CSV File to Sisense Integration
AI-first ETL from CSV File into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads CSV File into Sisense
Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings 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
CSV File: Delimited flat files from exports and drops.
Sisense: Analytics platform with elastic data models and embedded analytics.
How CSV File entities map to Sisense
| CSV File entity | Sisense object | Notes |
|---|---|---|
| rows | csv_file_rows | id PK · custom fields → flattened columns for the cube |
| columns | csv_file_columns | id PK · linked to csv_file_rows |
| headers | csv_file_headers | id PK · linked to csv_file_rows |
| delimiters | csv_file_delimiters | id PK · linked to csv_file_rows |
FAQ
How does Datrise handle CSV File'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 CSV File to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
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