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.

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.

In Sqoop, custom SerDes (Serializer/Deserializer) can be defined to handle complex data transformations during the import process. SerDes are essential for converting data between different formats during data import.

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.

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.

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.

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.