DatriseAI-first ETL

Apache Spark Spreadsheets

AI-first ETL from Apache Spark into Spreadsheets. Governed entities, incremental sync, typed landing tables.

How Datrise loads Apache Spark into Spreadsheets

Datrise syncs Apache Spark's records, events, and configuration objects into Spreadsheets as a tab per source entity. Flexible or custom fields land in JSON-stringified cells for nested fields, and timestamps such as created, updated, and status changes are typed as ISO-8601 text or serial date cells.

Sync is incremental: Datrise uses refreshes the tab's rows each run, preserving header order, so re-runs update only what changed. Sheets caps out around the low millions of cells, so Datrise lands a curated column set rather than every raw field.

Ideal for lightweight, shareable reporting for non-technical teams.

Endpoints

Apache Spark: SaaS or API data source for analytics and warehouse sync.

Spreadsheets: Business-friendly spreadsheet destination for collaborative analysis.

How Apache Spark entities map to Spreadsheets

Apache Spark entitySpreadsheets objectNotes
recordsapache_spark_recordsid PK · custom fields → JSON-stringified cells for nested fields
eventsapache_spark_eventsISO-8601 text or serial date cells events
configuration objectsapache_spark_configuration_objectsid PK · linked to apache_spark_records

FAQ

How does Datrise handle Apache Spark's custom fields in Spreadsheets?

Flexible values are stored as JSON-stringified cells for nested fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spreadsheets types.

How does the Apache Spark to Spreadsheets sync stay up to date?

It runs incrementally — Datrise uses refreshes the tab's rows each run, preserving header order.

Related pipelines

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