Great Expectations in a Data Science Workflow: Catching Bad Input Before Your Model Sees It

Machine learning models have a talent for finding patterns in garbage data. Give a model training data with a systematic error and it will dutifully learn to predict based on that error. The model validates fine. It performs great in testing. It ships to production. Then the underlying data error

The Hive Metastore for SQL Server DBAs: A Translation Guide

ADF Expression Language: Power, Quirks, and Learning to Stop Fighting It

The ADF expression language is the thing that makes parameterized pipelines actually work. It's the DSL for wiring dynamic behavior into your pipeline configuration: reference parameters, pull values from previous activity outputs, compute dates, build dynamic strings. It runs inside the ADF orchestration layer — not in Spark, not

Window Functions in Spark SQL: What Carries Over from T-SQL

Understanding DBFS: What It Is and What It Isn't

Stop Using Notebooks Like SQL Editors

Writing Custom Expectations in Great Expectations

The built-in expectation library covers the common cases well: nulls, ranges, value sets, row counts, regex patterns. But real data has domain-specific rules that a general-purpose library can't anticipate. A storm event can't end before it starts. A geocoded latitude must correspond to a valid US

ADF Mapping Data Flows: Code-Free Transformations Built on Spark

The single biggest complaint about ADF since 2014 has been the same: you can copy data, but you can't transform it. Copy Activity moves data from A to B. If you need to derive a column, aggregate rows, join two sources, or apply business logic — you had to

Cluster Autoscaling Isn't Magic: Tuning It for SQL Workloads

Delta Lake Isn't Just a Better Data Lake