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.

HBase Families are used to categorize columns into logical groups. Columns within the same family are stored together in HBase, which helps in optimizing data storage and retrieval.

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.

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.

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.

Logging is a common practice in debugging Hadoop applications. Developers use logging statements strategically to capture information about the flow and state of the application at various points. This helps in diagnosing issues, monitoring the application's behavior, and improving overall performance.