You may have seen the iconic episode of "I Love Lucy" where Lucy and Ethel get jobs wrapping chocolates in a candy factory. The high-speed conveyor belt starts up and the ladies are immediately out of their depth. By the end of the scene, they are stuffing their hats, pockets, and mouths full of chocolates, while an ever-lengthening procession of unwrapped confections continues to escape their station. It's hilarious. It's also the perfect analog for understanding the significance of the modern data pipeline.
The efficient flow of data from one location to the other - from a SaaS application to a data warehouse, for example - is one of the most critical operations in today's data-driven enterprise. After all, useful analysis cannot begin until the data becomes available. Data flow can be precarious, because there are so many things that can go wrong during the transportation from one system to another: data can become corrupted, it can hit bottlenecks (causing latency), or data sources may conflict and/or generate duplicates. As the complexity of the requirements grows and the number of data sources multiplies, these problems increase in scale and impact.