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Building a Reliable Shopify Integration at Scale

Data ReliabilityGraphQLETLShopifyAgencyEnterprise
Building a Reliable Shopify Integration at Scale

As one of Fivetran's highest-visibility data connectors, Shopify played a critical role in powering analytics for enterprise brands and agency partners. However, the connector's architecture—built originally on Shopify's webhook system—began to show reliability issues at scale. With growing demand from large clients like Under Armour, Allbirds, and OpenStore, we needed to rebuild the connector for accuracy, reliability, and scalability.

This case study outlines how I led the product strategy for migrating the Shopify connector to Shopify's GraphQL Bulk API—delivering 100% data accuracy, maintaining sync performance, and enabling the connector to scale with our Enterprise and Strategic customer segments.

The Problem

The existing Shopify connector relied heavily on webhooks to sync data. While fast and lightweight, webhook delivery was inconsistent. Over time, this led to:

  • Missed or dropped events
  • Sync failures and silent data discrepancies
  • Escalations from customers due to broken reports
  • Limited ability to support agencies managing multiple stores

The core issue: our architecture could no longer deliver the accuracy and reliability required by enterprise customers, and it was capping our ability to scale.

Goals

  • Eliminate data discrepancies and improve trust in reporting
  • Achieve 100% data accuracy through a more reliable sync mechanism
  • Maintain sync times within +10% of prior performance
  • Reduce support escalations and increase enterprise readiness

Solution

After evaluating options with engineering and support leads, we made the decision to migrate away from Shopify's webhook-based architecture and adopt Shopify's GraphQL Bulk API. This tradeoff sacrificed near-real-time syncs for guaranteed completeness, accuracy, and scalability—an essential shift for our Enterprise and Strategic customer base.

Key product decisions included:

  • Designing a phased migration plan to avoid data gaps during rollout
  • Building a new sync architecture optimized for batch processing
  • Maintaining system performance within SLA targets (+10% of original sync durations)
  • Implementing better observability and guardrails for support teams

Execution

  • Scoped and prioritized migration in collaboration with engineering and data reliability teams
  • Ran controlled rollouts starting with customers most impacted by webhook instability
  • Benchmarked performance throughout migration phases and set clear metrics for sync duration and error thresholds
  • Collaborated with Customer Success to gather feedback from early adopters and ensure minimal disruption
  • Partnered with support to build improved logging and alerting mechanisms for better incident visibility

Results

  • Achieved 100% data accuracy across all Shopify data syncs
  • Maintained sync durations within 10% of prior webhook-based approach (22 minutes vs 20 minutes)
  • Enabled agency customers to manage multiple Shopify stores at scale
  • Strengthened Fivetran's strategic position with Enterprise brands like Allbirds and OpenStore
  • Set a new internal standard for how to scale connectors for high-volume data sources

Key Takeaways

This initiative reinforced the value of scaling with customer trust in mind. While performance mattered, accuracy and consistency were essential for our enterprise users. By investing in a foundational system redesign, we unlocked growth, reduced support load, and delivered a connector that could scale with the most demanding data volumes.

The project also served as a blueprint for future migrations across other high-volume connectors and aligned closely with company-wide goals around Enterprise and Strategic account expansion.