Microsoft Fabric integration for ecommerce reporting

Start with the core systems: the commerce platform (orders, customers, events), ERP (stock, pricing, accounts), OMS (routing, allocation, status), WMS (dispatch, returns), and CRM (customer interactions). Decide extraction frequency based on use case: finance reconciliation needs hourly or daily, dashboards can run nightly, and planning tools might pull weekly forecasts. The integration team should define SLAs upfront so BI teams know what freshness to expect.

Define data ownership clearly: who owns the customer table, the product table, the revenue metrics, the forecasts. Write it down. Assign freshness SLAs to each domain. Set up monitoring and alerting so late or broken extracts are caught immediately. Require BI teams to document which tables feed which dashboards, and which dashboards drive decisions. Without this, the warehouse becomes a sprawl of experimental tables.

Schema changes should trigger alerts in the monitoring pipeline. Extract logic should validate against an expected schema before loading into the warehouse. If validation fails, the row should go to a dead-letter queue for inspection. The integration team should test schema changes from source systems in a staging environment before they hit production, and have a rollback plan if a change breaks the extract.

Before building models, map how customers, orders and accounts are identified across systems. Document the join rules: which field in commerce matches which field in ERP, where account hierarchies differ, how to handle orphaned records. Build a reference table (customer mapping, order matching) that the integration team maintains. Test joins for completeness: every commerce order should match an ERP order, every customer should reconcile. If joins leak, analytics is wrong.

Yes, via reverse-ETL pipelines. Curated customer segments, demand forecasts and adjustments can be published back to commerce platforms, marketing systems and OMS. The integration team builds and monitors these flows. Each reverse-ETL pipeline needs transformation logic, error handling, idempotency (so duplicate sends don't corrupt data) and reconciliation checks. If a reverse-ETL fails, the update doesn't land and teams need to know immediately.

Build a reconciliation pipeline that calculates total revenue, cost and margin in Fabric, then compares it to ERP general-ledger totals. Differences point to missing orders, duplicate captures, missing refunds or wrong cost allocations. Schedule this to run daily or weekly. Publish the reconciliation report to finance and operational teams so they can investigate breaks before they close the books. Fabric shouldn't be the source of truth for finance; it should verify ERP is accurate.

Extracts will fail. The warehouse will hold the last good snapshot of data. Dashboards will show stale information unless you explicitly alert teams that extracts have stopped. Build monitoring that detects extract failure within minutes. Decide how long stale data is acceptable before dashboards should go offline or be marked 'data not current'. Plan for graceful degradation: some dashboards can run on cached data, others need to fail safe.

Duplicates usually arise when customer IDs or order numbers differ across systems, or when extracts include both online and offline transactions with overlapping identifiers. Build a deduplication layer in the modelled tables that marks which records are duplicates and keeps only the canonical version. Document the deduplication rules. Test the models against known duplicates from the source systems. Finance teams need to understand why order counts in Fabric differ from order counts in commerce or ERP.

It depends on use case. Finance reconciliation should be daily or hourly. Inventory dashboards should be near real-time or at least hourly. Customer analytics can be nightly. Demand forecasts might be weekly. Agree SLAs with business owners before building the extract. Make SLAs explicit and monitorable. If a source system can't support the SLA (e.g. ERP only exports stock once a day), BI teams need to know and plan accordingly. Don't promise real-time if the source can only deliver daily.

Publish a curated semantic layer of trusted metrics and tables that teams should use. Make it easy to build dashboards from these. For any new dashboard, require teams to document its owner, the use case, the freshness SLA it depends on, and who is alerted if data fails. Review new dashboards before they go live. This slows down initial development but prevents dashboards from becoming dark and untrustworthy over time.

Monitor extract latency (is data arriving on schedule?), row counts (are we getting fewer orders or customers than expected?), schema changes (has the source API changed?), reconciliation variance (do Fabric totals match ERP?), and reverse-ETL success rates (are segments being published?). Alert the integration team on extract failures, the BI team on metric anomalies, and finance on reconciliation breaks. Make dashboards for these operational metrics so teams can see the health of the warehouse in real time.

Use version control for all extract code, transformation logic and model definitions. Tag releases and maintain a change log. Test new extract logic in a staging environment before deploying to production. For model changes, plan a transition period where old and new metrics are published side-by-side so teams can validate the new logic. Document breaking changes and how teams should update their dashboards. Without this discipline, redeploying a change can silently break dashboards.