations on Streaming Data with Apache Spark on Databricks

What you'll learn

Structured Streaming in Apache Spark treats real-time data as a table that is being constantly appended. In such a stream processing model the burden of stream processing shifts from the user to the system, making it very easy and intuitive to process streaming data with Spark. Apache Spark supports a range of windowing and join operations on streaming data using processing time and event time.

In this course, Windowing and Join Operations on Streaming Data with Apache Spark on Databricks, you will learn the difference between stateless operations that operate on a single streaming entity and stateful operations that operate on multiple entities accumulated in a stream. Then, you will explore the different kinds of windows supported by Apache Spark which includes tumbling windows, sliding windows, and global windows.

Next, you will understand the differences between event time, ingestion time, and processing time and see how you can perform windowing operations using both processing time as well as event time. Along the way, you will connect to an HDInsight Kafka cluster to read records for your input stream. You will then use watermarking to deal with late-arriving data and see how you can use watermarks to limit the state that Apache Spark stores.

Finally, you will perform join operations using streams and explore the types of joins that Spark supports for static-stream joins and stream-stream joins. You will also see how you can connect to Azure Event Hubs to read records.

When you are finished with this course, you will have the skills and knowledge of windowing and join operations needed to identify when these powerful transformations should be performed and how they are performed.

Table of contents