DatriseAI-first ETL

Parquet File ThoughtSpot

AI-first ETL from Parquet File into ThoughtSpot. Governed entities, incremental sync, typed landing tables.

How Datrise loads Parquet File into ThoughtSpot

Datrise syncs Parquet File's records, events, and configuration objects into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.

Ideal for natural-language search analytics over a warehouse.

Endpoints

Parquet File: SaaS or API data source for analytics and warehouse sync.

ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.

How Parquet File entities map to ThoughtSpot

Parquet File entityThoughtSpot objectNotes
recordsparquet_file_recordsid PK · custom fields → flattened columns for searchable fields
eventsparquet_file_eventsdate/time columns events
configuration objectsparquet_file_configuration_objectsid PK · linked to parquet_file_records

FAQ

How does Datrise handle Parquet File's custom fields in ThoughtSpot?

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

How does the Parquet File to ThoughtSpot sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the indexed tables.

Related pipelines

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