DatriseAI-first ETL

Aircall to Sisense Integration

AI-first ETL from Aircall into Sisense. Governed entities, incremental sync, typed landing tables.

How Datrise loads Aircall into Sisense

Datrise syncs Aircall's calls, users, teams, phone numbers, tags, and call recordings into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Aircall: Cloud phone system for sales and support teams.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Aircall entities map to Sisense

Aircall entitySisense objectNotes
callsaircall_callsid PK · custom fields → flattened columns for the cube
usersaircall_usersid PK · linked to aircall_calls
teamsaircall_teamsid PK · linked to aircall_calls
phone numbersaircall_phone_numbersid PK · linked to aircall_calls

FAQ

How does Datrise handle Aircall's custom fields in Sisense?

Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.

How does the Aircall to Sisense sync stay up to date?

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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