Snowflake to Sisense Integration

How Datrise loads Snowflake into Sisense

Datrise syncs Snowflake's databases, schemas, tables, views, query history, and warehouses 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

Snowflake: Cloud data platform with separated storage and compute.

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

How Snowflake entities map to Sisense

Snowflake entity	Sisense object	Notes
databases	`snowflake_databases`	id PK · custom fields → flattened columns for the cube
schemas	`snowflake_schemas`	id PK · linked to snowflake_databases
tables	`snowflake_tables`	id PK · linked to snowflake_databases
views	`snowflake_views`	id PK · linked to snowflake_databases

FAQ

How does Datrise handle Snowflake'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 Snowflake to Sisense sync stay up to date?

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

Related pipelines

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