DatriseAI-first ETL

Plaid Amazon Athena

AI-first ETL from Plaid into Amazon Athena. Governed entities, incremental sync, typed landing tables.

How Datrise loads Plaid into Amazon Athena

Datrise syncs Plaid's records, events, and configuration objects into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

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

Amazon Athena: Serverless SQL over S3 data lake tables.

How Plaid entities map to Amazon Athena

Plaid entityAmazon Athena objectNotes
recordsplaid_recordsid PK · custom fields → struct/map columns in Parquet
eventsplaid_eventstimestamp events
configuration objectsplaid_configuration_objectsid PK · linked to plaid_records

FAQ

How does Datrise handle Plaid's custom fields in Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the Plaid to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

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