DatriseAI-first ETL

Microsoft SQL Server to DuckDB Integration

AI-first ETL from Microsoft SQL Server into DuckDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads Microsoft SQL Server into DuckDB

Datrise syncs Microsoft SQL Server's tables, rows, schemas, indexes, primary keys, and change tracking 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

Microsoft SQL Server: Microsoft SQL Server transactional databases.

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

How Microsoft SQL Server entities map to DuckDB

Microsoft SQL Server entityDuckDB objectNotes
tablesmssql_sql_server_tablesid PK · custom fields → JSON or STRUCT columns
rowsmssql_sql_server_rowsid PK · linked to mssql_sql_server_tables
schemasmssql_sql_server_schemasid PK · linked to mssql_sql_server_tables
indexesmssql_sql_server_indexesid PK · linked to mssql_sql_server_tables

FAQ

How does Datrise handle Microsoft SQL Server'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 Microsoft SQL Server 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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