Apache Airflow leverages Directed Acyclic Graphs (DAGs) to manage task dependencies in complex Hive-based workflows, ensuring tasks are executed in the correct order to meet dependencies and maintain workflow integrity, a crucial aspect of orchestrating intricate data processing tasks.

The Hive Execution Engine is responsible for executing the query plan generated by the Hive Query Processor, converting it into MapReduce jobs or other forms of tasks, and managing the overall execution of queries for efficient processing.

Impersonation in Hive enables users to execute queries on behalf of others, preventing identity spoofing, facilitating delegated administration, and supporting secure multi-tenancy environments, enhancing security and accountability within the system. It is crucial for proper Authorization and Authentication, ensuring that users access only authorized data and resources while maintaining accountability for their actions.

The point-in-time recovery method in Hive allows for restoring data to a specific moment in the past, providing granularity and flexibility in recovery operations, thereby enhancing data resilience and ensuring minimal data loss in the event of failures or errors.

Enabling Kerberos authentication, setting up SSL encryption for Hive communication, implementing role-based access control (RBAC), and enabling Hive auditing are essential steps during Hive installation to configure security measures that comply with regulatory standards, ensuring data protection, access control, and auditability.

Apache Airflow's monitoring feature facilitates easy monitoring and troubleshooting of Hive tasks by providing real-time insights into task execution progress and identifying any issues or bottlenecks in the workflow, enhancing overall workflow management and efficiency.

The fault tolerance mechanism in Apache Spark, particularly the use of Resilient Distributed Datasets (RDDs), complements Hive's fault tolerance by providing additional resilience against data loss and ensuring data availability and reliability, even in the event of node failures. This combination enhances the overall fault tolerance capabilities of the Hive-Spark ecosystem, making it more robust and reliable for large-scale data processing tasks.

Integrating Hive with Apache Spark involves retaining the Hive Metastore for metadata management while changing the execution engine to Apache Spark. Spark SQL Catalyst optimizes query plans for efficient execution, coordinated by the Apache Spark Driver and parsed by the Hive Query Processor.

Hive's integration with other Hadoop ecosystem components brings benefits like simplified data integration and enhanced scalability. However, it also introduces challenges such as increased complexity and potential performance overhead, making installation and configuration crucial for optimizing the overall system performance and functionality.

LDAP integration in Hive is crucial for enhancing security by centralizing authentication processes, enabling users to authenticate using their existing credentials stored in a central directory service. This integration simplifies user management and improves security posture by eliminating the need for separate credentials for each Hive service.