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.
So why should the cloud be used for data flow? Well, for starts, a key benefit of using the cloud for data flow is the fact that you don't have to worry about setting up or maintaining DMZs. Instead of needing to figure out what devices need to be opened to the internet, and which ones don't, the cloud kind of serves as a natural barrier, I guess you could say. So you don't have to worry about what ports do and do not need to be opened. When deciding how to leverage the cloud for data flow, it's important to consider the usual suspects, like cost, speed, latency, etc. Pulling data into Azure isn't going to incur any costs because its considered ingress, however, pulling that data out later on is going to incur some kind of cost, because it's egress. It's also important to consider latencies and bandwidth for both moving data to Azure and from Azure. With that said, leveraging ExpressRoute and peering ensures data travels through a private connection, which would maintain a steady, and lower, latency than if it was traveling over the public internet. Other benefits to the cloud for data flow processing include the number of services that are available to facilitate the data flow process to begin with. Leveraging functionality as available as platform or as a service, allows you to essentially to hit the ground running.
Simply use the services that you need to design and manage your data flows without any worry about maintenance of the platform or services. The amount of work necessary to get up and running is minimal. To add to that, with such a wide array of services available, you're likely to have access to technologies that you simply wouldn't have access to on-prem. Stuff like machine learning and artificial intelligence would typically fall into this bucket. Leveraging the cloud provides elasticity as well. It allows you to scale almost infinitely if necessary. This isn't something that's easily accomplished on-prem. Cost and time would quickly become barriers to doing so. Instead, the cloud offers the ability to scale up and down as required, while allowing you to pay only for the services and resources that you use. This is especially helpful when dealing with uneven or inconsistent workflows that need to be processed. These benefits, however, will obviously vary on a service by service basis.
Lectures
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