Ecommerce Data Pipeline Setup —
Shopify, Amazon & Klaviyo Unified in 4–6 Weeks

Ecommerce data infrastructure is the layer of pipelines, storage, and integrations that collect, clean, and organise your sales, inventory, and customer data into a format that AI models and analytics tools can use. Without it, AI models train on incomplete or manually exported data and produce unreliable output. For Shopify and WooCommerce operators, this typically means connecting your storefront, warehouse system, and marketing tools into a single unified schema.

For most ecommerce businesses doing $1M to $20M, a structured data warehouse is the right starting point. A data lake stores raw files in their original format — useful at enterprise scale but harder to query and govern. A warehouse stores cleaned, structured data ready for reporting and model training. We recommend a warehouse for AI use cases like demand forecasting and returns prediction, and only add a lake layer if raw event volume justifies it.

It depends on the use case. Demand forecasting needs 12 to 18 months of SKU-level sales history to capture seasonality reliably. A returns prediction model can start with 6 months of returns data. A recommendation engine can work with as little as 90 days of click and purchase events. The data audit in Phase 1 tells you exactly what you have and which use cases your current history can support.

Yes. We connect to Shopify via the Admin API and pull orders, products, inventory, customers, and events on an automated schedule. We also work with WooCommerce, Amazon Seller Central, Magento, and custom stacks. For marketing data we integrate Klaviyo, GA4, and Segment. If you use a warehouse management system or ERP, we build a custom connector.

The typical engagement runs 4 to 6 weeks from kick-off to a validated, AI-ready data layer. Phase 1 (audit) takes one week. Phase 2 (build) takes three weeks. Phase 3 (validation and handover) takes one to two weeks. Timeline depends on the number of platforms, the volume of historical data, and whether there are significant data quality gaps to resolve.

SaaS analytics tools read data — they do not clean, transform, or structure it for AI. A Looker dashboard connected to your Shopify store shows you reports; it cannot train a demand forecasting model or a returns risk classifier. The Plumbing builds the layer underneath: clean, normalised, unified data that both analytics tools and AI models can sit on top of. We also handle deduplication, data quality checks, and schema design that SaaS tools assume someone else has already done.

We connect Shopify to BigQuery, Snowflake, Redshift, or PostgreSQL via the Shopify Admin API. The connection pulls orders, products, inventory, customers, and events on a daily automated schedule. We handle schema design, data type normalisation, and incremental loading — so your Shopify data warehouse setup is production-grade from day one, not a fragile script that breaks on every API change.

We use tools that fit your existing stack rather than forcing a new platform. For transformation we use dbt (data build tool) to clean, join, and model raw data into a structured schema. For orchestration we use Apache Airflow or a managed equivalent. For ingestion we build custom connectors or use Fivetran where it reduces build time. The output lands in BigQuery, Snowflake, or PostgreSQL — whichever you already use or we recommend based on your data volume and budget.