Summary
We have come a long way, friends! In this chapter, we covered batch-processing data, as well as streaming data. We embarked on a comprehensive journey through the world of data processing in Apache Spark with Python. We explored both batch processing and streaming data processing techniques, uncovering the strengths and nuances of each approach.
The chapter began with a deep dive into batch processing, where data is processed in fixed-sized chunks. We learned how to work with DataFrames in Spark, perform transformations and actions, and leverage optimizations for efficient data processing.
Moving on to the fascinating realm of stream processing, we learned about the nuances of Spark Structured Streaming, which enables the continuous processing of real-time data streams. Understanding the distinction between micro-batch processing and true streaming clarified how Spark processes streaming data effectively. This chapter highlighted the importance of defining schemas and...
