Tableau integration for ecommerce reporting

iWeb builds ETL pipelines that extract data from ERP, PIM, OMS and storefronts on a schedule (hourly, nightly, or real-time), land it in a warehouse or lakehouse, transform it into modelled tables, and expose those tables to Tableau through a live or extract connection. The pipelines include deduplication, reconciliation and data-quality checks so Tableau sees clean, consistent data.

The warehouse semantic layer (curated tables and calculated fields) is the source of truth; Tableau's metrics reference it. iWeb defines metrics in the warehouse first, certifies them with business owners, then exposes them through Tableau. This separates metric governance from dashboard design, so multiple dashboards can reuse the same metric without duplication or drift.

iWeb documents metric ownership, calculation logic and freshness SLAs in a metadata layer. When business rules change (e.g. how you calculate revenue or margin), the metric owner updates the definition in the warehouse. Tableau users see the updated metric across all dashboards. iWeb monitors schema and metric changes so updates are tracked and reversible.

Yes. iWeb builds reverse-ETL pipelines that export Tableau-computed cohorts, segments or audiences to your CRM, email platform, ad tools and storefronts. These pipelines run on a schedule and include monitoring so you know when a segment exports successfully and when it fails.

iWeb wires alerting and observability around every pipeline. When a job fails or is delayed, your team is notified immediately, can check the error log and rerun the job if needed. For critical pipelines, iWeb may implement automatic retries or failover logic to minimise manual intervention.

For most use cases, nightly extracts are sufficient. For dashboards that need to reflect orders or inventory within hours, iWeb implements change-data-capture or event streaming from your commerce platform, so Tableau sees updates with lower latency. We tune the balance between freshness and load on your source systems.

iWeb builds a metadata layer that tracks column-level lineage from ERP or PIM fields, through warehouse transformations, into Tableau dimensions and metrics. Analysts can drill down from a dashboard number to understand how it was calculated, which source systems fed it, and when it was last refreshed.

iWeb implements schema versioning and field mapping so ETL pipelines adapt to source-system schema changes without breaking. When a vendor releases an update, iWeb tests the new schema, updates the transformation logic, and deploys the change without interrupting Tableau dashboards.

iWeb designs the warehouse and Tableau semantic layer with role-based access controls. Business analysts can query certified tables and metrics; data engineers can maintain the pipeline; executives see curated dashboards only. Tableau's permissions are aligned with data governance so teams see only the data they are responsible for.

iWeb designs pipelines and query patterns that scale with order volume and traffic. During peak trading, we tune extraction parallelism, load balancing and Tableau query caching so freshness is maintained and dashboard latency stays acceptable. We monitor CPU, memory and query times throughout the trading period.

iWeb maps old and new schema side-by-side in the warehouse, so Tableau dashboards continue to work against the legacy data while you test the new source system. Once the new system is live, iWeb updates the ETL logic to pull from the new source, and Tableau queries automatically switch to the new tables.

iWeb implements column-level security and row-level filtering so dashboards never expose raw customer names, emails or payment data. Sensitive columns are masked or tokenised in the warehouse; aggregated metrics (e.g. customer count by cohort) are available for analytics. iWeb documents which columns are PII and enforces access controls in Tableau and the database.

Tableau can support both. For historical trends and strategic analysis, nightly warehouse extracts are sufficient. For real-time operational dashboards (e.g. orders per minute, live inventory), iWeb implements event streaming or change-data-capture so Tableau sees updates within minutes. The trade-off is higher complexity and infrastructure cost.

iWeb maintains the warehouse pipelines and metric definitions. Business analysts or Tableau developers update the dashboards and reports to reflect new metrics or drill-down logic. iWeb provides runbooks and training so your team can make changes independently and test them before publishing to production.