A three-person team running $5B in Shopify GMV couldn't afford to babysit analytics infrastructure. Here's how they stopped debugging reconciliation failures and started shipping features.

Tinybird is a real-time data analytics platform for building fast and scalable data pipelines, offering tools for data ingestion, processing, and visualization. From this blog users can learn about stream processing, data warehousing, and analytics techniques to unlock insights from their data and make data-driven decisions.

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Order Editing, a three-person bootstrapped team processing $5B in Shopify GMV, replaced a failing DynamoDB analytics pipeline with Tinybird. Their custom DynamoDB setup with materialized views consumed ~50% of engineering bandwidth due to reconciliation failures, rate-limit issues, and inconsistent data that caused enterprise merchant complaints. After evaluating PostHog and finding it too heavy, they chose Tinybird for its API-first, queryable data layer. The new architecture routes Shopify webhooks and Web Pixel events through SQS into Tinybird, serving analytics via API endpoints. The migration took two months (mostly backfill work) and now handles 3M events/day for ~$300/month. Key friction points included QPS limits requiring batched multi-store queries, unclear materialized view backfill docs, and AI tooling referencing outdated CLI patterns.

How Order Editing Replaced a Failing DynamoDB Pipeline with Tinybird

When the numbers don't reconcile, enterprise merchants notice

The decision happened on a ping pong table in New Zealand