DatriseAI-first ETL

Recurly Amazon Redshift

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

How Datrise loads Recurly into Amazon Redshift

Datrise syncs Recurly's subscriptions, invoices, plans, transactions, and dunning events 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

Recurly: Subscription management and recurring billing platform.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Recurly entities map to Amazon Redshift

Recurly entityAmazon Redshift objectNotes
subscriptionsrecurly_subscriptionsid PK · custom fields → SUPER columns
invoicesrecurly_invoicesid PK · linked to recurly_subscriptions
plansrecurly_plansid PK · linked to recurly_subscriptions
transactionsrecurly_transactionsid PK · linked to recurly_subscriptions

FAQ

How does Datrise handle Recurly'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 Recurly 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

Early access

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