Introduction
To understand the basics of big data workflows, we must understand what a process is and how it co-relates to the workflow in data-active environments. Methods tend to be designed as high-level, end-to-end structures useful for decision making and normalizing how things get done in a business or organization. In contrast, workflows are task-dependent and require more accurate data than processes. Processes consist of one or more workflows relevant to the overall objective of the process.
Big Data WorkFlows
In many ways, big data workflows are pretty similar to standard ones. Data is necessary for any workflow in the various steps to accomplish the given tasks. Let us consider the workflow in the preceding healthcare example. One elementary workflow is the task of “drawing blood.” Drawing blood is a necessary task needed to complete the entire diagnostic process. If some tragedy happens and blood has not been removed, or the data from that blood test has been lost, it will directly impact the integrity or truthfulness of the overall activity.
What happens when users introduce a highly dependent workflow on a big data source? Although one might be able to use existing workflows with big data, one cannot assume that a process or workflow will work correctly by just substituting a big data source for an authoritative source. This scenario may not work because standard data-processing methods do not have the common strategies or performance to handle the variety of the big data.