SQL Server

is Microsoft's SQL database. Fivetran's integration service replicates data from your SQL Server source database and loads it into your destination at regular intervals.

Row-based relational databases, like SQL Server, are optimized for high-volume, high-frequency transactional applications. While very performant as production databases, they are not optimized for analytical querying. Your analytical queries will be very slow if you build your BI stack directly on top of your transactional SQL Server database, and you run the risk of slowing down your application layer.

Column-based databases are optimized for performing analytical queries on large volumes of data at speeds far exceeding those of SQL Server. While these databases are not good for high-frequency transactional applications, they are highly efficient in data storage. They permit more data compression (10x-100x) than row-based databases, which makes them a cost effective way to store and access data for analytical purposes.

Supported services

Fivetran supports the following SQL Server database services:

Supported configurations

Fivetran supports the following SQL Server configurations:

Supportability Category	Supported Values
Database versions	SQL Server 2012 - 2019
Maximum table count per connector	15,000
Maximum column count per table	1,019
Connector limit per database	No limit
Transport Layer Security (TLS)	TLS 1.1 - 1.3

IMPORTANT: We do not support single-user mode.

Which SQL Server instance types we can connect to depend on whether you use or as your incremental update mechanism. Read our documentation for more information.

Instance Types	Change Tracking	Change Data Capture

Primary instance
Availability group replica

Primary instance
Availability group replica

Primary instance
Availability group replica

Primary instance
Availability group replica

Primary instance
Availability group replica

Known limitations

For connectors using , we don't sync computed columns because CDC doesn't support these columns. Learn more in Microsoft's documentation.

Features

Feature Name	Supported	Notes
		All tables and fields
		All tables and fields
		Column level, table level, and schema level

		Table level
History

Priority-first sync
Fivetran data models
		: Generic SQL Server on EC2, SQL Server RDS : Azure SQL Database, Azure SQL Managed Instance

Setup guide

Follow our step-by-step setup guides for specific instructions on how to set up your SQL Server database type:

Private Preview

Sync overview

Once Fivetran is connected to your database, we pull a full dump of all selected data from your database. We then use one of SQL Server's two built-in tracking mechanisms, and , to pull all your new and changed data at regular intervals. If data in your database changes (for example, you add new tables or change a data type), Fivetran automatically detects and persists these changes into your destination.

Syncing empty tables and columns

Fivetran can sync empty tables for your SQL Server connector.

We can also sync empty columns in most cases. However, if you don't add rows after you create a new column, we cannot sync that new column. We need at least one row to see a new column because we learn of changes to a table's column cardinality when we see a row with a new or removed column during an update.

For more information, see our .

Replication speeds

Two major factors can cause disparities between our estimates and the exact replication speed for your Fivetran-connected databases: network latency and discrepancies in the format of the data we receive versus how the data is stored at rest in the data destination.

The ability to sync changes quickly also depends on the sync frequency you configure. The risk of the sync falling behind, or being unable to keep up with data changes, decreases as the sync frequency increases. We recommend a higher sync frequency for data sources with a high rate of data changes.

To measure the rate of new data in your database, check the disk space usage metrics over time for databases hosted on cloud providers. Use the to find this information for self-hosted databases.

Schema information

Fivetran tries to replicate the exact schema and tables from your database to your destination.

Fivetran-generated columns

Fivetran adds the following columns to every table in your destination:

_fivetran_deleted (BOOLEAN) marks rows that were deleted in the source table
_fivetran_synced (UTC TIMESTAMP) indicates the time when Fivetran last successfully synced the row

We add these columns to give you insight into the state of your data and the progress of your data syncs.

Type transformation and mapping

As we extract your data, we match SQL Server data types to data types that Fivetran supports. If we don't support a certain data type, we automatically change that type to the closest supported type or, for some types, don't load that data at all. Our system automatically skips columns with data types that we don't accept or transform.

The following table illustrates how we transform your SQL Server data types into Fivetran supported types:

SQL Server Type	Fivetran Type	Fivetran Supported	Notes
BIGINT	LONG	True
BINARY	BINARY	True
BIT	BOOLEAN	True	Not supported as primary keys for Teleport
CHAR	STRING	True
DATE	LOCALDATE	True
DATETIME	LOCALDATETIME	True
DATETIME2	LOCALDATETIME	True
DATETIMEOFFSET	TIMESTAMP	True	Not supported as primary keys for Teleport
DECIMAL	BIGDECIMAL	True
FLOAT	DOUBLE	True
GEOMETRY	JSON	True	Not supported as primary keys for Teleport
GEOGRAPHY	JSON	True	Not supported as primary keys for Teleport
HIERARCHYID	STRING	True
IMAGE	BINARY	True
INTEGER	INTEGER	True
MONEY	BIGDECIMAL	True
NCHAR	STRING	True
NTEXT	STRING	True
NUMERIC	BIGDECIMAL	True
NVARCHAR	STRING	True
REAL	FLOAT	True
ROWVERSION	BINARY	True
SMALLDATETIME	LOCALDATETIME	True
SMALLMONEY	BIGDECIMAL	True
SMALLINT	SHORT	True
TEXT	STRING	True
TIME	STRING	True
TIMESTAMP	BINARY	True
TINYINT	SHORT	True
UNIQUEIDENTIFIER	STRING	True
VARCHAR	STRING	True
VARBINARY	BINARY	True
XML	STRING	True

We also support syncing user-defined data types. When you create a user-defined type in SQL Server, you are required to choose a base type. If Fivetran supports that base type, we automatically transform your user-defined type to its corresponding Fivetran type. For more information, see .

If we are missing an important type that you need, please .

In some cases, when loading data into your destination, we may need to convert Fivetran data types into data types that are supported by the destination. This can change either the data type itself or the parameterization on a data type. For more information, see the individual data destination pages under the .

Excluding source data

If you don’t want to sync all the data from your database, you can exclude schemas, tables, or columns from your syncs on your Fivetran dashboard. To do so, go to your connector details page and uncheck the objects you would like to omit from syncing. For more information, see our .

Alternatively, you can change the permissions of the Fivetran user you created and restrict its access to certain tables or columns.

How to allow only a subset of tables

In your primary database, you can grant SELECT permissions to the Fivetran user on all tables in a given schema:

GRANT SELECT on SCHEMA::<schema> to fivetran;
content_copy

or only grant SELECT permissions for a specific table:

GRANT SELECT ON [<schema>].[<table>] TO fivetran;
content_copy

How to allow only a subset of columns

You can restrict the column access of your database's Fivetran user in two ways:

Grant SELECT permissions only on certain columns:

GRANT SELECT ON [<schema>].[<table>] ([<column 1>], [<column 2>], ...) TO fivetran;
content_copy

Deny SELECT permissions only on certain columns:

GRANT SELECT ON [<schema>].[<table>] TO fivetran;
DENY SELECT ON [<schema>].[<table>] ([<column X>], [<column Y>], ...) TO fivetran;
content_copy

Initial Sync

Once Fivetran is connected to your database or , we first copy all rows from every table in every schema for which we have SELECT permission (except for those you have excluded in your Fivetran dashboard) and add . We copy rows by performing a SELECT statement on each table. For large tables, we copy a limited number of rows at a time so that we don't have to start the sync over from the beginning if our connection is lost midway. How many rows we copy at a time depends on whether your tables have clustered indices or not. For tables with clustered indices, we copy 500,000 rows at a time. For tables without clustered indices, we copy 5,000,000 rows at a time.

Connecting to a read replica

You can only connect Fivetran to a read replica if Change-Data Capture is enabled on the primary database. We need this in order to perform our incremental updates.

Updating data

Fivetran performs incremental updates of any new or modified data from your source database. During incremental updates, we request only the data that has changed since our last sync. We use one of SQL Server's two built-in tracking mechanisms for incremental updates: (CT) and (CDC).

Both CT and CDC create change records that Fivetran accesses on a per-table basis during incremental updates. To guarantee data integrity, we check for changes on every table with CT or CDC enabled during each update, which can add to the sync time. Customers who sync with many thousands of tables can therefore expect longer syncs.

Tables that do not have CT or CDC enabled still appear on your Fivetran dashboard, but they are disabled. They also have a message indicating that you need to enable either CT or CDC.

If CT and CDC are both enabled on a table, we use CDC as the incremental update mechanism.

CT vs CDC

There are several key differences between change tracking (CT) and change data capture (CDC):

Feature	Change tracking	Change data capture
Syncs tables with primary keys	Yes	Yes
Syncs tables without primary keys	No	Yes
Can read from a replica	No	Yes
Tracks DML changes	Yes	Yes
Provides full record of changed data	No	Yes
Tracks previous changes	No	Yes

NOTE: CDC has heavier processing and storage overhead than CT.

To learn more about CDC and CT, read on below or see .

Change tracking

Change tracking (CT) records when a row in a table has changed, but does not capture the data that was changed. CT also does not capture how many times the row changed or record any previous changes. SQL Server records changes from all tables that have CT enabled in a single internal change table.

You cannot sync tables without primary keys if you choose CT as your incremental update mechanism. CT needs primary keys to identify rows that have changed.

You must enable CT on the primary database, as well as on each individual table that you want to sync. When you enable CT on your primary database, you can select a window size. Window size determines how long your change records are kept in the change table before they are deleted. If not explicitly specified, the default value is 3 days. We recommend changing the window size to 7 days. A longer window gives us more time to resolve any potential sync issues before change records are deleted. Our system detects when we were unable to process changes to a table before they were deleted from the change table. When we detect this situation, we trigger a re-sync for that table.

Change tracking is a lightweight background process that should not impact your production workload. CT takes up minimal storage space on your hard drive because its change table only records the primary keys of changed rows.

Change data capture

NOTE: Fivetran can only sync tables that have a single CDC capture instance. Our syncs only include tables and columns that are present in a CDC instance. If you add new tables or columns, you must that includes them and delete the old instance.

NOTE: To use a Clustered Columnstore Index (CCI) for CDC, you must enable CDC before adding the CCI.

Change data capture (CDC) tracks every change that is applied to a table and records those changes in a shadow history table. Unlike CT, CDC captures what data was changed and when, so you can see how many times a row has changed and view past changes.

CDC can track changes on any kind of table, with or without primary keys. Due to SQL Server limitations, a SQL Server connector with CDC enabled automatically propagates column type changes to your destination but does not propagate any other schema changes, such as renamed columns, new tables, and new columns.

You must enable CDC on the primary database, as well as on each individual table that you want to sync. When you enable CDC on your primary database, you can select a window size (also known as a retention period). Window size determines how long your change records are kept in the shadow history table before they are deleted. Unless specified, the default window size is 3 days. We recommend increasing the window size to 7 days. A longer window gives us more time to resolve any potential sync issues before change records are deleted.

CDC is a heavier process than CT. CDC takes up more storage space in your database because it captures entire changed records, not just the primary keys of changed rows. CDC also uses more compute resources than CT because it writes each table's changes to its own shadow history table. If you want to reduce some of the load on your production database, you can configure CDC to read from a replica, though you will need to enable it on your primary database first.

Fivetran Teleport Sync

Fivetran Teleport Sync is a proprietary database replication method that offers the completeness of snapshots while approaching the speed of log-based systems. With this sync mechanism, Fivetran can incrementally replicate your database with no additional setup other than a read-only SQL connection.

Use Fivetran Teleport Sync if enabling the tracking mechanisms is not an option or if there are other reasons that would prohibit the Fivetran user from using CT or CDC.

Fivetran Teleport Sync's queries perform the following operations on your database:

Do a full table scan of each synced table
Perform calculations on all values in each synced table's rows
Aggregate a compressed table snapshot in the database's memory

For optimum Fivetran Teleport Sync performance, we recommend that you make the following resources available in your database:

1 GB Free RAM
1 Free CPU Core
IOPS (Teleport Sync times decrease linearly with an increase of available IOPS).

Limitations

Fivetran Teleport Sync cannot sync tables without primary keys. Teleport does not support all column types as primary keys. See which columns we support as primary keys in the .

DROP COLUMN DDL operations prompt a table re-sync. ADD COLUMN operations do not prompt a table re-sync because we are able to process the schema change without it.

Teleport can capture a maximum row size of 8000 bytes. This means that rows larger than 8000 bytes will cause a connector failure. This limit applies to SQL Server 2014 (12.x) and earlier. Later versions do not have this limit.

Tables with a primary key

We merge changes to tables with primary keys into the corresponding tables in your destination:

An INSERT in the source table generates a new row in the destination with _fivetran_deleted = FALSE
A DELETE in the source table updates the corresponding row in the destination with _fivetran_deleted = TRUE
An UPDATE in the source table updates the corresponding row in the destination

Tables without a primary key

NOTE: Fivetran cannot sync tables without a primary key using CT or Teleport. You must have CDC enabled to sync tables without a primary key.

We handle changes to tables without a primary key differently:

An INSERT in the source table generates a new row in the destination with _fivetran_deleted = FALSE.
The _fivetran_id helps us handle DELETE operations:
- If there is a row in the destination that has a corresponding _fivetran_id value, that row will be updated with _fivetran_deleted = TRUE.
- If there is not a row in the destination that has a corresponding _fivetran_id value, a new row will be added with _fivetran_deleted = TRUE.
An UPDATE in the source table is treated as a DELETE followed by an INSERT, so it results in two rows in the destination:
- A row containing the old values with _fivetran_deleted = TRUE
- A row containing the new values with _fivetran_deleted = FALSE

As a result, one record in your source database may have several corresponding rows in your destination. For example, suppose you have a products table in your source database with no primary key:

description	quantity
Shrink-ray gun	1
Boogie robot	2
Cookie robot	3

You load this table into your destination during your initial sync, creating this destination table:

description	quantity	_fivetran_synced	_fivetran_index	_fivetran_deleted	_fivetran_id
Shrink-ray gun	1	'2000-01-01 00:00:00'	0	FALSE	asdf
Cookie robot	2	'2000-01-01 00:00:00'	1	FALSE	dfdf
Boogie robot	3	'2000-01-01 00:00:00'	2	FALSE	ewra

You then update a row:

UPDATE products SET quantity = 4 WHERE description = 'Cookie robot';
content_copy

After your UPDATE operation, your destination table will look like this:

description	quantity	_fivetran_synced	_fivetran_index	_fivetran_deleted	_fivetran_id
Shrink-ray gun	1	'2000-01-01 00:00:00'	0	FALSE	asdf
Cookie robot	2	'2000-01-01 00:00:00'	3	TRUE	dfdf
Boogie robot	3	'2000-01-01 00:00:00'	2	FALSE	ewra
Cookie robot	4	'2000-01-01 00:00:00'	4	FALSE	zxfd

You then delete a row:

DELETE FROM products WHERE description = 'Boogie robot';
content_copy

After your DELETE operation, your destination table will look like this:

description	quantity	_fivetran_synced	_fivetran_index	_fivetran_deleted	_fivetran_id
Shrink-ray gun	1	'2000-01-01 00:00:00'	0	FALSE	asdf
Cookie robot	2	'2000-01-01 00:00:02'	3	TRUE	dfdf
Cookie robot	4	'2000-01-01 00:00:02'	4	FALSE	zxfd
Boogie robot	3	'2000-01-01 00:00:02'	5	TRUE	ewra

So, while there may be just one record in your source database where description = Cookie robot, there are two in your destination - an old version where _fivetran_deleted = TRUE, and a new version where _fivetran_deleted = FALSE.

We also de-duplicate rows before we load them into your destination. We use the _fivetran_id field, which is the hash of the non-Fivetran values in every row, to avoid creating multiple rows with identical contents. If, for example, you have the following table in your source:

description	quantity
Shrink-ray gun	1
Shrink-ray gun	1
Shrink-ray gun	1

Then your destination table will look like this:

description	quantity	_fivetran_synced	_fivetran_index	_fivetran_deleted	_fivetran_id
Shrink-ray gun	1	'2000-01-01 00:00:00'	0	FALSE	asdf

Deleted rows

We don't delete rows from the destination, though the way for how we process deletes differs for and .

Change tracking

You cannot sync tables without a primary key because CT requires primary keys to record changes. Tables without primary keys are excluded from your syncs. They are still listed in your Fivetran dashboard, but appear disabled.

If you want to add a primary key to a table, you can run the following query in your primary database:

ALTER TABLE <schema>.<pkless_table> ADD _id int NOT NULL IDENTITY (1,1) PRIMARY KEY
content_copy

The new _id primary key column...

Auto-generates a sequential _id value when a new record is added
Cannot be changed or overwritten with new values
Automatically populates on all records when added to an existing table

Change data capture

We merge changes to tables without primary keys into the corresponding tables in your destination:

An INSERT in the source table generates a new row in the destination with _fivetran_deleted = FALSE
A DELETE in the source table updates the corresponding row in the destination with _fivetran_deleted = TRUE
An UPDATE in the source table soft deletes the existing row in the destination by setting _fivetran_deleted = TRUE, then inserts a new row with the updated values

Migrating Service Providers

If you want to migrate service providers, we will need to do a full re-sync of your data because the new service provider won't retain the same change tracking data as your original SQL Server database.