Data Pruning is a technique in Hadoop used to optimize data transformation by processing only relevant data. It involves eliminating unnecessary data early in the processing pipeline, reducing the amount of data that needs to be processed and improving overall job performance.

The High Availability architecture in Hadoop is designed to avoid a single point of failure in the filesystem. It ensures that critical components like the NameNode have redundancy and failover mechanisms in place to maintain continuous operation even if a node fails.

In advanced Hadoop data pipelines, Avro is used for efficient data serialization and storage. Avro is a binary serialization format that provides a compact and fast way to serialize data, making it suitable for Hadoop applications where efficiency is crucial.

Apache Oozie is primarily used for workflow orchestration in the Hadoop ecosystem. It allows users to define and manage workflows of Hadoop jobs, making it easier to coordinate and schedule complex data processing tasks in a distributed environment.

Data Cleaning is a critical step in Hadoop data pipelines, ensuring data quality and usability. This process involves identifying and rectifying errors, inconsistencies, and inaccuracies in the data, making it suitable for analysis and reporting.

The process of replicating data blocks to multiple nodes in Hadoop is known as Replication. This practice helps in achieving fault tolerance and ensures that data is available even if some nodes in the cluster experience failures.

For ensuring data durability in Hadoop, Fault Tolerance is a critical factor in capacity planning. Fault tolerance mechanisms, such as data replication and redundancy, help safeguard against data loss and enhance the system's ability to recover from failures.

The Bundle job is a special type of Oozie job designed to run workflows based on time and data triggers. It allows you to schedule and manage the execution of multiple workflows in a coordinated manner.

In advanced data analytics, Hive can be used with HBase for real-time query processing. HBase is a NoSQL, distributed database that provides real-time read and write access to large datasets, making it suitable for scenarios requiring low-latency queries.

To enhance cluster performance, Compression is a technique used to optimize data read/write operations in HDFS. Compressing data reduces storage space requirements and minimizes data transfer times, leading to improved overall performance.