Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)!
Last updated: 13-Oct-2026
[MF] Direct Lake
- {def} Power BI semantic model table storage mode option available in MF [1]
- optimized for large volumes of data to be quickly loaded into memory from Delta tables available in the OneLake [1]
- leverages VertiPaq query engine [1]
- {benefit} deliver query performance comparable to Import mode
- without the management overhead of data refresh cycles to load the entire data volume [1]
- similar to Import mode
- {difference}refreshes copies only metadata (aka framing)
- [Import mode] replicates the data and creates an entire cached copy of the data for the semantic model [1]
- depends on data preparation being done in the data lake [1]
- {benefit} enables high-performance interactive analysis [1]
- ideal for semantic models connecting to large data sources
- e.g. lakehouse, warehouse and other data source with Delta tables [1]
- performance depends on well-tuned Delta tables [1]
- {benefit} performance
- normally outperforms DirectQuery queries when loading and interacting with visuals in reports [1]
- {benefit}maximizes ROI because
- analyzed data volumes can exceed capacity's max memory limits [1]
- only the needed data to answer a query is loaded into memory [1]
- {benefit} minimizes data latencies
- makes new data available without refresh schedules [1]
- quickly and automatically synchronizing a semantic model with its sources [1]
- {operation} refresh
- low-cost operation that analyzes the metadata of the latest version of the Delta tables and updates references to the latest files in OneLake
- DL moves data preparation to OneLake
- <-- uses the full breadth of Fabric technologies for data prep
- incl. Spark jobs, T-SQL DML statements, dataflows, pipelines, etc.
- {operation} transcoding
- the process of loading data from OneLake only when a query requests a column for the first time
- {action} the semantic model receives a DAX (or MDX) query
- {step} determines what columns are needed to produce the result
- {step} determines which columns are already resident in memory [2]
- {step} loads the missing columns from OneLake [2]
- typically a fast operation, however it can depend on factors
- e.g. the cardinality of data stored in the columns
- remains resident until there's reason for it to be removed (evicted) from memory
- {reason} the model or table was refreshed after a Delta table update at the source [2]
- {reason} no query used the column for some time [2]
- {reason} other memory management reasons [2]
- e.g. memory pressure in the capacity due to other, concurrent operations [2]
- SKU determines the maximum available memory for each Direct Lake semantic model on the capacity [2]
- {operation} framing
- an operation triggered by a refresh of a semantic model [2]
- it usually takes only a few seconds to complete
- <-- because it's a low-cost operation where the semantic model analyzes the metadata of the latest version of the Delta Lake tables and updates to reference the latest Parquet files in OneLake [2]
- the process might evict resident table column segments and dictionaries from memory if the underlying data changed [2]
- the point in time of the refresh becomes the new baseline for all future transcoding events [2]
- DL queries only consider data in the Delta tables as of the time of the most recent framing operation [2]
- DL tables are queried to return data based on the state of the Delta table at the point of the most recent successful framing operation [2]
- that time isn't necessarily the latest state of the Delta tables [2]
- the semantic model analyzes the Delta log of each Delta table during framing to drop only the affected column segments and to reload newly added data during transcoding [2]
- {optimization} dictionaries usually aren't dropped when incremental framing takes effect, and new values are added to the existing dictionaries [2]
- helps reduce the reload burden and benefits query performance [2]
- when a Delta table receives no updates, no reload is necessary for columns already resident in memory [2]
- queries show far less performance impact after framing because incremental framing essentially enables the semantic model to update substantial portions of the existing in-memory data in place [2]
- can fail if a Delta table exceeds the Fabric capacity guardrails [2]
- e.g. when a Delta table has more than 10,000 parquet files [2]
- {architecture} Direct Lake on OneLake
- isn't coupled with the SQL analytics endpoint [2]
- offers tighter integration with OneLake features such as OneLake security [2]
- offers more efficient DAX query plans because [2]
- <-- isn’t required to check for SQL based security [2]
- fallback isn't supported [2]
- {architecture} Direct Lake on SQL analytics endpoint
- a DAX query might use DirectQuery fallback [2]
- involves seamlessly switching to DirectQuery mode [2]
- DirectQuery fallback
- retrieves data directly from the SQL analytics endpoint of the lakehouse or the warehouse [2]
- fallback occurs when SQL based security is detected in the SQL analytics endpoints
- a DirectQuery operation sends a query to the SQL analytics endpoint. Fallback operations might result in slower query performance [2]
- {setting} Keep your Direct Lake data up to date
- performs automatic updates of Direct Lake tables [2]
- via semantic model settings >> Refresh
- {enabled|default} ensures that data changes in OneLake are automatically reflected in the Direct Lake semantic model [2]
- the semantic model performs a framing operation whenever data modifications in the underlying Delta tables are detected [2]
- {recommendation} leave the setting on, especially when you have a small or medium-sized semantic model
- useful in case of low-latency reporting requirements and Delta tables are modified regularly [2]
- {disabled} it might be needed to disable automatic updates to allow completion of data preparation jobs or an ETL process before exposing any new data to consumers of the semantic model [2]
- users can trigger a refresh manually in the Fabric portal [2]
- via refresh schedule, or programmatically by using the Power BI REST API or TOM [2]
References:
[1] Microsoft Learn (2026) Direct Lake overview [link]
[2] Microsoft Learn (2026)
Acronyms:
API - Application Programming Interface
DL - Direct Lake
MDX - Multidimensional Expressions
MF - Microsoft Fabric
REST
SKU - Stock Keeping Unit
TOM - Tabular Object Model
T-SQL - Transact SQL
