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.

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 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.

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 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 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.

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.

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.

Advanced Hadoop applications might use CombineFileInputFormat for custom data processing requirements. This InputFormat combines small files into larger input splits, reducing the number of input splits and improving the efficiency of processing small files in Hadoop.

Snappy compression in Parquet allows for efficient storage and faster query processing. Snappy is a fast and lightweight compression algorithm, making it suitable for use in Big Data processing environments like Hadoop.

For advanced data analytics, Hadoop Streaming API can be coupled with Apache Pig to handle complex queries and computations. Pig provides a high-level scripting language, Pig Latin, making it easier to express data transformations and analytics tasks.

Crunch optimizes the process of creating MapReduce jobs in Hadoop through Lazy Evaluation. It delays the execution of operations until the results are actually needed, reducing unnecessary computations and improving overall performance.