DatriseAI-first ETL

Parquet File Amazon DynamoDB

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

How Datrise loads Parquet File into Amazon DynamoDB

Datrise syncs Parquet File's records, events, and configuration objects into Amazon DynamoDB as an item per source record in a table per entity. Flexible or custom fields land in nested map/list attributes, and timestamps such as created, updated, and status changes are typed as ISO-8601 string or epoch number attributes.

Sync is incremental: Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id, so re-runs update only what changed. Partition-key design on the entity id to spread throughput evenly. DynamoDB rewards access-pattern-first key design, so Datrise sets partition/sort keys from your entity ids rather than scan-heavy defaults.

Ideal for serverless apps needing single-digit-millisecond key lookups on CRM data.

Endpoints

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

Amazon DynamoDB: Serverless key-value and document store on AWS.

How Parquet File entities map to Amazon DynamoDB

Parquet File entityAmazon DynamoDB objectNotes
recordsparquet_file_recordsid PK · custom fields → nested map/list attributes
eventsparquet_file_eventsISO-8601 string or epoch number attributes events
configuration objectsparquet_file_configuration_objectsid PK · linked to parquet_file_records

FAQ

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

Flexible values are stored as nested map/list attributes, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon DynamoDB types.

How does the Parquet File to Amazon DynamoDB sync stay up to date?

It runs incrementally — Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id.

Related pipelines

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