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.

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.

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.

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.

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.

Hadoop's HDFS snapshots play a crucial role in point-in-time recovery. They capture the state of the file system at a specific point, allowing users to revert to that state in case of data corruption or accidental deletion. This feature enhances data recovery capabilities in Hadoop.

The function of a Combiner in MapReduce is result aggregation. It combines (or aggregates) the intermediate output generated by the Mapper before sending it to the Reducer. This helps in reducing the volume of data transferred over the network and improves overall processing efficiency.

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.

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.

For real-time data processing in Hadoop, Apache Kafka is the most suitable framework. Kafka is a distributed streaming platform that allows for the ingestion and processing of real-time data streams. It provides high-throughput, fault tolerance, and scalability, making it ideal for building real-time data pipelines.

Kafka is an essential Hadoop ecosystem component for real-time processing and analysis of streaming data. It acts as a distributed publish-subscribe messaging system, providing high-throughput, fault tolerance, and scalability for handling real-time data streams.

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.