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.

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.

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.

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.

The 'split-by' clause in Sqoop during data import is significant as it allows the user to define the primary key for splitting the data. This is crucial for parallel processing and efficient import of data into Hadoop.

Hive supports Bucketing as a form of dynamic partitioning. Bucketing involves dividing data into fixed-size files or buckets based on the column values, optimizing storage and improving query performance, especially for certain query patterns.

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.

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.

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.

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.

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.

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.