Architecting the storage layer and selecting the appropriate file formats establishes the structural foundation of a data lake. The next technical requirement is defining the mechanisms that transport data from operational systems into this storage. This process involves more than simply copying files; it requires resilient systems capable of handling network interruptions, varying data velocities, and the necessity for consistent data states.

This chapter focuses on the engineering patterns used to move data from sources into the lake. We examine batch ingestion workflows for handling high-volume historical loads and contrast them with Change Data Capture (CDC) techniques. CDC allows for the synchronization of database states by reading transaction logs, enabling near real-time updates without the overhead of full table scans.

Beyond the transport mechanism, we address the operational challenges inherent in distributed storage. You will learn techniques for handling schema evolution when upstream data structures change. The material also covers the "small file problem", a common performance issue in object storage, and mitigation strategies such as compaction. We conclude by defining idempotency in the context of data engineering, ensuring that pipelines can be re-executed safely during failure recovery without generating duplicate records.