Final answer:
Spark, a distributed computing framework, has limitations including difficulties in analysis, parameterization, and communication. It is also limited by software and hardware requirements. Additionally, it lacks support for real-time processing and a built-in file management system.
Step-by-step explanation:
Spark, a popular distributed computing framework, has some demerits or limitations. First, it can be difficult to analyze data in Spark due to its complexity and the need for expertise in writing efficient Spark code. It also requires parameterization, which involves tuning various parameters to optimize performance. Additionally, Spark can be challenging to communicate with due to its distributed nature, where data is spread across multiple nodes.
Another limitation of Spark is that it tends to be limited by software and hardware requirements. It requires a cluster of machines to perform distributed processing, which might not be readily available or affordable for all users. This can make it more difficult for individuals or smaller organizations to utilize Spark for their data processing needs.
Lastly, Spark has some specific drawbacks. It does not have built-in support for real-time processing, which means it is not well-suited for applications that require immediate data processing and analysis. It also lacks a built-in file management system, which can make it more challenging to handle and organize large volumes of data.