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.

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.

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.

Hive utilizes SerDes (Serializer/Deserializer) to process data that is not structured in a traditional database format. SerDes allow Hive to interpret and convert data between its internal representation and the external format, making it versatile for handling various data structures.