Alleviating resource contention in a Hive cluster requires a multifaceted approach, including optimizing Hive configurations, implementing resource pools and queues, tuning infrastructure resources, and monitoring query performance. By strategically allocating resources, prioritizing critical queries, and continuously monitoring system performance, Hive administrators can effectively alleviate contention issues and optimize resource management to ensure smoother query execution and improved overall cluster performance.

The primary methods used for recovering data in Hive include point-in-time recovery, which allows restoring data to a specific timestamp for consistency, and restoring from backups, leveraging previously created backups to recover lost or corrupted data, ensuring data integrity and availability for analytics and decision-making processes even after adverse events.

User-Defined Functions in Hive enable users to extend Hive functionality by defining custom functions in languages like Java, Python, etc., which can perform specialized operations on data, enhancing the capabilities of Hive for various use cases.

In the context of Hive, authentication verifies the identity of users accessing the system, while authorization determines the actions they can perform within Hive based on their authenticated identity and assigned permissions, ensuring secure access and control over Hive resources.

Configuring Hive integration with Apache Druid for real-time market data analysis involves steps such as data ingestion, schema design, query optimization, and ensuring data synchronization and consistency. These steps are essential for optimizing query performance, ensuring scalability, and maintaining data integrity in financial analysis scenarios.

The Hive Metastore is responsible for storing metadata about Hive tables and partitions, including information such as schemas, column names, data types, and partitioning details, facilitating efficient query processing.

Hive can integrate with Apache Kafka using various methods, including Kafka Connect, Hive Streaming, and custom scripts. However, using Kafka Connect provides a more streamlined and efficient approach to integrate Kafka with Hive, enabling seamless data transfer and processing between the two systems.

While Hive doesn't have a specific "Backup Scheduler" feature, implementing a dedicated Backup and Restore Tool can facilitate scheduling and managing backup and recovery operations efficiently, ensuring data integrity and availability in Hive environments.

High availability and fault tolerance in a Hive cluster can be achieved through various methods like redundant metastore databases, Hadoop federation, backup Namenode, and Hive replication. These strategies ensure data reliability and accessibility, minimizing downtime and enhancing the overall robustness of the Hive environment.

The Hive Execution Engine converts HiveQL queries into executable tasks, typically MapReduce jobs, for distributed processing across the Hadoop cluster.