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.

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.

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 the context of optimizing Hadoop applications, data compression plays a significant role in reducing network traffic. Compressing data before transferring it between nodes reduces the amount of data that needs to be transmitted, resulting in faster and more efficient data processing in the Hadoop cluster.