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A.I. Commerce Studio

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Data pipeline to an analytics warehouse

Sales, marketing and operations data is collected, checked and modelled in a warehouse with clear owners.

Fictional scenario. An example situation that illustrates the approach. It is not a client project and describes no measured results.

Business problem

A fictional retailer builds reports in spreadsheets from different exports. Numbers differ between departments.

Assumptions

  • Sources have APIs or exports.
  • There is a cloud data warehouse or one will be chosen.
  • Departments agree on definitions.

Constraints

  • Personal data only where needed.
  • Every metric has an owner.
  • No manual corrections in the warehouse.

Reference architecture

Components by stage — an illustration, not a running system and not a certified design.

  1. 01Sources

    • CRM
    • Shop
    • Advertising

  2. 02Ingestion

    • Scheduled extraction
    • Raw tables

  3. 03Transformation

    • Tested models
    • Metric definitions

  4. 04Output

    • Reports
    • Analysis datasets
The diagram as a list
StageComponents
SourcesCRM, Shop, Advertising
IngestionScheduled extraction, Raw tables
TransformationTested models, Metric definitions
OutputReports, Analysis datasets

Interfaces

FromToVia
SourcesIngestionAPIs and exports
IngestionWarehouseBatch loads
WarehouseReportsRead-only views

Data flow

  1. Data is extracted on a schedule.
  2. Raw data is kept unchanged.
  3. Transformations are code with tests.
  4. Metrics follow agreed definitions.
  5. Reports read only the models.

Security boundaries

  • Personal data is masked in the analytics layer.
  • Read roles per department.
  • Source secrets in a store.

Where a person approves

  • A change to a metric definition.
  • A new source with personal data.

Delivery phases

  1. 01 · Discovery

    Metrics, sources and owners.

  2. 02 · Foundation

    Ingestion and raw tables.

  3. 03 · Models

    Transformations and tests.

  4. 04 · Operation

    Freshness and quality monitoring.

Limitations

  • Quality depends on the sources.
  • Historical data may be incomplete.

Trade-offs

  • Batch vs streaming — simplicity vs freshness.
  • Strict definitions — alignment work up front.

Related services

Related assessments

Integration patterns used

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