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.

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.

To address performance issues with Hive queries, analyzing query statistics and tuning data partitioning are essential steps. Analyzing query statistics helps identify bottlenecks, while tuning data partitioning optimizes data retrieval efficiency. These approaches can significantly improve query performance by reducing resource consumption and enhancing data access patterns within the Hive Architecture.

Hive supports different types of User-Defined Functions, including Scalar, Aggregate, and Table functions. Understanding these types helps users create custom functions tailored to their specific use cases, enhancing the flexibility and power of Hive.

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.