What are the primary criteria associated with data pipelines?

The primary criteria associated with data pipelines are latency, complexity, and compute cost because these factors significantly influence the efficiency and effectiveness of data transfer and processing within a system.

Latency refers to the delay between data being generated and it being available for use. In real-time applications, a lower latency is crucial to ensure quick decision-making and timely insights.

Complexity involves how intricate a data pipeline is, including how many steps, transformations, or integrations are needed for the data to be processed effectively. Reducing complexity can lead to easier management and maintenance of the pipeline.

Compute cost focuses on the expenses incurred from processing data, including the computational resources required to run the pipeline. A well-optimized pipeline should minimize these costs while still meeting performance needs.

Together, these criteria provide a foundational framework for evaluating and designing effective data pipelines, ensuring that they are efficient, economically viable, and responsive to the needs of data users.