### Q1: What is one key advantage of using Apache Spark for data processing in the finance sector?  
### Q2: In which situation would Apache Kafka be preferred over Apache Spark for data processing, and why?  
### Q3: How does the ability to perform in-memory data processing in Apache Spark enhance its performance compared to traditional disk-based systems?  
### Q4: How can Apache Spark be utilized to detect fraudulent activities in customer transactions?  
### Q5: What are some specific business scenarios where Apache Kafka’s event streaming capabilities would be beneficial?  
### Q6: How do the functionalities of Apache Spark and Kafka align with the needs of a fintech organization?  
### Q7: How can effective communication of technical concepts improve teamwork in data engineering projects?  
### Q8: What strategies can be adopted to clearly convey the advantages of Apache Kafka to non-technical stakeholders?  
### Q9: In what ways can enhancing your communication skills lead to better project outcomes in data engineering?