Parquet File to Looker Studio Integration
AI-first ETL from Parquet File into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Parquet File into Looker Studio
Datrise syncs Parquet File's row groups, columns, schemas, partitions, file batches, and column statistics into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
Parquet File: Columnar Parquet files from lakes and exports.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Parquet File entities map to Looker Studio
| Parquet File entity | Looker Studio object | Notes |
|---|---|---|
| row groups | parquet_file_row_groups | id PK · custom fields → flattened columns for chart 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 Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the Parquet File to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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Early access
Connect Parquet File to Looker Studio the easy way
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