Parquet File to ThoughtSpot Integration
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 row groups, columns, schemas, partitions, file batches, and column statistics 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: Columnar Parquet files from lakes and exports.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How Parquet File entities map to ThoughtSpot
| Parquet File entity | ThoughtSpot object | Notes |
|---|---|---|
| row groups | parquet_file_row_groups | id PK · custom fields → flattened columns for searchable fields |
| columns | parquet_file_columns | id PK · linked to parquet_file_row_groups |
| schemas | parquet_file_schemas | id PK · linked to parquet_file_row_groups |
| partitions | parquet_file_partitions | id PK · linked to parquet_file_row_groups |
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.
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Early access
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