Everything a data engineer needs to work with PySpark DataFrames — from how Spark actually runs your code, to the everyday operations and the performance fixes that keep jobs from crashing.
Read MoreThree ways to add or change columns in PySpark — and one of them, used in a loop, quietly wrecks performance. Here's what each does and when to reach for it.
Read MoreYou see both in PySpark code and wonder which is "correct" or faster. The honest answer: they're the exact same function. Here's the proof — and the gotchas that actually cause bugs.
Read MoreEveryone assumes incremental is always the better choice. It isn't — for small tables it's over-engineering. Here's the real trade-off, a simple decision tree, and how to detect changes.
Read MoreReaching for MERGE by default is one of the most expensive habits in data engineering. The right write pattern depends on your source — here's the decision, one question at a time.
Read MoreGetting data in seems simple until the bill arrives or the SLA is missed. Two questions — what's the source, and how fresh must it be — decide the right pattern every time.
Read MoreOne storage account, four very different tools. Here's what Blob, File, Queue, and Table each do — and one app that uses all four — in the simplest way possible.
Read MoreThree ways to lay out a Delta table so queries read less data — and the senior skill isn't picking the newest one, it's knowing when NOT to switch. Explained with a bookstore.
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