Hi @skolukmar, Yes, you can control the size of microbatches in Delta Live Tables on Databricks using options similar to Spark Structured Streaming. You can use **`maxBytesPerTrigger`** to limit the data processed per microbatch by setting a maximum byte size, and **`maxFilesPerTrigger`** to limit the number of files considered in each trigger. For example, `.option("maxBytesPerTrigger", 104857600)` sets a 100 MB limit per microbatch, while `.option("maxFilesPerTrigger", 100)` restricts it to 100 files. These settings help manage workload and optimize pipeline performance. Is there anything specific you’re trying to achieve with these settings? Maybe I can help further!

One other thought -- if you are considering using pandas_udf api, there is a way to control batch size there:pandas_udf guide   note the comments there about arrow batch size params.