Final answer:
Apache Spark has drawbacks such as being difficult to analyze, having significant hardware requirements, and being highly dependent on theoretical assumptions that may not hold in practice, potentially leading to performance issues.
Step-by-step explanation:
While Apache Spark is a powerful tool for handling big data processing and analytics, it does have its limitations. Some demerits include:
- Difficult to analyze: Spark can be cumbersome when dealing with complex data processing tasks or when fine-tuning performance.
- Hardware requirements: Due to its in-memory computation, Spark requires a substantial amount of RAM, leading to higher hardware costs.
- Spark's performance is highly dependent on theoretical assumptions, and may not always match up to these expectations in practice. This dependency can result in less than optimal performance under certain conditions.
Additionally, while it offers near real-time processing with its micro-batch processing model, Spark is not optimized for true real-time processing. Scalability can also be a concern, as Spark's ability to scale effectively can be limited by the underlying hardware and software architecture.