Having configured the underlying storage engine and defined the data model, the focus shifts to populating the warehouse. Ingestion serves as the entry point for raw information. At scale, simply copying files is insufficient. You must account for network instability, schema drift, and the velocity of incoming events. This chapter examines the engineering patterns required to move data reliably from source systems to your analytical environment without degrading performance.

We begin by analyzing Change Data Capture (CDC) architectures. Unlike query-based polling which strains source databases, log-based CDC captures binary events from the transaction log to synchronize state in near real-time. Following this, we address the mathematical necessity of idempotency in pipeline design. An idempotent process ensures that re-running a failed job does not result in duplicate records. Formally, for a data transformation function $f(x)$, idempotency guarantees that:

$f(f(x)) = f(x)$

This property allows you to retry pipelines safely in distributed environments where network failures are common.

The final sections compare the trade-offs between micro-batch loading and continuous streaming. While streaming offers lower latency, it introduces complexity regarding file fragmentation and compute costs. We will also evaluate techniques for managing late-arriving data and watermarks to maintain temporal accuracy in your time-series tables. By the end of this chapter, you will be able to architect ingestion systems that balance latency requirements with resource efficiency.