Simplifying Data Ingestion in Microsoft Fabric: COPY INTO vs. BULK INSERT

Microsoft Fabric is an all-in-one data analytics platform that unifies data integration, storage, and analytics capabilities. One of its key components is the Fabric Data Warehouse, designed to handle large-scale data processing and analytical workloads. Traditionally, data ingestion into a data warehouse has relied on tools like Azure Data Factory (ADF) or dedicated data pipelines. However, Microsoft has introduced two new direct ingestion methods—COPY INTO and BULK INSERT—that allow users to load data into Fabric Data Warehouse tables without using ADF or complex pipelines.

These new capabilities simplify and accelerate the process of bringing data from external sources, particularly Azure Data Lake Storage Gen2, into Fabric Data Warehouse. This article explores the functionalities, key features, and considerations of these new ingestion methods and highlights the differences between them to help users determine the best option for their needs.

Copy Into

In Microsoft Fabric, the COPY INTO statement is a Transact-SQL (T-SQL) command designed for high-throughput data ingestion into Warehouse tables. It enables loading data from external storage accounts, such as Azure Data Lake Storage Gen2, directly into Warehouse tables.

Key Features of COPY INTO:

Supported File Formats: COPY INTO supports ingestion from files in PARQUET and CSV formats.
Source Compatibility: The primary supported data source for COPY INTO is Azure Data Lake Storage Gen2.
Flexible Options: Various parameters can be configured, such as specifying the source file format, defining a location to store rejected rows, and skipping header rows.

Considerations:

Authentication: By default, COPY INTO uses the credentials of the executing Azure Active Directory (Azure AD) user. Ensure that the user has appropriate permissions to access the specified storage account and container.
Error Handling: You can specify a location to store rows that fail to load using the REJECTED_ROW_LOCATION option, which is useful for capturing and analyzing problematic data rows.

Microsoft has also introduced the BULK INSERT statement in public preview as of Q1 2025, enhancing data import capabilities within Fabric.

BULK INSERT Statement

The BULK INSERT statement allows loading data from files stored in Azure Data Lake Storage Gen2 directly into Fabric Data Warehouse tables. This functionality is particularly beneficial for users migrating from SQL Server, as it minimizes code changes during the transition. The BULK INSERT statement supports additional configuration options, such as specifying the CODEPAGE for loading textual files, providing greater flexibility during data import operations.

Considerations:

Authentication: Ensure that the executing user has appropriate permissions to access the specified storage account and container.
Error Handling: Implement error handling mechanisms to manage and log any issues that arise during the bulk import process.
Performance: Utilize batching and parallelism where applicable to enhance data loading performance.

In Microsoft Fabric, both the COPY INTO and BULK INSERT statements are utilized for high-performance data ingestion into Data Warehouse tables. While they serve similar purposes, there are key differences in their functionality and use cases.

Feature	COPY INTO	BULK INSERT
Purpose	Designed specifically for loading data from external storage accounts into Fabric Data Warehouse tables.	Facilitates the import of data from files, particularly beneficial for SQL Server migration.
Source Compatibility	Supports external storage, primarily Azure Data Lake Storage Gen2.	Designed for file-based imports into Fabric Data Warehouse.
Support Formats	PARQUET, CSV	Various file formats, with additional encoding control (e.g., CODEPAGE).
Migration Compatibility	May require modifications when migrating from SQL Server.	Allows for minimal code changes when transitioning from SQL Server.
Error Handling	Supports REJECTED_ROW_LOCATION for capturing failed rows.	Requires explicit error handling strategies.
Performance	Optimized for high-throughput ingestion with parallel processing.	Performance can be enhanced through batching and parallel execution.

Conclusion

The introduction of COPY INTO and BULK INSERT in Microsoft Fabric simplifies the process of data ingestion into Fabric Data Warehouse. COPY INTO is ideal for loading data from external storage sources like Azure Data Lake Storage Gen2, offering flexibility and high-throughput capabilities. BULK INSERT, on the other hand, is particularly useful for organizations migrating from SQL Server, as it enables a smoother transition with minimal code changes and additional configuration options.

Choosing the right method depends on the specific use case—COPY INTO is best for external storage ingestion, while BULK INSERT is more suitable for file-based imports with added control over encoding and migration considerations. Both methods enhance data loading efficiency, reducing reliance on complex pipelines and accelerating data warehouse operations in Microsoft Fabric.

At Quadrant Technologies, we harness the power of Microsoft Fabric to unify data, streamline analytics. Contact us at marcomms@quadranttechnologies.com to explore how Microsoft Fabric can transform your data ecosystem.