In performance optimization, Memory tuning is critical for efficient resource utilization and task scheduling in Hadoop. Proper memory configuration ensures that tasks have sufficient memory, preventing performance bottlenecks and enhancing overall cluster efficiency.

Data Compression is crucial for optimizing storage capacity in Hadoop cluster capacity planning. It reduces the amount of space required to store data, enabling more efficient use of storage resources and improving overall cluster performance.

Parquet optimizes performance through columnar storage. It stores data column-wise instead of row-wise, allowing for better compression and efficient processing of specific columns during complex data processing operations. This reduces the I/O overhead and enhances query performance.

HDFS achieves fault tolerance through data replication. Each data block is replicated across multiple nodes in the Hadoop cluster. If a node or block becomes unavailable, the system can retrieve the data from its replicated copies, ensuring data reliability and availability.

In scenarios requiring complex query capabilities, utilizing composite keys in data modeling can be effective. Composite keys allow for hierarchical organization and efficient retrieval of data based on multiple criteria, enabling the execution of complex queries in HBase.

Kerberos in Hadoop provides authentication, ensuring that only authorized users can access the Hadoop cluster. It uses tickets to verify the identity of users and prevent unauthorized access, thus enhancing the security of the Hadoop environment.

The number of Mappers in a custom MapReduce job is primarily determined by the size of the input data. Each input split is processed by a separate Mapper, and the total number of Mappers is influenced by the size of the input data and the configured input split size.

In Hadoop administration, HDFS Balancer is essential for balancing data and processing load across the cluster. The HDFS Balancer utility redistributes data blocks across DataNodes to ensure uniform data distribution and prevent data imbalance.

In Impala, Data Caching is a mechanism that speeds up data retrieval operations. Caching involves storing frequently accessed data in memory, reducing the need to read from disk and improving query performance. It is particularly useful for repetitive queries on large datasets.

ZooKeeper plays the role of configuration management in the Hadoop ecosystem. It is a distributed coordination service that helps manage and synchronize configuration information across the cluster, ensuring consistency and reliability in a distributed environment.