Case study · Production data platform rebuild and operation

Making ecommerce reporting reliable from source to dashboard

An ecommerce brand had a live reporting stack across Shopify, Klaviyo, Triple Whale, Redo, custom Java processing, Azure, Snowflake, and Power BI. Greyfield rebuilt the path from source connectors through Snowflake and Power BI, then took responsibility for reporting for retention, inventory, planning, ecommerce, and campaigns.

Weekly to rare

pipeline failures, caught before users see them

~80%

fewer data errors

~35%

more Power BI usage

Technology

Snowflake

Airbyte

Shopify

Microsoft Power BI

At a glance

IndustryDTC apparel ecommerce

LocationUnited States

ChallengeThe reporting path had grown across commerce, marketing, processing, warehouse, and BI systems without one clear operating model.

EngagementProduction data platform rebuild and operation

ResultWeekly failures became rare after monitoring was added across the reporting path

The challenge

The reporting path had grown across commerce, marketing, processing, warehouse, and BI systems without one clear operating model. A failed connector, processing job, Snowflake model, refresh, or report could leave the business with stale or incorrect numbers. At the same time, teams needed new reporting for retention, inventory, planning, promotions, returns, and campaign performance.

Approach

What Greyfield did

Rebuild the path from sources to Snowflake

Reworked the flow across Shopify, Klaviyo, Triple Whale, Redo, custom Java processing, Azure, and Snowflake. Added job locks, timeouts, clearer logs, and failure signals so failures were blocked or easier to diagnose.

Rebuild the reporting layer

Traced Power BI metrics back through Snowflake models and source tables, then delivered reporting for customer retention, order frequency, product pathways, inventory, planning, ecommerce, returns, and campaign performance.

Run releases and monitoring across the full path

Put releases under source control, verified production changes, monitored daily jobs and report refreshes, and handled incidents from source connectors through Power BI.

Results

What changed for the client

Greyfield rebuilt the production path from source connectors through Snowflake and Power BI, then took responsibility for releases, monitoring, recovery, and new reporting. Average daily Power BI warehouse use was about 27% lower in the measured post-change window. This is an observed comparison, not a guaranteed long-term reduction.