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.

One strategy to optimize a Reduce task that is slower than the Map tasks is Speculative Execution. In this approach, multiple instances of the same Reduce task are launched on different nodes, and the one that finishes first is accepted, reducing the overall job completion time.

The hdfs-site.xml file in Hadoop configuration specifies the number of replicas for each block in HDFS. This configuration is essential for ensuring fault tolerance and data reliability by controlling the replication factor of data blocks across the cluster.

When a Hadoop job is running slower than expected, the initial check should focus on Hadoop configuration. This includes parameters related to memory, task allocation, and parallelism. Suboptimal configuration settings can significantly impact job performance.

A local job runner in Hadoop unit testing simulates the Hadoop environment locally. It allows developers to test their MapReduce jobs on a single machine before deploying them on a Hadoop cluster, facilitating faster development cycles and easier debugging.

The primary challenge in unit testing Hadoop applications involving HDFS is handling large datasets. Unit testing typically involves smaller datasets, and dealing with the volume of data in HDFS during testing poses challenges. Strategies like using smaller datasets or mocking HDFS interactions are often employed to address this challenge.

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.