Field notes on autonomous data.
Guides, product news, and perspective on moving data teams from maintenance to ownership.
AI agents for data engineering
What an autonomous data engineering agent really does, where it helps most, and why human-in-the-loop matters.
Read →What is a data product?
A dataset built like software — owned, tested, documented, and governed — so the org can trust it and build on it.
Read →Change Data Capture (CDC), explained
What CDC is, why it beats batch, and the methods — with their trade-offs.
Read →ETL vs ELT: what changed, which to use
Why ELT overtook ETL, when ETL still fits, and a practical way to decide.
Read →Replicate Postgres to BigQuery in real time
The approaches, the hard parts, and a reference setup.
Read →Log-based vs query-based CDC
The three CDC methods compared — and when each wins.
Read →Exactly-once delivery in data pipelines
What the delivery guarantees mean and how systems achieve exactly-once.
Read →Schema drift: what it is, how to handle it
Why source schema changes break pipelines — and how to keep data flowing.
Read →What is reverse ETL?
Syncing modeled warehouse data back into operational tools — data activation, explained.
Read →Data pipeline pricing explained
MAR, rows, credits — how the models work and how to estimate your bill.
Read →Migrating off Informatica: a practical path
A structured, lower-risk alternative to a multi-quarter GSI engagement.
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