In cases of uneven data distribution, the Partitioner in MapReduce helps balance the load by ensuring that data with the same key goes to the same reducer. This helps in achieving a more even distribution of processing tasks among reducers, improving performance.

Apache Storm is a tool in Hadoop designed for efficient real-time stream processing. It allows for the processing of data in motion, making it suitable for scenarios where low-latency and real-time insights are crucial.

The primary role of Apache Sqoop in the Hadoop ecosystem is data ingestion. Sqoop facilitates the transfer of data between Hadoop and relational databases, making it easier to import and export structured data. It helps bridge the gap between the Hadoop Distributed File System (HDFS) and relational databases.

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.

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 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.

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.

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.

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.

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.