DatriseAI-first ETL

Teradata D Amazon DynamoDB

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

How Datrise loads Teradata D into Amazon DynamoDB

Datrise syncs Teradata D'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

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

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

How Teradata D entities map to Amazon DynamoDB

Teradata D entityAmazon DynamoDB objectNotes
recordsteradata_d_recordsid PK · custom fields → nested map/list attributes
eventsteradata_d_eventsISO-8601 string or epoch number attributes events
configuration objectsteradata_d_configuration_objectsid PK · linked to teradata_d_records

FAQ

How does Datrise handle Teradata D'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 Teradata D 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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