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Microsoft previewed ADF v2 and it's a ground-up redesign. Not an incremental update. Not a new version of the same architecture with better connectors. A different product that happens to share a name with its predecessor. I've been spending time with the preview. Here's
One of the projects I'm most proud of from this period was building the automation that staged all of NOAA, USDA, and NCIS data into our Azure Data Lake. I say "proud" not because the code was clever — it was mostly straightforward download-and-stage logic — but because
The file format you choose for your data lake is a ten-year decision. You probably don't treat it that way — it shows up as a checkbox in a sprint, "store processed events in S3," and the format is whatever is convenient today. But that choice determines
When I wrote my first tSQLt test in late 2012, unit testing SQL Server stored procedures was enough of a novelty that I had to explain what unit testing was to the DBAs I worked with. That's changed. Four years of using tSQLt across projects has sharpened my
When ADF shipped in 2014, the connector library was the charitable way to describe it. There were enough sources to make a convincing demo. There weren't enough to handle real enterprise integration scenarios without falling back to custom code on a significant fraction of your workloads. Two years
The Scala vs. Python debate for Spark has been running since Spark launched. Two years ago, the Scala camp had a real argument: Python's PySpark API lagged behind, serialization overhead for Python UDFs was measurable, and if you were doing anything performance-sensitive, Scala was the right call. That
In March 2016, Microsoft announced SQL Server on Linux. I read the press release twice to make sure I had it right. The product that had been a Windows-only server application for its entire 20+ year existence was going to run on Ubuntu and Red Hat. And shortly after: SQL