Cascading provides a Java-based API that simplifies the construction and management of data processing workflows. It enables developers to create complex data pipelines with ease, enhancing the efficiency of data processing in Hadoop.

To optimize query performance, Hive can store data in the Parquet format. Parquet is a columnar storage format that is highly efficient for analytics workloads, as it allows for better compression and retrieval of specific columns without reading the entire dataset.

Remote Procedure Call (RPC) is the primary protocol used for inter-node communication in a Hadoop cluster. It facilitates communication between nodes in the cluster, allowing them to exchange information and coordinate tasks effectively.

Batch processing in Hadoop is typically used for complex, time-insensitive data analysis. It involves processing large volumes of data at scheduled intervals, making it suitable for tasks that don't require immediate results.

Cascading uses a declarative style for defining data processing pipelines, allowing developers to focus on the logic of the computation rather than the low-level details of MapReduce. This is in contrast to the traditional procedural style of MapReduce programming, where developers need to explicitly define each step in the processing.

The Mapper in MapReduce allows for the transformation of data before it reaches the reducer phase. It processes input data and generates intermediate key-value pairs, which are then shuffled and sorted before being sent to the reducers for further processing.

In HBase, TimeToLive (TTL) settings on column families are used to define the retention and versioning policies of data. It determines how long versions of a cell are kept in the system before being automatically deleted.

Apache Hive optimizes data transformation tasks through query optimization. It employs techniques such as predicate pushdown, map-side joins, and dynamic partition pruning to enhance query performance and reduce the amount of data processed. This optimization improves the efficiency of data processing in Hive.

YARN (Yet Another Resource Negotiator) enhances Hadoop's processing capabilities by introducing a separate ResourceManager for resource management. In earlier versions, the JobTracker handled both resource management and job scheduling, limiting scalability. With YARN, ResourceManager handles resource allocation, allowing more flexibility and scalability in processing tasks.

For loading real-time streaming data into Hadoop, Apache Kafka is the most appropriate ecosystem tool. Kafka is designed for high-throughput, fault-tolerant, and scalable data streaming, making it suitable for real-time data ingestion into Hadoop clusters.