DatriseAI-first ETL

Holded to Amazon Redshift Integration

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

How Datrise loads Holded into Amazon Redshift

Datrise syncs Holded's contacts, leads, invoices, estimates, products, and projects into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Holded: Spanish business suite joining CRM, invoicing, and accounting.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Holded entities map to Amazon Redshift

Holded entityAmazon Redshift objectNotes
contactsholded_contactsid PK · custom fields → SUPER columns
leadsholded_leadsid PK · linked to holded_contacts
invoicesholded_invoicesid PK · linked to holded_contacts
estimatesholded_estimatesid PK · linked to holded_contacts

FAQ

How does Datrise handle Holded's custom fields in Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the Holded to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

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