DatriseAI-first ETL

Timely to Amazon DynamoDB Integration

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

How Datrise loads Timely into Amazon DynamoDB

Datrise syncs Timely's time entries, projects, clients, users, tags, and billable hours 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

Timely: Automatic time tracking for teams and projects.

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

How Timely entities map to Amazon DynamoDB

Timely entityAmazon DynamoDB objectNotes
time entriestimely_time_entriesid PK · custom fields → nested map/list attributes
projectstimely_projectsid PK · linked to timely_time_entries
clientstimely_clientsid PK · linked to timely_time_entries
userstimely_usersid PK · linked to timely_time_entries

FAQ

How does Datrise handle Timely'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 Timely 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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