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.

Effective disaster recovery in a Hadoop environment involves crucial strategies like data replication across data centers. This ensures that even if one data center experiences a catastrophic failure, the data remains available in other locations. Regular backups, failover planning, and monitoring with alerts are integral components of a comprehensive disaster recovery plan.

The integration of Hadoop with Kerberos provides data encryption to secure sensitive data in transit. It ensures that data moving between different nodes in the Hadoop cluster is encrypted, adding an extra layer of protection against unauthorized access.

Oozie ensures efficient workflow management in complex data pipelines through its Workflow feature. Workflows in Oozie allow you to define a sequence of actions, manage dependencies, and handle the flow of data between Hadoop jobs. This is essential for orchestrating interdependent tasks and ensuring the overall efficiency of the data processing pipeline.

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.

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.

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.