Category: Cloud

Mark Kromer shows how you can convince Azure Data Factory to skip a variable number of lines before beginning processing:

A common requirement that I see from customers who are processing text files in data lakes with Azure Data Factory, is to read and process files where there are variable numbers of lines that precede both the data headers and the data that needs to be processed. ADF already has facilities that handle the ability to switch headers off or on as well as the ability to specify parameterized skip line counts. However, in many cases, files that are received for processing have variable numbers of superfluous lines that need to be skipped.

In ADF, between pipeline activities and data flows, there are a number of ways to handle this scenario. In this post, I am going to demonstrate one such technique. 

Read on to see which technique Mark chose and how to get it working.

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Paul Andrew explains the idea of pipeline hierarchies with respect to Azure Data Factory:

Next, even if the concept isn’t new, where I’d like to call out two big differences in my approach to orchestration with ADF comes from working within Microsoft Azure. The highly scalable cloud platform presents some new challenges that SSIS simply didn’t. For me these are:

– Needing to consider our wider solution and what things now cost. I’m fairly sure I’ve said it before. When working with ‘Pay-as-you-go’ services we need to think about designing for cost/consumption as well as all our other data transformation and output requirements. In Azure it is so easy to just leave resources running night and day, when only a short window of compute is needed.
– We need to consider the scale out capabilities of the other services that ADF is going to invoke. Or, to put it another way, how much parallel activity execution do we want ADF to achieve? As you may know the ADF ForEach activity by default allows us to execution inner activities in parallel, but is that enough?

It’s a very interesting idea; read the whole thing.

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Mark Kromer walks us through two techniques we can use in Azure Data Factory to deal with schema drift:

Azure Data Factory’s Mapping Data Flows have built-in capabilities to handle complex ETL scenarios that include the ability to handle flexible schemas and changing source data. We call this capability “schema drift“.

When you build transformations that need to handle changing source schemas, your logic becomes tricky. In ADF, you can either build data flows that always look for patterns in the source and utilize generic transformation functions, or you can add a Derived Column that defines your flow’s canonical model.

Click through for the discussion and comparison. Schema drift has been the bane of Integration Services’s existence, so it’s good to see them tackling the idea in Azure Data Factory.

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