Data Flow Basics
Data Flow Components
Building a Dataflow with Azure Data Factory
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In this course, we're going to review the features, concepts, and requirements that are necessary for designing data flows and how to implement them in Microsoft Azure. We’re also going to cover the basics of data flows, common data flow scenarios, and what all is involved in designing a typical data flow.
- Understand key components that are available in Azure that can be used to design and deploy data flows
- Know how the components fit together
This course is intended for IT professionals who are interested in earning Azure certification and for those who need to work with data flows in Azure.
To get the most from this course, you should have at least a basic understanding of data flows and what they are used for.
In this lecture, I want to touch on what database services are available in Azure, since databases typically play a role in the data flow process.
Getting back to our previous data flow example of the casino, the database service is the source of information. Player info from the floor’s slot machines is continuously written to the backend database. In turn, the data from the database is then fed in as the source for the casino’s overall data flow. That said, in other scenarios, the database service can serve as some intermediary step in the data flow process, or it can even serve as a data mart.
There are many different database services available in Azure. They are available as both PaaS offerings and DaaS offerings. For example, Azure offers services such as Azure SQL Database and Cosmos DB. Because Cosmos DB is a NoSQL implementation, it’s a good fit for data such as documents and graphs. Conversely, because Azure SQL Database is a more traditional relational database, it’s better suited for relational data. There are also several IaaS options available as well. Simply run a database instance within an IaaS virtual machine, if PaaS isn’t your thing for some reason.
So, when designing a data flow, you may find yourself splitting between or among multiple models, or even platforms, due to the data that needs to be processed.
Tom is a 25+ year veteran of the IT industry, having worked in environments as large as 40k seats and as small as 50 seats. Throughout the course of a long an interesting career, he has built an in-depth skillset that spans numerous IT disciplines. Tom has designed and architected small, large, and global IT solutions.
In addition to the Cloud Platform and Infrastructure MCSE certification, Tom also carries several other Microsoft certifications. His ability to see things from a strategic perspective allows Tom to architect solutions that closely align with business needs.
In his spare time, Tom enjoys camping, fishing, and playing poker.