Sparkpost → DuckDB
AI-first ETL from Sparkpost into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Sparkpost into DuckDB
Datrise syncs Sparkpost's records, events, and configuration objects into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.
Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.
Ideal for local and notebook analytics without standing up a server.
Endpoints
Sparkpost: SaaS or API data source for analytics and warehouse sync.
DuckDB: In-process analytics database for fast local OLAP.
How Sparkpost entities map to DuckDB
| Sparkpost entity | DuckDB object | Notes |
|---|---|---|
| records | sparkpost_records | id PK · custom fields → JSON or STRUCT columns |
| events | sparkpost_events | TIMESTAMP WITH TIME ZONE events |
| configuration objects | sparkpost_configuration_objects | id PK · linked to sparkpost_records |
FAQ
How does Datrise handle Sparkpost's custom fields in DuckDB?
Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.
How does the Sparkpost to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
Related pipelines
More destinations for Sparkpost
Early access
Connect Sparkpost to DuckDB the easy way
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