Home
Resources

Resources

Guides for teams who live in their ad data.

Practical write-ups on Amazon Ads and AMC, retail media data, warehouses, attribution and AI agents, from the engineers who build them.

Get an ad data plan See case studies

What you get

Why read these.

From the people who build it

Write-ups by the engineers who build these pipelines, dashboards and agents.

Ad data, end to end

Amazon Ads and AMC, retail media, warehouses, attribution and AI agents.

Free to read

No sign-up wall. Read, share, and ask us about anything that applies to you.

Amazon Ads & PPC

Amazon PPC Automation: How AI-Driven Bid Management Actually Works

How Amazon PPC automation works: AI bid management, dayparting and rules engines that replace manual campaign work without wasting ad spend.

Read the article

Amazon Ads & PPC

Amazon Marketing Cloud (AMC), Explained: What It Is and Who Actually Needs It

How Amazon Marketing Cloud works in practice: instances and regions, the schema, privacy thresholds, a worked SQL example, and running it in production.

Read the article

Data Engineering

ETL Pipelines for Amazon Sellers: How to Unify SP-API, Ads API, and Inventory Data

Amazon sellers juggle data across SP-API, the Ads API, and spreadsheets. Here's how a proper ETL pipeline unifies it into one reliable source of truth.

Read the article

Data Engineering

Retail Media Data Engineering: Unifying Amazon, Walmart, and Instacart Reporting

Retail media spend is spread across Amazon, Walmart, Instacart and more, each with its own dashboard. How to build one data model across all of them.

Read the article

Data Engineering

Amazon SP-API vs Ads API: A Technical Guide for Data and Engineering Teams

SP-API and the Amazon Ads API cover different data, with different rate limits, auth flows and report models. How to use both in one pipeline.

Read the article

Data Engineering

How to Scale an Amazon Agency: Data Infrastructure Lessons from 35 to 3,500+ Brands

Scaling an Amazon agency from dozens to thousands of brands breaks manual reporting completely. Here's the infrastructure that makes that scale possible.

Read the article

Data Engineering

How to Build a Marketing Data Warehouse with dbt and Snowflake

How to build a marketing data warehouse with dbt and Snowflake: architecture, modeling layers, metric definitions and the mistakes to avoid.

Read the article

Data Engineering

Server-Side CAPI at Agency Scale: The Implementation Guide

How to implement Meta CAPI, Google Enhanced Conversions and TikTok Events API server-side: deduplication, match quality and CRM enrichment at scale.

Read the article

Data Engineering

Building a Custom Marketing Mix Model (MMM): A Practical Guide

What marketing mix modeling is, when it beats attribution, how Meridian and Robyn work, and what it takes to build an MMM your team trusts.

Read the article

Data Engineering

First-Party Data Clean Rooms, Explained (AMC, Ads Data Hub & Beyond)

What data clean rooms are, how Amazon Marketing Cloud, Ads Data Hub and Meta Advanced Analytics work, and when a clean room is worth the effort.

Read the article

Amazon Seller Ops

Amazon Account Suspension: A Data-Driven Approach to Faster Reinstatement

Amazon account and ASIN suspensions are time-sensitive and document-heavy. How structured case management and clean records speed up reinstatement.

Read the article

Amazon Seller Ops

Buy Box Monitoring and Inventory Forecasting: The Data Systems That Prevent Lost Sales

Losing the Buy Box or running out of stock both cost sales silently. Here's how monitoring and forecasting systems catch both before revenue is lost.

Read the article

AI & Automation

AI Agents for Advertising: Letting Agents Act Within Your Rules

Where AI ad agents genuinely help, how to keep them inside rules you set, and the data foundation they need before they touch a bid or a budget.

Read the article

AI & Automation

AI Insights Grounded in Your Ad Warehouse: Asking Your Ad Data Plain-English Questions

How to let your team ask plain-English questions of your ad data and get answers grounded in your own warehouse, using RAG over a semantic layer.

Read the article

Rather talk it through?

Bring us your messiest ad report and an engineer will walk you through it.

Talk to an engineer