Field notes on autonomous data.

Guides, product news, and perspective on moving data teams from maintenance to ownership.

Featured · Perspective

AI agents for data engineering: what they actually do

Beyond the hype — what an autonomous data engineering agent really is, where it helps most, and why human-in-the-loop governance is non-negotiable.

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Latest
Guide

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.

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Guide

Change Data Capture (CDC), explained

What CDC is, why it beats batch, and the methods — log-based, query-based, trigger-based — with their trade-offs.

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Guide

ETL vs ELT: what changed, which to use

Why ELT overtook ETL in the cloud era, when ETL still fits, and a practical way to decide.

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Guide

Replicate Postgres to BigQuery in real time

The approaches, the hard parts — schema drift, type mapping, deletes, backfill — and a reference setup.

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Engineering

Log-based vs query-based CDC

The three CDC methods compared on source impact, completeness, and latency — and when each wins.

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Engineering

Exactly-once delivery in data pipelines

At-most, at-least, exactly-once — what each guarantee means, why exactly-once is hard, and how systems get there.

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Guide

Schema drift: what it is, how to handle it

Why source schema changes break pipelines — and the strategies that keep data flowing safely.

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Guide

What is reverse ETL?

Syncing modeled warehouse data back into the operational tools business teams live in — data activation, explained.

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Guide

Data pipeline pricing explained

MAR, rows moved, connectors, credits — how the models work and how to estimate your real bill.

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Guide

Migrating off Informatica: a practical path

A structured, lower-risk alternative to a multi-quarter GSI engagement — inventory, convert, validate, cut over.

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See it for yourself.

The best resource is a live demo on your own data.