Simplify data management with mirroring and virtualization

  • 8 minutes

Organizations often face challenges in managing and analyzing their data due to the complexity of integrating various data sources, ensuring data consistency, and maintaining real-time data availability. SQL Database in Microsoft Fabric addresses these challenges by providing a unified platform that simplifies data integration, enhances data consistency, and ensures near real-time data availability.

Integrate with mirroring

One of the key features of SQL Database in Microsoft Fabric is its ability to mirror databases from Azure SQL Database directly into Fabric's OneLake. Additionally, SQL Database in Fabric is automatically mirrored for analytics purposes, with data continuously replicated into OneLake in near real-time.

Diagram of Fabric Database Mirroring for Azure SQL Database.

This mirroring process ensures that data is continuously replicated in near real-time, eliminating the need for complex Extract, Transform, Load (ETL) processes. By doing so, it reduces the total cost of ownership and accelerates the time-to-insight, allowing businesses to unlock business intelligence, artificial intelligence, data engineering, data science, and data sharing scenarios.

After you initiate a mirroring process, you can monitor the replication status by selecting the Monitor replication option from the Replication tab. If there are no updates in the source tables, the engine will back off and resume regular polling after detecting updated data.

Screenshot of how to monitor a mirroring process for SQL database in Fabric.

To learn more about how to configure mirrored databases, see Tutorial: Configure Microsoft Fabric mirrored databases from Azure SQL Database.

Explore data virtualization

Data virtualization in SQL Database in Fabric is a capability that allows you to access and manipulate data from various sources without the need to physically move or copy the data. This approach provides a unified view of data, enabling seamless integration and analysis across different platforms.

These features enable scenarios such as querying Parquet, CSV, and Delta tables available in a lakehouse.

Capability Definition Query Example
Database scoped credential Allows you to create credentials that can be used to access external data sources securely. CREATE DATABASE SCOPED CREDENTIAL MyCredential WITH IDENTITY = 'USER IDENTITY';
External data source This enables you to define external data sources, such as files stored in OneLake. 'abfss://aaaaaaaa-0000-1111-2222-bbbbbbbbbbbb@<onelake_account_name>.dfs.fabric.microsoft.com/bbbbbbbb-1111-2222-3333-cccccccccccc/Files/parquet/data1.parquet';
External file format This capability lets you specify the format of external files, such as Parquet, CSV, and Delta files. CREATE EXTERNAL FILE FORMAT MyFileFormat WITH ( FORMAT_TYPE = DELIMITEDTEXT, FORMAT_OPTIONS ( FIELD_TERMINATOR = ',', STRING_DELIMITER = '"' ) );
External table This allows you to create tables that reference data stored outside the SQL database. CREATE EXTERNAL TABLE MyExternalTable ( Column1 INT, Column2 NVARCHAR(50) ) WITH ( LOCATION = 'myfolder/myfile.csv', DATA_SOURCE = MyExternalDataSource, FILE_FORMAT = MyFileFormat );