Advanced Big Data analytics often employ Machine Learning techniques for predictive modeling and analysis. Machine Learning algorithms enable systems to learn and make predictions or decisions based on data patterns, contributing to advanced analytics in Big Data applications.

In HBase, the RowKey column is used to uniquely identify each row in a table. It serves as the primary key and is crucial for efficient data retrieval in HBase tables.

In HiveQL, the EXPLAIN command is used to show the query execution plan. It provides insights into how Hive intends to execute the given query, including the sequence of tasks and operations involved. Analyzing the execution plan helps optimize queries for better performance.

Data Serialization in Hadoop is crucial for optimizing read/write operations on large datasets. Serialization is the process of converting complex data structures into a format that can be easily transmitted or stored. In Hadoop, this optimization helps in efficient data transfer and storage.

In Flume, the Channel Selector mechanism allows for dynamic data routing and transformation. It helps in directing incoming data to different channels based on specified criteria, enabling flexibility in data processing and handling.

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.

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.