Disadvantages of PySpark :

Performance Overhead : While PySpark offers ease of use and integration with Python, it may incur a performance overhead compared to writing Spark applications in Scala, the native language of Apache Spark. This overhead is primarily due to the dynamic nature of Python and the additional serialization/deserialization required when exchanging data between Python and Java Virtual Machine (JVM) processes.

Limited Type Safety : Python's dynamic typing can lead to potential runtime errors that may not be caught until runtime, unlike statically typed languages like Scala. This lack of type safety can make it more challenging to debug and maintain PySpark applications, especially for larger codebases.

Limited Development Tools : While PySpark benefits from Python's extensive ecosystem of libraries and tools, it may lack some of the development tools and IDE support available for other languages like Scala or Java. However, this gap is gradually narrowing as the PySpark community continues to grow and develop more robust tools and integrations.

Dependency Management : Managing dependencies and ensuring compatibility between different Python libraries and Spark versions can be challenging, especially in larger projects with complex dependency graphs. Users may encounter issues with version conflicts or incompatible libraries, requiring careful management and testing of dependencies.