In a situation of slow data processing, prioritizing network optimization is crucial. This involves examining and enhancing the network infrastructure to reduce data transfer latency and improve overall cluster performance. Efficient data movement across nodes can significantly impact processing speed.

In Hadoop, 'Data Locality' is a common technique used for distributing data uniformly across the cluster. It aims to place computation close to the data, reducing data transfer overhead and improving overall performance.

In HDFS, the NameNode manages the file system namespace and regulates access to files. It keeps track of the metadata, such as file names and block locations, ensuring efficient file system operations.

Load balancing is a key factor in Hadoop development to ensure the scalability of applications. It involves distributing the computational workload evenly across the nodes in a cluster, preventing bottlenecks and optimizing resource utilization. This is crucial for maintaining performance as the system scales.

In MRUnit, Mocking is a crucial concept for validating the output of MapReduce jobs. It involves creating simulated objects (mocks) to imitate the behavior of real objects, allowing for effective testing of MapReduce programs without the need for a Hadoop cluster.

The default input format for a MapReduce job in Hadoop is TextInputFormat. It treats input files as plain text files and provides key-value pairs, where the key is the byte offset of the line, and the value is the content of the line.

Oozie is used in the Hadoop ecosystem for orchestrating complex workflows of batch jobs. It allows users to define and manage workflows that involve the execution of various Hadoop jobs and actions, providing a way to coordinate and schedule data processing tasks.

In YARN, the ResourceManager is responsible for keeping track of the heartbeats from the Node Manager. The Node Manager periodically sends heartbeats to the ResourceManager to signal its availability and health status, enabling efficient resource management in the cluster.

In Spark, In-Memory persistence allows for storing frequently accessed data in memory, reducing the need to recompute it. This enhances the performance of Spark applications by leveraging fast in-memory access to the data.

Scalability of a Hadoop cluster refers to its ability to handle the expected volume of data storage. A scalable cluster can easily accommodate growing data without compromising performance, making it a crucial aspect of Hadoop infrastructure design.