Migration from Fivetran to Airbyte for GA4 and Magento
- Data and Cloud Migrations, Data Engineering, Open Source Ingestion
The Challenge
An eCommerce platform relied on Fivetran to integrate data from Google Analytics 4 and Magento into their warehouse. As data volume increased, Fivetran’s monthly active row pricing became unsustainable. The client also faced limitations in connector customization, scheduling control, and transformation flexibility.
They needed a scalable ingestion solution that would support both current operations and future growth while reducing ongoing integration costs.
Key pain points
Rising costs driven by Fivetran MAR pricing model
Limited ability to customize connectors and sync logic
Vendor bound infrastructure and SLA constraints
Need for more control over reporting specific workflows
Business Goal
Cost Model
Reduced integration tooling cost by replacing MAR based pricing with open source ingestion
Pipeline Control
Customizable sync scheduling and transformations for reporting needs
Flexibility
Improved ability to support future sources without vendor constraints
Zero Downtime Cutover
Migration completed with no data loss or business disruption
Tech Stack
What We Did
Approach
We proposed migrating ingestion workflows from Fivetran to Airbyte’s self hosted open source platform.
We began with a full assessment of the client’s existing pipelines, including GA4 and Magento data flow frequency and transfer volume. Airbyte was then installed and configured within the client’s environment to ensure infrastructure compatibility.
New ingestion pipelines were established from GA4 and MySQL (Magento) directly into Snowflake. To ensure a clean transition, we created a dedicated Snowflake schema for Airbyte ingested data and recreated all existing transformation logic in dbt.
Extensive validation was performed by comparing Airbyte outputs against Fivetran processed data. Once parity was confirmed, all reporting models were fully transitioned to Airbyte.
Outcomes
70% Reduction in Data Integration Costs
Achieved by transitioning to a more efficient ingestion architecture and eliminating reliance on high-cost native connectors.
40% Faster Data Pipeline Execution
Optimized ingestion and transformation workflows significantly improved data processing speed and reduced latency.
Zero Downtime and No Data Loss During Migration
Ensured a seamless transition to the new architecture with uninterrupted data availability and complete data integrity.
3x Improvement in Pipeline Flexibility and Customization
Enabled through the use of modular tools and custom connectors, allowing greater control over data ingestion, transformation, and scaling.
Related Work
