Source connector

SQL Server connector

SQL Server integration for databases, warehouses and BI tools

Microsoft SQL Server transactional databases. Datrise captures Microsoft SQL Server entities such as tables, rows, schemas, indexes, primary keys and change tracking, then lands normalized and typed models for warehouse reporting, attribution, and AI workflows.

The SQL Server Change Tracking / Change Data Capture (T-SQL) authenticates with username / password, OAuth 2.0, incremental runs key on SYS_CHANGE_VERSION (Change Tracking) or LSN range (Change Data Capture).

Of the 6 objects, 5 sync as upserts keyed on the primary key and 1 as append-only streams, so each incremental run moves only what changed since the last watermark. Every object lands in its own table (for example `mssql_sql_server_tables`) with a schema derived from the SQL Server API rather than a flattened CSV, in any of the 43 supported destinations.

Datrise is in early access: pick a destination below to open the SQL Server pipeline page, and join the waitlist to get the SQL Server connector with guided onboarding.

Incremental syncSchema-aware mapping43 destinations available

What Microsoft SQL Server data Datrise extracts

Each object lands in its own typed table. Sync mode is derived from the object: history-style objects append, everything else upserts.

Object	Landing table	Sync mode
tables	mssql_sql_server_tables	Upsert by primary key, incremental on updated-at
rows	mssql_sql_server_rows	Upsert by primary key, incremental on updated-at
schemas	mssql_sql_server_schemas	Upsert by primary key, incremental on updated-at
indexes	mssql_sql_server_indexes	Upsert by primary key, incremental on updated-at
primary keys	mssql_sql_server_primary_keys	Upsert by primary key, incremental on updated-at
change tracking	mssql_sql_server_change_tracking	Append-only, incremental by timestamp

Microsoft SQL Server API: setup facts for the connector

Verified against the vendor documentation on 2026-09-02. Unknown values are left blank rather than guessed.

API	SQL Server Change Tracking / Change Data Capture (T-SQL)
Authentication	username / password, OAuth 2.0 — SQL Server Authentication (login/password) or Windows Authentication (Kerberos/NTLM, no password on the wire); Microsoft Entra ID token auth is available for cloud-connected scenarios.
Pagination	keyset — Change Tracking reads via `CHANGETABLE(CHANGES table, @last_version)` filtered by `SYS_CHANGE_VERSION`; CDC reads via `cdc.fn_cdc_get_all_changes_<capture_instance>` filtered by an LSN range from `sys.fn_cdc_get_min_lsn`/`sys.fn_cdc_get_max_lsn`.
Rate limit	Not published
Incremental cursor	SYS_CHANGE_VERSION (Change Tracking) or LSN range (Change Data Capture) — Change Tracking reports only that a row changed plus its latest values; CDC captures full before/after row images from the transaction log.
Webhooks	No
Bulk export	Yes — bcp utility / BULK INSERT
Deletes detectable	Yes — Change Tracking: `SYS_CHANGE_OPERATION = 'D'`; CDC: `__$operation = 1` in the change table

Official API reference

What bites when replicating Microsoft SQL Server

Change Tracking retention defaults to `CHANGE_RETENTION = 2 DAYS`; a connector down longer than that permanently loses its resume point (`CHANGE_TRACKING_MIN_VALID_VERSION`) and must fall back to a full snapshot.
CDC's default cleanup job retains only 4320 minutes (3 days) of change data and deletes in batches of up to 5000 rows per cycle, while the capture job itself processes roughly 1000 transactions per 5-second scan, so a busy source can lag behind real time.
A single source table supports at most 2 concurrent CDC capture instances, which limits in-place schema-change migration strategies.
Query Notifications fire exactly 1 time per subscription (built on Service Broker), requiring the client to re-execute the query to resubscribe — it is not a continuous push feed comparable to an HTTP webhook.

Compare this against every other documented API

Popular destinations for Microsoft SQL Server

Choose a destination below to open the ready pipeline page.

A
Airtable
Relational spreadsheet destination for ops and go-to-market teams.
View pipeline
A
Amazon Athena
Serverless SQL over S3 data lake tables.
View pipeline
A
Amazon DynamoDB
Serverless key-value and document store on AWS.
View pipeline
A
Amazon QuickSight
AWS serverless BI with SPICE and embedded analytics.
View pipeline
A
Amazon Redshift
AWS petabyte-scale warehouse with Spectrum.
View pipeline
A
Amazon S3 Data Lake
Object storage landing zone for parquet and snapshots.
View pipeline
A
Apache Superset
Open-source BI for SQL exploration, charts, and dashboard publishing.
View pipeline
A
Azure Data Lake Storage
ADLS Gen2 object storage for analytics workloads.
View pipeline
A
Azure Synapse
Microsoft analytics workspace with SQL pools.
View pipeline

Microsoft SQL Server connector FAQ

What SQL Server data does the connector extract?: 6 objects: tables, rows, schemas, indexes, primary keys and change tracking. Each lands in its own table with types preserved; history-style objects (1: change tracking) load as append-only streams.
Is the SQL Server integration incremental?: Yes. After the initial full load, every run reads only records changed since the last watermark (updated-at or event timestamp) and applies an upsert or an append depending on the object.
Where can I load SQL Server data?: Into 43 destinations: relational databases, cloud warehouses, data lakes, spreadsheets and BI tools. Each SQL Server → destination pair has its own page with the object-to-table mapping.

Related sources

Other sources in the same category, each with its own connector page.

Early access

Launch your Microsoft SQL Server pipeline with guided onboarding

Join the waitlist to get priority access for Microsoft SQL Server integrations with guided setup, schema-aware mapping, and production-grade incremental sync.