The Small Files Problem in Spark: How to Detect and Fix It
41,600 files holding 14.8 GB took 34 minutes just to scan. Here is what causes the small files problem, the exact numbers that reveal it in the Spark UI, and four ways to fix it.
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PySpark Data Serialization: Why Python UDFs Are Slow
Spark runs on the JVM. Python UDFs do not. Every row has to be converted between the two — and here is what that costs, how to spot it, and how to fix it.
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Azure Storage Redundancy Explained: LRS vs ZRS vs GRS vs GZRS
Six redundancy options are really three yes/no questions. What each one protects against, why none of them is a backup, and how to pick per medallion layer.
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Azure Databricks Instance Types Explained: Which Worker Type to Choose
General Purpose, Memory Optimized, disk cache accelerated, Storage Optimized, GPU — what each one actually solves, and how to read the VM name so the dropdown stops being a guess.
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Databricks Clusters Explained: Why My Pipeline Doubled the Bill
A walkthrough of what each Databricks compute type actually is — job cluster, all-purpose, SQL Warehouse, serverless — told through one pipeline that quietly cost twice what it should have.
Read MorePySpark DataFrame Guide: Columns, Filtering & Performance
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 MorePySpark withColumn vs withColumns vs select: Which to Use
Three 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 MorePySpark filter() vs where(): Is There Any Difference?
You 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.
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