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.

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 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 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.