I've made this argument before on this blog — in the context of Hive scripts in 2012, in the context of MapReduce jobs in 2013. I'm going to make it again in 2016 because the tools are better now, the excuses are thinner, and the data teams
In 2012, I wrote about treating Azure VM gallery images as release artifacts — versioned, tested, deployable artifacts that embodied infrastructure state rather than manual configuration procedures. In 2016, containers made the same idea faster, lighter, and more widely applicable. The progression from bare metal to VM images to container images
After the CPU multithreading benchmark that beat SQL Server's parallelism by 3.5×, the natural next question was: what's the ceiling? An 8-core CPU at full utilization was fast. But a GPU has hundreds or thousands of cores. For the right computation, the performance difference
Spark 2.0 shipped in July 2016. The headline features are Structured Streaming and a 2x performance improvement on TPC-DS benchmarks via a rewritten query optimizer. Both are real and worth covering. But the feature I want to focus on is the one getting the least attention in the
The Integration Runtime is ADF v2's answer to a question v1 never asked explicitly: where does this activity actually execute? In v1, cloud activities ran on Microsoft-managed compute, on-premises activities ran on the DMG server, and nobody made you think about it much. In v2, the
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