The Data Lifecycle | DAL4 A5.1 |
The concept of data flow involves transforming, storing, and analyzing data in order to answer questions about your organization. It helps you to understand where your organization is performing well, and where you could improve. It can also give you an insight into what the future of your organization might look like.
In this video course, you'll learn the basics of data flow, including the data lifecycle, and look at some common data flow scenarios.
In this next lesson, we're going to touch on the data lifecycle. It's important to understand the data lifecycle because the different stages of the lifecycle will affect data flow. Overall, there are really five key stages in the data lifecycle. You have the initial collection of data, the preparation of that collected data, the ingestion of the data into storage, processing or transformation of the data into usable form, and then you have analysis of the transformed data. During collection, data is acquired from other processes or maybe even user input. Such data might be in varied formats, or it may be unstructured. Preparation of the collected data may or may not happen next, depending on the process. In cases where ETL is in play, and data needs to be transformed before it is ingested or loaded, there is certainly a preparation step that occurs. Once data has been collected and prepared, it needs to be ingested into storage.
In the context of this discussion, the data would typically be ingested into cloud storage. Once the data has been ingested into storage, it needs to be processed or, if ELT is being used, transformed into a usable format. Finally, once the data has progressed through all previous stages of the lifecycle, it can be analyzed and interpreted. With the typical data lifecycle in mind, you then need to think about some things as you design a data flow that encompasses this data. You need to think about where the data is coming from, and in what format it's arriving. You need to determine how it needs to be transformed and if so, how that needs to be done. You need to think about the ultimate destination of this data and its analysis. Where's the data going and what questions does it need to answer? Only after considering all these concepts can you begin to formulate a data flow plan and come up with data flow requirements.
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