What does "data latency" refer to in data engineering?

Data latency refers to the delay between data generation and its availability for action. In the context of data engineering, this concept is crucial because it impacts how quickly insights can be derived from data and how timely decisions can be made based on that data.

When data is generated, it often goes through various processes before it can be analyzed or utilized, such as being transmitted over networks, processed, or stored. The time taken during these phases contributes to the overall latency. High data latency can hinder real-time decision-making, which is increasingly critical in many applications, such as financial trading, fraud detection, and real-time analytics.

Understanding data latency helps engineers design systems that minimize delays and enhance the responsiveness of applications relying on timely data availability. This is especially pertinent in environments where quick action based on the latest data is essential.