This playbook breaks down how data pipelines work and when to use Python vs SQL. We'll walk through a real Meta Ads to BigQuery pipeline using the bronze/silver/gold model, plus share a production template you can use to build your own.
This playbook demonstrates how to build a modern Python data pipeline that extracts Meta Ads data to BigQuery, eliminating CSV exports and creating the warehouse foundation needed to reconcile platform-reported conversions with actual revenue across Meta, GA4, and sales data from a CRM.
This playbook extends the Meta Ads automation with AI image composition. We'll add a single LangGraph node that transforms product photos into marketing creatives using Gemini's vision model, then feeds these composed images into the existing campaign workflow.
This playbook shows how to use LangGraph workflows to generate campaign configs from simple inputs. We'll build a system that transforms product descriptions into complete campaign strategies with audience segments and ad variations, while preserving human approval before execution.
This playbook shows how to use configuration-driven campaign matrices to automate Meta Ads at scale. We'll build a system that transforms campaign configs into complete ad variations with targeting, budgets, and creatives, eliminating manual setup across dozens of ads.
This playbook shows how to build Pydantic models that enable agents to create Meta ad campaigns programmatically. We'll implement the complete workflow from image upload to live ads, with automatic validation for budgets, content safety, and platform requirements built into every step.
