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In Microsoft Fabric, the Delta Lake table format is the standard for analytics. Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to big data and analytics workloads.
All Fabric experiences generate and consume Delta Lake tables, driving interoperability and a unified product experience. Delta Lake tables produced by one compute engine, such as Fabric Data Warehouse or Synapse Spark, can be consumed by any other engine, such as Power BI. When you ingest data into Fabric, Fabric stores it as Delta tables by default. You can easily integrate external data containing Delta Lake tables by using OneLake shortcuts.
To achieve interoperability, all the Fabric experiences align on the Delta Lake features and Fabric capabilities. Some experiences can only write to Delta Lake tables, while others can read from it.
The following matrix shows key Delta Lake features and their support on each Fabric capability.
Fabric capability | Name-based column mappings | Deletion vectors | V-order writing | Table optimization and maintenance | Write partitions | Read partitions | Liquid Clustering | TIMESTAMP_NTZ | Delta reader/writer version and default table features |
---|---|---|---|---|---|---|---|---|---|
Data warehouse Delta Lake export | No | Yes | Yes | Yes | No | Yes | No | No | Reader: 3 Writer: 7 Deletion Vectors |
SQL analytics endpoint | Yes | Yes | N/A (not applicable) | N/A (not applicable) | N/A (not applicable) | Yes | Yes | No | N/A (not applicable) |
Fabric Spark Runtime 1.3 | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Reader: 1 Writer: 2 |
Fabric Spark Runtime 1.2 | Yes | Yes | Yes | Yes | Yes | Yes | Yes, read only | Yes | Reader: 1 Writer: 2 |
Fabric Spark Runtime 1.1 | Yes | No | Yes | Yes | Yes | Yes | Yes, read only | No | Reader: 1 Writer: 2 |
Dataflows | Yes | Yes | Yes | No | Yes | Yes | Yes, read only | No | Reader: 1 Writer: 2 |
Data pipelines | No | No | Yes | No | Yes, overwrite only | Yes | Yes, read only | No | Reader: 1 Writer: 2 |
Power BI direct lake semantic models | Yes | Yes | N/A (not applicable) | N/A (not applicable) | N/A (not applicable) | Yes | Yes | No | N/A (not applicable) |
Export Power BI semantic models into OneLake | Yes | N/A (not applicable) | Yes | No | Yes | N/A (not applicable) | No | No | Reader: 2 Writer: 5 |
KQL databases | Yes | Yes | No | No* | Yes | Yes | No | No | Reader: 1 Writer: 1 |
Eventstreams | No | No | No | No | Yes | N/A (not applicable) | No | No | Reader: 1 Writer: 2 |
* KQL databases provide certain table maintenance capabilities such as retention. Data is removed at the end of the retention period from OneLake. For more information, see One Logical copy.
Note
Currently, Fabric doesn't support these Delta Lake features:
Microsoft Fabric supports special characters as part of the table names. This allows the usage of unicode characters to compose table names in Microsoft Fabric experiences.
The following special characters are either reserved or not compatible with at least one of Microsoft Fabric technologies and should not be used as part of a table name: " (double quotes), ' (single quote), #, %, +, :, ?, ` (backtick).
Events
31 Mar, 11 pm - 2 Apr, 11 pm
The biggest Fabric, Power BI, and SQL learning event. March 31 – April 2. Use code FABINSIDER to save $400.
Register todayTraining
Module
Work with Delta Lake tables in Microsoft Fabric - Training
Tables in a Microsoft Fabric lakehouse are based on the Delta Lake technology commonly used in Apache Spark. By using the enhanced capabilities of delta tables, you can create advanced analytics solutions.
Certification
Microsoft Certified: Fabric Data Engineer Associate - Certifications
As a Fabric Data Engineer, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes.