To run an SDP (Spark Declarative Pipeline) in parallel with dynamic parameters, you need to understand that SDP is "smart"—it builds a dependency graph and runs everything it can at the same time by default.Here is a simple breakdown of how to handle...
You just need the pipeline CLI that comes with PySpark itself.In the Dockerfile, install PySpark with the pipelines extra:RUN pip install --no-cache-dir "pyspark[pipelines]"This installs the spark-pipelines CLI, which is required to run Spark Declara...
I would recommend the best ways to install the driver depending on your specific environment:1. Recommended: Use Unity Catalog VolumesFor modern Databricks runtimes (13.3 LTS and above), storing JARs in a Unity Catalog Volume is the standard for sec...
I think this error is something a lot of people hit when moving from regular PySpark to Spark Declarative Pipelines in Spark 4.1.I believe the main reason it shows up is because SDP doesn’t work like normal PySpark where you can run things cell by ce...
This is a really interesting find, and honestly not something most people expect from materialized views.Under the hood, MVs in Databricks declarative pipelines are still Delta tables. So when you set partitionOverwriteMode=dynamic and partition by a...
