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.

The HDFS Balancer is a crucial component in monitoring Hadoop clusters. It ensures data replication and consistency by redistributing data blocks across the nodes to maintain a balanced storage load. This helps prevent data skew and ensures optimal performance in the cluster.

Structuring a MapReduce job for optimal performance with diverse data types involves using appropriate Data Serialization techniques. This ensures efficient data transfer between Map and Reduce tasks, especially when dealing with varied data formats and structures.

The component of HDFS responsible for data replication and storage is DataNode. DataNodes are responsible for storing and managing the actual data blocks and replicating them to ensure fault tolerance.

Custom implementations in MapReduce often involve overriding the map() method for tailored data processing. The map() method defines how input data is transformed into intermediate key-value pairs, a crucial step in the MapReduce process.

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.

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.