Summary
Throughout this chapter, we have reviewed different features of Structured Streaming and looked at how we can leverage them in Azure Databricks when dealing with streams of data from different sources.
These sources can be data from Azure Event Hubs or data derived using Delta tables as streaming sources, using Auto Loader to manage file detection, reading from Apache Kafka, using Avro format files, and through dealing with data sinks. We have also described how Structured Streaming provides fault tolerance while working with streams of data and looked at how we can visualize these streams using the display function. Finally, we have concluded with an example in which we have simulated JSON files arriving in the storage.
In the next chapter, we will dive more deeply into how we can use the PySpark API to manipulate data, how we can use Python popular libraries in Azure Databricks and the nuances of installing them on a distributed system, how we can easily migrate from...
