DatriseAI-first ETL

PipeRun to Microsoft Power BI Integration

AI-first ETL from PipeRun into Microsoft Power BI. Governed entities, incremental sync, typed landing tables.

How Datrise loads PipeRun into Microsoft Power BI

Datrise syncs PipeRun's deals, contacts, companies, pipeline stages, activities, and sales teams into Microsoft Power BI as star-schema-friendly tables for an Import or DirectQuery dataset. Flexible or custom fields land in flattened columns (nested fields expanded), and timestamps such as created, updated, and status changes are typed as date/time columns with a date table.

Sync is incremental: Datrise uses incremental-refresh windows aligned to Power BI's RangeStart/RangeEnd, so re-runs update only what changed. Date-partitioned fact tables matching incremental-refresh policies. Power BI measures and relationships live in the .pbix model, so Datrise keeps the underlying tables stable and star-schema-shaped.

Ideal for Microsoft-stack self-serve reporting.

Endpoints

PipeRun: Sales pipeline management for Brazilian revenue teams.

Microsoft Power BI: Microsoft business intelligence with datasets, reports, and semantic models.

How PipeRun entities map to Microsoft Power BI

PipeRun entityMicrosoft Power BI objectNotes
dealspiperun_dealsid PK · custom fields → flattened columns (nested fields expanded)
contactspiperun_contactsid PK · linked to piperun_deals
companiespiperun_companiesid PK · linked to piperun_deals
pipeline stagespiperun_pipeline_stagesid PK · linked to piperun_deals

FAQ

How does Datrise handle PipeRun's custom fields in Microsoft Power BI?

Flexible values are stored as flattened columns (nested fields expanded), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Microsoft Power BI types.

How does the PipeRun to Microsoft Power BI sync stay up to date?

It runs incrementally — Datrise uses incremental-refresh windows aligned to Power BI's RangeStart/RangeEnd.

Related pipelines

Early access

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