am little time f…ll of this results in unnecessary complexity, confusion and a great deal of wasted time and energy. Slow progression through the lifecycle shown in Figure 15 coupled with high-severity errors finding their way into production can leave a data analytics team little time for innovation.
…Agile, DevOps and statistical process control — that comprise the intellectual heritage of DataOps. Agile governs analytics development, DevOps optimizes code verification, builds and delivery of new analytics and SPC orchestrates and monitors the data factory. Figure 2 illustrates how Agile, DevOps and statistical process control flow into DataOps.
… pipeline, progresses through a series of steps and exits in the form of reports, models and views. The data pipeline is the “operations” side of data analytics. It is helpful to conceptualize the data pipeline as a manufacturing line where quality, efficiency, constraints and uptime must be managed. To fully embrace this manufacturing mindset, we call this pipeline the “data factory.”