Google BigQuery integration for ecommerce reporting

Start with order transactions (orders, line items, returns) and customer data from your commerce platform, then add ERP data (invoices, stock movements, GL entries) and PIM product data. This core foundation powers most dashboards and segments. Expand to behavioural events, CRM data and payments as teams demand analytics depth.

iWeb establishes freshness SLAs for each source system (e.g., orders loaded within 15 minutes, inventory within 1 hour, product changes within 2 hours). We build a dashboard that tracks when each pipeline last ran successfully and alerts data owners if loads fall behind the SLA.

iWeb builds extraction logic that handles schema changes gracefully (new fields are ignored or mapped to new columns; removed fields cause alerts). If a source is offline, the pipeline pauses and alerts the engineering team. iWeb designs fallback logic so dashboards stay available on last-known-good data while the issue is resolved.

iWeb builds data quality rules that run after every load, checking for missing values, type mismatches, duplicates and referential integrity gaps. Failed rows land in a quarantine table; alerts go to data owners; dashboards pull only from validated data.

iWeb designs the initial curated tables and metrics, but ownership transfers to your analytics and BI teams. We establish a process: new metrics or changes to existing definitions go through review, version control and approval before they reach dashboards. This prevents silent changes that break trust.

iWeb builds reverse-ETL flows that query BigQuery on a schedule, extract audience rows, match customer IDs and push the list to your marketing platform API or SFTP. We handle incremental updates (add new segment members, remove churned ones) and monitor delivery so marketers see counts and know when exports succeed or fail.

iWeb implements cost monitoring by team or business area, identifies slow queries and implements slot reservations if needed. We set query budgets and alert when teams approach limits. We also partition tables, compress data and optimize schemas to keep scans efficient and costs predictable.

Teams can query directly using SQL, through a BI tool like Looker or Tableau, or through the semantic layer iWeb publishes. iWeb trains teams on the curated tables and safe query patterns so analysts can self-serve without needing to understand the full warehouse schema.

iWeb monitors query health and alerts owners if a query fails (e.g., because a source table was dropped or a field was removed). We maintain a change log and rollback procedures so teams can revert definitions quickly. For critical segments used in campaigns, iWeb sets up monitoring so failures trigger alerts before campaigns go out with stale audiences.

iWeb designs data governance so PII (customer names, emails, payment details) is tagged, masked or excluded from user-facing views. We implement role-based access control so only authorized teams can see sensitive data. We document data lineage so compliance teams can audit where PII flows and who accesses it.

Yes, if iWeb has designed the warehouse schema to abstract away platform-specific details. When you migrate to a new platform, iWeb remaps the extraction logic to the new source schema and validates that the curated tables remain unchanged. Dashboards and segments stay stable during the replatform.

iWeb designs Type-2 slowly-changing dimensions that track history (effective dates, version numbers) so you can see what values were active at any point in time. This enables accurate historical analysis (e.g., revenue by product attribute as it was when the order was placed, not as it is today).

iWeb publishes dashboards that track pipeline success rates, data freshness, quality rule failures, query performance and costs. Alerts flow to Slack or email when pipelines fail, data is stale, quality rules breach or costs spike. Teams see the health of the warehouse in realtime.

BigQuery can ingest both batch and streaming data. iWeb designs batch flows for slower-moving data (inventory, product changes) and streaming for high-velocity events (orders, customer actions). Both land in the same tables and can be queried together, so dashboards show a unified view.