AppsFlyer → DuckDB
AI-first ETL from AppsFlyer into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads AppsFlyer into DuckDB
Datrise syncs AppsFlyer's installs, in-app events, campaigns, and attribution touchpoints 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
AppsFlyer: Mobile attribution and marketing analytics platform.
DuckDB: In-process analytics database for fast local OLAP.
How AppsFlyer entities map to DuckDB
| AppsFlyer entity | DuckDB object | Notes |
|---|---|---|
| installs | appsflyer_installs | id PK · custom fields → JSON or STRUCT columns |
| in-app events | appsflyer_in_app_events | TIMESTAMP WITH TIME ZONE events |
| campaigns | appsflyer_campaigns | id PK · linked to appsflyer_installs |
| attribution touchpoints | appsflyer_attribution_touchpoints | id PK · linked to appsflyer_installs |
FAQ
How does Datrise handle AppsFlyer'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 AppsFlyer to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
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Early access
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