DatriseAI-first ETL

Mailshake to CSV Files Integration

AI-first ETL from Mailshake into CSV Files. Governed entities, incremental sync, typed landing tables.

How Datrise loads Mailshake into CSV Files

Datrise syncs Mailshake's campaigns, recipients, sequences, email events, replies, and leads into CSV Files as one CSV per source entity. Flexible or custom fields land in JSON-encoded strings for nested fields, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes a fresh, fully-typed CSV per entity each run, so re-runs update only what changed. Optional date-suffixed files for change tracking. CSV has no types, so Datrise emits a companion schema and quotes/escapes consistently so downstream loaders don't misparse commas and newlines.

Ideal for portable hand-off into any tool that ingests delimited files.

Endpoints

Mailshake: Cold outreach sequences and reply tracking.

CSV Files: Flat-file destination for exports and lightweight data sharing.

How Mailshake entities map to CSV Files

Mailshake entityCSV Files objectNotes
campaignsmailshake_campaignsid PK · custom fields → JSON-encoded strings for nested fields
recipientsmailshake_recipientsid PK · linked to mailshake_campaigns
sequencesmailshake_sequencesid PK · linked to mailshake_campaigns
email eventsmailshake_email_eventsISO-8601 timestamp columns events

FAQ

How does Datrise handle Mailshake's custom fields in CSV Files?

Flexible values are stored as JSON-encoded strings for nested fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native CSV Files types.

How does the Mailshake to CSV Files sync stay up to date?

It runs incrementally — Datrise uses writes a fresh, fully-typed CSV per entity each run.

Related pipelines

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