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.
Throughout this course, we're going to talk about the basics of data flow, starting with the fundamentals. We'll talk a bit about how you can provide data flow solutions using Microsoft Azure services. We're going to discuss a few reference architectures as well, so we can kind of pull everything together. We'll also cover some common data flow scenarios, so you can better understand the requirements, the technologies, and the core data flow pipeline. These basic concepts are important because businesses need to know where they are performing well and where they are performing poorly. We need to ask hard questions about where the business is and where it's headed. Such answers can be pulled from the data that's collected by the business itself.
There may even be cases where additional questions about the business might be asked in the future. As such, it's critical to keep raw data around for some time, so any future questions can be answered. The concept of data flow encompasses the initial ingestion of data, any required transformations, storage of the data and then, ultimately, analysis of the data. Data flow is essentially about what needs to happen with the data in order to meet business requirements and how it can be used to answer questions about the business. As such, it's important to get this right. Without the right data flow, a business may not be able to find answers that it needs today, let alone answers it might need in the future.
Determining Data Flow Requirements
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