The component of Apache Pig that translates scripts into MapReduce jobs is the Pig Compiler. It takes Pig Latin scripts as input and converts them into a series of MapReduce jobs that can be executed on a Hadoop cluster for data processing.

Apache Spark's ClusterManager feature allows for dynamic allocation of resources based on workload. The ClusterManager dynamically adjusts the resources allocated to Spark applications based on their needs, optimizing resource utilization.

Resource management is a key aspect of managing and optimizing cluster performance in Hadoop. Tools like YARN (Yet Another Resource Negotiator) play a crucial role in efficiently allocating and managing resources for running applications in the Hadoop cluster.

HBase is a distributed NoSQL database that integrates with the Hadoop ecosystem for efficient data storage and retrieval. It is designed to handle large volumes of sparse data and is well-suited for random, real-time read/write access to Hadoop data.

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.

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.

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.

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.

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.