Hive integrates with various components of the Hadoop ecosystem such as Flume for data ingestion, Sqoop for data transfer between Hadoop and relational databases, and Spark for fast data processing and analytics, ensuring a comprehensive solution for handling diverse data processing and analysis needs.

Designing and deploying a User-Defined Function (UDF) in Hive for custom encryption logic involves developing a Java class implementing the UDF, which can encapsulate the desired encryption algorithm. This approach offers flexibility and performance for handling sensitive data encryption requirements at the row level in Hive tables.

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.

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.

Apache Druid's indexing mechanism optimizes query performance by employing various indexing strategies such as dimension-based indexing, time-based indexing, bitmap indexing, and aggregation-based indexing, which accelerate data retrieval by efficiently organizing and accessing data based on specific dimensions, time values, bitmaps, and pre-computed aggregations, respectively, resulting in faster query execution when used in conjunction with Hive.

Addressing regulatory compliance issues in Hive requires implementing a range of measures such as data masking to anonymize sensitive information, strict authentication and authorization protocols to control access, data lineage tracking for regulatory reporting, and data retention policies to manage the data lifecycle. These measures ensure that the organization complies with regulatory requirements while maintaining efficient data processing practices within Hive.