Build scalable data pipelines with Apache Spark.
What it does
Guides the development of scalable Apache Spark applications for batch processing, streaming analytics, and machine learning.
When to use it
Use for large-scale data processing, distributed analytics, streaming, machine learning, and ETL pipelines.
How to use it
It changes how the agent builds, tests, and optimizes Apache Spark data-processing applications.
What you provide
- An existing project
Latest review
What you need · 2
The Python examples specify Python 3.7 or later.
The compatibility section specifies Java 8 or later.
Access · 2
This skill
pyspark
Execute
Installs PySpark
Apache Spark
Execute
Deploys Spark applications to clusters