DatriseAI-first ETL

Azure Table Storage DuckDB

AI-first ETL from Azure Table Storage into DuckDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads Azure Table Storage into DuckDB

Datrise syncs Azure Table Storage'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

Azure Table Storage: SaaS or API data source for analytics and warehouse sync.

DuckDB: In-process analytics database for fast local OLAP.

How Azure Table Storage entities map to DuckDB

Azure Table Storage entityDuckDB objectNotes
recordsazure_table_storage_recordsid PK · custom fields → JSON or STRUCT columns
eventsazure_table_storage_eventsTIMESTAMP WITH TIME ZONE events
configuration objectsazure_table_storage_configuration_objectsid PK · linked to azure_table_storage_records

FAQ

How does Datrise handle Azure Table Storage'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 Azure Table Storage 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

Early access

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