How to export incremental data from Azure SQL Database to Azure using Change Data Capture (CDC)

By:   |   Updated: 2022-06-02   |   Comments   |   Related: > Change Data Capture


SQL Server has a built-in feature called Change Data Capture (CDC) which can capture incremental changes being made to your SQL database tables. Here's how to use it with Azure SQL Database and Azure Data Factory.


This tutorial creates an Azure Data Factory pipeline for exporting Azure SQL Database Change Data Capture (CDC) information to Azure Blob Storage.

The high-level steps for implementing the solution are:

  • Create an Azure SQL Database table
  • Deploy an Azure Data Factory
  • Create linked services for Azure database and Azure Blob Storage
  • Create source and sink datasets
  • Create a pipeline to check the modified source data
  • Execute and monitor the incremental run of the Azure Data Factory pipeline.


  • Azure SQL Database: This article uses the Azure SQL Database as a source data store. You require an existing database before proceeding with the article. If you do not have it, refer to Azure tips.
  • Azure Blob Storage: Create an Azure storage account and blob storage for the destination datastore.
  • Azure Data Factory: Deploy an Azure Data Factory cloud-managed service for defining pipelines and scheduling. Refer to these articles SQL Server Azure Data Factory Tips for deploying the service.

I have already deployed the prerequisites for this article as shown below:

  • Azure Data Factory: azdtdemo
  • Azure SQL Database: myazuresql1
  • Azure storage account: mydemoazstorage
azure resources

The storage account [mydemoazstorage] has a blob container [myazuresqldata] to store the exported CSV file with incremental data.

azure containers

Create a data source table in Azure SQL Database

This section creates a sample [Employees] table and enables the change data capture (CDC) features. The following script does the following tasks.

  • Creates a [Employees] data table
  • Enable CDC for Azure database using SP sys.sp_cdc_enable_db
  • Enable CDC for the Employees table
  • Inserts two records into the [Employees] table
Create table Employees 
Emp_id int, 
First_name varchar(50), 
Last_name varchar(50), 
Email varchar(100), 
[Location] varchar(50), CONSTRAINT "PK_Employees" PRIMARY KEY CLUSTERED ("Emp_id") 
EXEC sys.sp_cdc_enable_db 
EXEC sys.sp_cdc_enable_table
@source_schema = 'dbo',
@source_name = 'Employees', 
@role_name = 'null',
@supports_net_changes = 1
insert into Employees 
     (Emp_id, first_name, last_name, email, [Location]) 
     (1, 'Rajendra', 'Gupta', '[email protected]', 'Bracknell'),
     (2, 'Kusum', 'Agarwal', '[email protected]', 'London'); 

If you are not familiar with Change Data Capture for Azure DB, you can refer to an article Change Data Capture (CDC) for Azure SQL Database.

Create Linked Services

This section creates linked services for the source (Azure DB) and destination (Azure Blob Storage) in the Azure Data Factory (ADF).

To create the linked services, launch ADF in the Azure portal and click on Linked Services as highlighted below.

azure linked services

First, we will create a linked service for the destination Azure container. Click on +New and Choose Azure Blob Storage from the list of datastore.

azure new linked service

It opens the new linked service (Azure Blob Storage) configuration page. Enter the following values.

  • Enter AzureBobStore for Name
  • Connect via Integration Runtime: AutoResolveIntegrationRunTime
  • Select Azure subscription and storage account from the drop-down list

Once you enter the required information, click on Test Connection to validate the connection to Azure blob storage linked service is working fine.

azure new linked service

Click on Create, and it creates the linked service as shown below.

azure linked services

Create Azure SQL Database Linked Service

We need another linked service for Azure Database. In the new linked service page, select Azure SQL Database and select the Azure server, database, authentication method as SQL authentication and specify credentials to connect the database. Click Test connection to validate the connection details.

azure new linked service
azure new linked service

Create Datasets in Azure Data Factory

This step creates a dataset to represent the data source and data destination. In ADF, click on DataSet and New Dataset.

adf create dataset

Select Azure SQL Database from the datastore and click Continue.

adf create dataset

In the Set properties, select the dataset name and connection information below.

  • LinkedService: Select the linked service deployed before for the Azure database.
  • Table Name: We enabled CDC for the [Employees] table. The CDC process automatically creates a new CDC table [dbo].[dbo_Employees_CT].
adf create dataset

Click Ok, and it creates the dataset [AzureSqlTable1] as shown below.

adf create dataset

Create a Dataset for Azure Blob Storage Destination Data

Like the source Azure database dataset, we need to create a dataset representing data copied to the sink data store. Select Azure Blob Storage and click Continue.

adf new dataset

Select the data format as delimited text (CSV) and click continue.

adf dataset select format

Now, we need to set properties for the CSV file. Enter the name, select linked service and enter the container name (in my demo- myazuresqldata) in the file path as shown below.

adf create dataset
adf create dataset

Create Pipeline to Copy Changed (incremental) Data from Azure SQL Database to Azure Blob Storage

This step creates a pipeline in Azure Data Factory (ADF). The pipeline uses the lookup activity to check the changed records in the source table.

We create a new pipeline in the Data Factory UI and rename it to [IncrementalDataCopyPipeline].

adf pipeline

Drag the Lookup activity from the activities toolbox to the pipeline designer surface and rename it to GetRecordsChangedCount. The lookup activity retrieves the number of records in the CDC table for a time window.

adf lookup

As shown below, the lookup activity is renamed to GetRecordsChangeCount.

adf lookup

Click on the settings and enter the following SQL query that uses CDC table-valued functions sys.fn_cdc_get_min_lsn and sys.fn_cdc_map_time_to_lsn.

DECLARE  @from_lsn binary(10), @to_lsn binary(10);  
SET @from_lsn =sys.fn_cdc_get_min_lsn('dbo_Employees');  
SET @to_lsn = sys.fn_cdc_map_time_to_lsn('largest less than or equal',  GETDATE());
SELECT count(1) changecount FROM cdc.fn_cdc_get_all_changes_dbo_Employees(@from_lsn, @to_lsn, 'all')
adf get changed data

After enabling the CDC, we inserted two records in the [Employees] table. Therefore, it returns the 2 records while previewing the data. You can click on the Preview data button to ensure the lookup activity obtains a valid output.

adf preview data

Now, drag and drop the IF CONDITION from the Iteration & Conditions in the activities toolbox.

adf if condition

Rename the IF Condition activity to HasChangedRows.

adf if condition

In the expression box of the IF CONDITION enter the following code.

adf if condition

Edit the true condition and drag a copy activity in the pipeline designer surface.

adf copy data

Rename the copy data activity as IncrementalDataCopyActivity.

adf copy data

Click on the source and copy-paste the following SQL query.

DECLARE @from_lsn binary(10), @to_lsn binary(10); 
SET @from_lsn =sys.fn_cdc_get_min_lsn('dbo_Employees'); 
SET @to_lsn = sys.fn_cdc_map_time_to_lsn('largest less than or equal', GETDATE());
SELECT * FROM cdc.fn_cdc_get_all_changes_dbo_Employees(@from_lsn, @to_lsn, 'all')
adf copy data

Click on the Sink tab and choose the Azure Storage dataset for the sink dataset.

adf copy data

Drag the green button from the Lookup activity to the If Condition and connect them like below.

adf work flow

Validate, Publish, and Execute

Click on the Validate icon to verify that the pipeline data flow is configured successfully. As shown below, our pipeline in ADF is validated. It did not report any errors.

adf pipeline validated

Look at the blue button at the top, and it states we need to publish all changes in the Azure Data Factory.

adf work flow

Click on Publish all, and it deploys the package for deployment. You get a message – Publishing completed in the activity bar.

adf work flow

To execute the ADF workflow, we can trigger it immediately or create a new schedule to execute it on a specific schedule.

adf activities

Click on Add Trigger and Trigger Now.

adf triggers

The Output tab states that all task is successfully executed. It shows the execution time of each item as well.

adf output tab

Now, switch to Azure blob container, and it has a CSV file – data.csv with the Azure SQL Database table data.

azure blob container files

Click on the CSV file and download it to view the data.

adf download csv data properties

As shown below, the CSV container has two records that we inserted after enabling the CDC for the [Employees] table.

exported data

Let's insert two more records in the [Employees] table in Azure SQL Database and trigger the pipeline in ADF.

insert into Employees 
     (Emp_id, first_name, last_name, email, [Location]) 
     (3, 'Arjun', 'k', '[email protected]', 'Jaipur'),
     (4, 'Mohan', 'r', '[email protected]', 'Delhi');

Refresh the Azure container and view the contents in the data.csv. You got the incremental data added in the CSV file.

exported data

Similarly, you can execute the update and delete statements and view the CSV data after triggering ADF.

UPDATE Employees SET first_name='test' WHERE emp_id=3 
DELETE Employees WHERE emp_id=4 

The highlighted column helps to understand the DML performed on the CDC table.

  • __$operation = 2 : Inserted rows
  • __$operation = 4 : Updated row after update
  • __$operation = 4 : Row before the update
  • __$operation = 1 : Deleted Row
exported data

In the downloaded CSV file, we do not have the column headers. Edit the CSV configuration and check on column – First Row as a header to add a column header.

adf download csv data properties

Publish the configuration changes in Azure Data Factory and trigger the execution. You get the column headers in the CSV file downloaded from the Azure Data Storage.

exported data

Wrapping up

This article explored exporting the incremental data from Azure SQL Database to Azure using Change Data Capture (CDC). You can customize the pipeline and data flow as per your requirements.

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About the author
MSSQLTips author Rajendra Gupta Rajendra Gupta is a Consultant DBA with 14+ years of extensive experience in database administration including large critical OLAP, OLTP, Reporting and SharePoint databases.

This author pledges the content of this article is based on professional experience and not AI generated.

View all my tips

Article Last Updated: 2022-06-02

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