True: Data Lake scalability is important to business leaders as it allows them to manage large volumes of data and make decisions based on that data, which can help drive business growth. 

True: Scalability and speed are related but not the same thing. A scalable system can ensure that operations are carried out efficiently, even with an increase in data and user volume, but it is not a guarantee that all operations will always be fast. 

True: While some might believe that more scalable systems are less secure due to their complexity and greater number of components, scalability and security are not mutually exclusive. With proper planning and implementation, it is possible to create scalable systems that are also highly secure, ensuring data protection and user privacy. 

True: Scalability can have a significant impact on the reliability of a system. A system that is not scalable can experience performance issues, instability, or crashes as the amount of data and user demands increase. On the other hand, a scalable system can maintain reliability even as demands increase by ensuring that users can access and utilize data as needed. 

True: While data lake scalability can help improve data security, companies must also adopt other security measures, such as data encryption and access control, to ensure adequate protection of sensitive data. 

True: Data Lake scalability is important for any company that needs to manage and store data, regardless of the size of the data volume. 

True: Data Lake scalability involves both the technology and the people who manage and use the data. Companies must invest in training and skills development to ensure their employees are equipped to effectively manage large volumes of data. 

True: Scalability can have an indirect impact on data quality. A scalable system can ensure that data is stored, processed and accessed efficiently, even when the volume of data increases significantly. This efficiency can lead to better data quality as users can access and analyze up-to-date and accurate information without experiencing bottlenecks in system performance. However, scalability alone does not guarantee data quality; companies must also implement processes and practices to maintain data quality over time. 

True: Horizontal and vertical scalability are different ways to increase the capacity of a system. Horizontal scaling involves adding more servers to the system to distribute the workload, while vertical scaling involves improving the capabilities of a single server, such as increasing memory, processing power, or storage capacity.  

True: Data Lake scalability is important even for companies that do not have immediate expansion plans, as it allows them to manage large volumes of data efficiently and cost-effectively.