DatriseAI-first ETL

Azure Table Storage to DuckDB Integration

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 tables, entities, partition keys, row keys, properties, and timestamps 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: Key-value NoSQL store on Azure.

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

How Azure Table Storage entities map to DuckDB

Azure Table Storage entityDuckDB objectNotes
tablesazure_table_storage_tablesid PK · custom fields → JSON or STRUCT columns
entitiesazure_table_storage_entitiesid PK · linked to azure_table_storage_tables
partition keysazure_table_storage_partition_keysid PK · linked to azure_table_storage_tables
row keysazure_table_storage_row_keysid PK · linked to azure_table_storage_tables

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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