Urban Airship to Amazon S3 Data Lake Integration
AI-first ETL from Urban Airship into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Urban Airship into Amazon S3 Data Lake
Datrise syncs Urban Airship's push notifications, channels, audiences, message events, segments, and named users into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.
Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.
Endpoints
Urban Airship: Mobile push and cross-channel message orchestration.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Urban Airship entities map to Amazon S3 Data Lake
| Urban Airship entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| push notifications | urban_airship_push_notifications | id PK · custom fields → nested struct/map fields in Parquet |
| channels | urban_airship_channels | id PK · linked to urban_airship_push_notifications |
| audiences | urban_airship_audiences | id PK · linked to urban_airship_push_notifications |
| message events | urban_airship_message_events | ISO-8601 timestamp columns events |
FAQ
How does Datrise handle Urban Airship's custom fields in Amazon S3 Data Lake?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.
How does the Urban Airship to Amazon S3 Data Lake sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.
Related pipelines
More destinations for Urban Airship
- Urban Airship → Azure Data Lake Storage
- Urban Airship → Azure Synapse
- Urban Airship → Spreadsheets
- Urban Airship → Airtable
- Urban Airship → CSV Files
- Urban Airship → MongoDB
- Urban Airship → Supabase
- Urban Airship → Neon
- Urban Airship → PlanetScale
- Urban Airship → Amazon DynamoDB
- Urban Airship → Looker
- Urban Airship → Looker Studio
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Early access
Connect Urban Airship to Amazon S3 Data Lake the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.