Working with Structured Streaming Data using Autoloader

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.