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Articles by Laura Chicovis
Stop bad data at the pull request with Great Expectations and GitHub Actions
By the end of this post, a pull request that breaks your data will fail in GitHub before anyone can merge it. Most data quality checks run after the data lands. But often, what broke it was a code change approved an hour earlier. So let's check it right there, in the pull request, with three pieces: A data contract in YAML. A Python script that turns it into Great Expectations (GX) checks. A GitHub Actions workflow that runs the script on every pull request. All the files are below, ready to copy.
Open or closed model? There is a third option
Open or closed? The question shows up in every architecture review, and it usually gets settled with a benchmark chart and a price-per-token comparison. But it often gives the wrong answer, because the two options on the table are not the two you have. There is a third sitting between them, and for most workloads it wins. Why the binary is badly posed The usual reasoning runs like this. An H100 rents for a few dollars an hour, while a frontier API charges a markup per token on that same hardware.
How to run generative AI on SQL tables with Snowflake Cortex
The question that decides whether Cortex belongs in your stack is not what it can do. It is what it costs once the table has millions of rows instead of five. That question exists because the model call is an ordinary SQL function. It sits inside a SELECT, composes with WHERE, JOIN and GROUP BY, and runs once per row. So this walkthrough goes in that order. Access first, then the functions on a small slice, then the credit consumption before anything scales up.
How to integrate Apache Airflow with OpenLineage for end-to-end traceability
By the end of this walkthrough, every DAG run in your Airflow instance emits structured lineage events that name the exact tables each task read and wrote, and you can open a graph and answer "which upstream job produced this number" without grepping a single scheduler log. That is the whole promise. No manual documentation, no lineage spreadsheet that goes stale in three weeks. The orchestrator reports what it actually did, while it is doing it. The setup itself is short.
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