Hi,

From your description, this is most likely not a configuration or REFRESH command issue, but incremental eligibility falling back to Full due to query pattern or source history.

• SQL part in the MLV: This is the most likely reason. LEFT JOIN + GROUP BY patterns often make Fabric unable to guarantee incremental correctness, so it silently falls back to Full Refresh. A good quick test is to create a simple single-table MLV (no joins or aggregations) and see if incremental works there.
• Using PySpark to create tables: The main thing to check is that CDF was enabled before the first data load and that you’re not rewriting tables using overwrite mode (append/merge patterns are safer for incremental scenarios).
• REFRESH MATERIALIZED LAKE VIEW command: This just executes whatever refresh policy Fabric determines is safe based on lineage, SQL pattern, and change tracking.

If you can share a simplified version of the MLV SQL, it would be easier to pinpoint the exact trigger.

Hi dragospopescu ,

The problem for the incremental refresh not being cascaded is due to the below reasons

• GROUP BY + LEFT JOIN pattern not incrementally maintainable this resets the incremental flag

• Bronze table is being overwritten instead of appended

• Unsupported aggregation pattern

• Also is your final notebook doing mode("overwrite") or mode("append")

If you can share more details of the MLV SQL can provide you the exact details.

If this post helps, then please appreciate giving a Kudos or accepting as a Solution to help the other members find it more quickly.
If I misunderstand your needs or you still have problems on it, please feel free to let us know. Thanks a lot!

Hello ksukhani & ssrithar 

As you can imagine, I can't provide even a simplified version of the codes, but I did my best below in trying to show you how the structure looks like. I think this should help well enough in the context of this post.

Bronze tables

These are created in similar fashion, relying on PySpark for parametrization, dynamic logic for file ingestion, ETL and clean-up, and finally the writing of the tables which is done as below.

df.write.mode("overwrite").format("delta").option("delta.enableChangeDataFeed", "true").partitionBy("Key").saveAsTable(table_name)

The overwrite is there because the input files have timestamps, so if say you already have data from 01.01.2026, when you upload the file for 01.02.2026 you don't need the previous data anymore. Thus, you need to overwrite the existing data in the lakehouse.

Silver MLV

Below is the structure/skeleton for the MLV, as mentioned at the beginning.

sql_statement = f"""

CREATE OR REPLACE MATERIALIZED LAKE VIEW mlv_nane
TBLPROPERTIES (delta.enableChangeDataFeed=true)

AS

SELECT 
    columns 
FROM (
    SELECT columns FROM table WHERE conditions
    UNION ALL
    SELECT columns FROM table WHERE conditions
    UNION ALL
    SELECT columns FROM table WHERE conditions
    UNION ALL
    SELECT columns FROM table WHERE conditions
) AS base_table

LEFT JOIN (SELECT DISTINCT columns FROM table) AS join_1 
    ON base_table.id = join_1.id

LEFT JOIN (SELECT DISTINCT columns FROM table) AS join_2 
    ON base_table.id = join_2.id

LEFT JOIN (SELECT DISTINCT columns FROM table) AS join_3 
    ON base_table.id = join_3.id

LEFT JOIN (SELECT DISTINCT columns FROM table) AS join_4 
    ON base_table.id = join_4.id

LEFT JOIN (SELECT DISTINCT columns FROM table) AS join_5 
    ON base_table.id = join_5.id

"""

spark.sql(sql_statement)

Testing

In the notebook I created for testing, the logic is something like this:

from pyspark.sql import Row

mock_row = Row( columns and values )

mock_df = spark.createDataFrame([mock_row])

mock_df.write.format("delta").option("delta.enableChangeDataFeed", "true").mode("append").saveAsTable(table_name)

Note: table_name from Testing is the same table_name from the Bronze layer.

Hi dragospopescu,

Thank you for reaching out to the Microsoft Fabric Community Forum. Also, thanks to ssrithar, ksukhani,  for those inputs on this thread.

Has your issue been resolved? If the response provided by the community member ssrithar, ksukhani,  addressed your query, could you please confirm? It helps us ensure that the solutions provided are effective and beneficial for everyone.

Hope this helps clarify things and let me know what you find after giving these steps a try happy to help you investigate this further.

Thank you for using the Microsoft Community Forum.