Hadoop's High Availability feature allows automatic failover of the NameNode service in case of a crash. This ensures continuous operation of the Hadoop cluster even in the face of a NameNode failure, enhancing the reliability of the system.

The operation used for joining two datasets in Apache Pig is the JOIN operation. It enables the combination of records from two or more datasets based on a specified condition, facilitating the merging of related information from different sources.

In scenarios requiring high throughput and low latency, configuring HBase for in-memory compaction can be beneficial. This involves keeping more data in memory, reducing the need for disk I/O and enhancing data access speed. It's particularly effective for read-heavy workloads with a focus on performance.

Hadoop uses Speculative Execution to ensure that data processing continues even if a node fails during a MapReduce job. The framework identifies slow-running tasks and launches backup tasks on other nodes, ensuring timely completion of the job.

Replication is a key feature of Hadoop that ensures data redundancy and fault tolerance. Hadoop replicates data blocks across multiple nodes in the cluster, reducing the risk of data loss in case of node failures and enhancing the system's overall reliability.

Cost optimization in Hive enables efficient execution of transformation queries on large datasets. It involves optimizing the execution plan to reduce resource usage and improve performance while processing Hive queries.

Advanced data loading in Hadoop may involve the use of Protocol Buffers, a tool for efficient data serialization. Protocol Buffers is a language-agnostic data serialization format developed by Google for efficient and extensible data interchange.

LLAP (Live Long and Process) in Hive is designed for low-latency query processing. It allows long-running daemons to keep processing data, providing quick response times for time-sensitive data analysis scenarios. LLAP maintains cached data for faster query execution.

In HBase, data compression refers to the technique of storing the same data in different formats for performance optimization. It reduces storage space and improves read and write performance by compressing the data before storage.

YARN ensures high availability and fault tolerance through an Active-Standby configuration. In this setup, there are primary and secondary ResourceManager nodes. If the primary fails, the secondary takes over, ensuring continuous operation and fault tolerance.