DatriseAI-first ETL

Harvest Amazon DynamoDB

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

How Datrise loads Harvest into Amazon DynamoDB

Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization 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

Harvest: Time tracking and project profitability for services teams.

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

How Harvest entities map to Amazon DynamoDB

Harvest entityAmazon DynamoDB objectNotes
time entriesharvest_time_entriesid PK · custom fields → nested map/list attributes
projectsharvest_projectsid PK · linked to harvest_time_entries
clientsharvest_clientsid PK · linked to harvest_time_entries
invoicesharvest_invoicesid PK · linked to harvest_time_entries

FAQ

How does Datrise handle Harvest'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 Harvest 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

Early access

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