DatriseAI-first ETL

Snowflake to Amazon DynamoDB Integration

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

How Datrise loads Snowflake into Amazon DynamoDB

Datrise syncs Snowflake's databases, schemas, tables, views, query history, and warehouses 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

Snowflake: Cloud data platform with separated storage and compute.

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

How Snowflake entities map to Amazon DynamoDB

Snowflake entityAmazon DynamoDB objectNotes
databasessnowflake_databasesid PK · custom fields → nested map/list attributes
schemassnowflake_schemasid PK · linked to snowflake_databases
tablessnowflake_tablesid PK · linked to snowflake_databases
viewssnowflake_viewsid PK · linked to snowflake_databases

FAQ

How does Datrise handle Snowflake'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 Snowflake 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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