DatriseAI-first ETL

Mautic Amazon DynamoDB

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

How Datrise loads Mautic into Amazon DynamoDB

Datrise syncs Mautic's contacts, accounts, deals, activities, and lifecycle events 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

Mautic: Open-source CRM for customizable sales and customer workflows.

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

How Mautic entities map to Amazon DynamoDB

Mautic entityAmazon DynamoDB objectNotes
contactsmautic_contactsid PK · custom fields → nested map/list attributes
accountsmautic_accountsid PK · linked to mautic_contacts
dealsmautic_dealsid PK · linked to mautic_contacts
activitiesmautic_activitiesISO-8601 string or epoch number attributes events

FAQ

How does Datrise handle Mautic'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 Mautic 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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