Hadoop operates on the principle of data distribution, allowing it to process large datasets in parallel. The data is divided into smaller blocks and distributed across the nodes in the cluster, enabling parallel processing and efficient data analysis.

Optimizing HDFS for both storage efficiency and data processing speed involves implementing data tiering. This strategy involves segregating data based on access patterns and placing frequently accessed data on faster storage tiers, enhancing performance without compromising storage efficiency.

Hadoop clusters use the advanced technique of Data Locality Optimization to enhance performance during data processing. This technique ensures that computation is performed on the node where the data resides, minimizing data transfer across the network and improving overall efficiency.

Apache HBase utilizes a column-family store as its primary storage model. Data is organized into column families, which consist of columns containing related data. This design allows for efficient storage and retrieval of large amounts of sparse data.

To manage Hadoop's file system namespace, a Hadoop administrator uses the ResourceManager. The ResourceManager is responsible for managing and scheduling resources across the Hadoop cluster, including handling job submissions and monitoring their execution.

In a Kerberized Hadoop cluster, the Ticket Granting Service (TGS) issues tickets for authenticated users. These tickets are then used to access various services within the cluster securely.

Apache Flume ensures end-to-end data delivery through an acknowledgment mechanism. It confirms the successful receipt of events, providing reliability in the face of network failures. This mechanism helps maintain data integrity and consistency throughout the data collection process.

The HDFS Storage Policies feature allows administrators to specify policies for moving and storing data blocks based on factors like performance, reliability, and cost. It provides flexibility in managing data storage within the Hadoop cluster.

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.

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.

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.

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.