### Q1: What are the main features of Apache Airflow that make it suitable for managing complex workflows?  
### Q2: How does Luigi differ from Apache Airflow in terms of task dependency management?  
### Q3: What challenges might arise when scaling a data pipeline in a rapidly growing fintech company, and how can they be addressed?  
### Q4: In what ways can data pipeline tools streamline the ETL process for a business in the finance sector?  
### Q5: How can the use of data pipelines enhance the decision-making process in a financial organization?  
### Q6: What specific benefits did the fintech company gain by implementing a real-time fraud detection system using Apache Airflow?  
### Q7: How can effective communication between data engineers and business stakeholders be facilitated through the use of data pipeline tools?  
### Q8: Why is documentation of workflows important for collaboration among teams working with data pipeline tools?  
### Q9: How can automated logging features of data pipeline tools contribute to better data governance in finance and healthcare sectors?