Apr 27 · 13 min read · You monitor schema changes in a data warehouse by periodically querying metadata catalogs (like INFORMATION_SCHEMA), subscribing to event-driven notifications, or comparing structural hashes of your tables over time. Each method trades off between de...
Join discussionApr 20 · 19 min read · Data downtime is the total period during which data is missing, erroneous, or otherwise unfit for use. It is the data equivalent of application downtime: the window between when something breaks and when it is fully resolved. During data downtime, da...
Join discussionApr 12 · 11 min read · The six dimensions of data quality are accuracy, completeness, consistency, timeliness, validity, and uniqueness. Each dimension measures a different aspect of whether data is fit for its intended use. Together they define whether a dataset can be tr...
Join discussionApr 11 · 9 min read · Spend five minutes in any data engineering forum and you'll find the same confession repeated in different words: "We just eyeball row counts and pray." It shows up on Reddit, Hacker News, the dbt Community Forum, Stack Overflow. The phrasing changes...
Join discussionApr 11 · 9 min read · Data observability and data quality get used interchangeably, but they solve different problems. Confusing them leads to buying the wrong tool, building the wrong monitors, and missing the issues that actually break things. Here's the short version: ...
Join discussionApr 11 · 7 min read · Last Tuesday a source team renamed order_status to status in Snowflake. No announcement. Your dbt models kept running, but every query referencing order_status silently returned NULLs. The revenue dashboard showed a 40% drop. It took four hours to tr...
Join discussionMar 26 · 9 min read · So I gave myself a challenge, to build a production-grade data pipeline from scratch, using real industry tools, at zero cost, and document every stumble along the way. This is that story. The Proble
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Mar 19 · 14 min read · I knew something was wrong the first time I saw a “candidate” come back with the recruiter’s phone number. Nothing was broken in the obvious places. Extraction ran. Persistence succeeded. The UI showed a clean-looking result. But the identity was wro...
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Mar 19 · 9 min read · I noticed something that felt impossible at first: the more enrichment I added, the more often my records got worse. Not catastrophically. Not in a way that threw errors. Just… quietly. A city would flip to something nearby. A state would “correct” i...
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Mar 19 · 14 min read · I found the bug the way you always find the worst ones: not in a unit test, not in staging, but in a "why is this field blank again?" message after a real sync. The Zoho CRM API call looked correct. The request even included a fields parameter. And y...
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