Firebolt to Sisense Integration

How Datrise loads Firebolt into Sisense

Datrise syncs Firebolt's databases, tables, aggregating indexes, query jobs, table schemas, and partitions 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

Firebolt: Cloud data warehouse tuned for fast analytics.

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

How Firebolt entities map to Sisense

Firebolt entity	Sisense object	Notes
databases	`firebolt_databases`	id PK · custom fields → flattened columns for the cube
tables	`firebolt_tables`	id PK · linked to firebolt_databases
aggregating indexes	`firebolt_aggregating_indexes`	id PK · linked to firebolt_databases
query jobs	`firebolt_query_jobs`	id PK · linked to firebolt_databases

FAQ

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

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

Related pipelines

Early access

Connect Firebolt to Sisense the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.