November 2022
Intermediate to advanced
327 pages
7h 20m
English
Content preview from Genomics in the Azure Cloud
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Chapter 4. Orchestrating Data Movement and Transformation
When you work in a data-driven role (e.g., as a bioinformatician, data scientist, data analyst, etc.), having data available to you is paramount to being successful. With a cloud environment, a common hurdle that organizations have when they first start out is actually getting data into the cloud for everyone to use. Data orchestration encompasses the processes for getting data into and out of cloud resources and also managing certain data tasks. In this chapter, we’ll learn how to orchestrate data movement in the cloud by connecting to sources (often outside our cloud environment) and copying data over to our data lake or other destination.
In Azure, the standard tool for data orchestration is called Data Factory. This tool blends the capabilities of a traditional extract-transform-load (ETL) tool, like SQL Server Integration Services, with orchestration capabilities for queuing up external data tasks. With Data Factory, we’ll learn how to extract data from outside our Azure environment into our data lake and also learn how to transform and load that data into our data warehouse. See Figure 4-1.
Outside of Azure, there are certainly other third-party ETL tools available for you to purchase, but it’s worth giving Data Factory a shot as it is very well-integrated with the other Azure services that we cover in this book. Plus, Data Factory supports more than 90 built-in connectors to common enterprise platforms like SAP, ...
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