Working with Structured Streaming Data

This module starts with building reliable streaming pipelines using Databricks' Structured Streaming. It highlights techniques to handle data reliability, such as state management, checkpointing, Write-Ahead Log (WAL), and ensuring exactly-once processing. The importance of handling schema evolution dynamically is discussed, with a focus on using Autoloader to efficiently process streaming data while accommodating schema changes.

Additionally, it covers trigger modes (micro-batch, continuous) to control processing frequency and output modes (append, complete, update) to define how results are stored. Key features like fault tolerance, real-time data processing, and scalability for large datasets are also addressed to enhance pipeline efficiency and robustness.