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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Apache Oozie handles dependencies between multiple Hadoop jobs using a Directed Acyclic Graph (DAG). The DAG defines the order and dependencies between tasks, ensuring that the subsequent tasks are executed only when the prerequisite tasks are completed successfully.

Cascading's Lingual feature enables the execution of complex join operations in data processing pipelines. Lingual is a SQL interface for Cascading, making it easier to express complex data transformations.