Data Locality is a key indicator of the cluster's ability to process data efficiently in Hadoop. It refers to the practice of placing computation close to the data, reducing the need for data movement across the network. This enhances performance by maximizing the use of locally stored data.

In a scenario with multiple diverse jobs, optimizing resource allocation for balanced performance involves using the Fair Scheduler. The Fair Scheduler dynamically allocates resources among jobs based on demand, ensuring fair distribution and preventing resource starvation for any specific job type.

Batch processing typically has higher latency than real-time processing. In batch processing, data is collected and processed in predefined intervals, leading to delays, while real-time processing handles data as it arrives, reducing latency.

The Hadoop Streaming API handles different data formats through serialization. Serialization is the process of converting complex data structures into a format that can be easily stored, transmitted, or reconstructed. It allows Hadoop to work with various data types and ensures compatibility during the MapReduce process.

For high-speed data querying and analysis, Apache Impala is crucial. Impala provides low-latency SQL queries directly on Hadoop data, allowing for real-time analytics without the need for data movement. It is suitable for scenarios where rapid and interactive analysis of large datasets is required.

Apache Flume is highly suitable for event-driven data ingestion into Hadoop due to its fault tolerance. It can reliably collect and transport large volumes of data, ensuring that data is not lost even in the presence of node failures or network issues.

The role of a combiner in the MapReduce framework is partial aggregation. It performs a local reduction of data on each mapper node before sending it to the reducer. This reduces the volume of data transferred over the network and improves the efficiency of the data transformation process.

The most effective Oozie feature in this scenario is the Coordinator. Coordinators in Oozie allow you to define and manage time-based schedules for recurring jobs. They are well-suited for situations where data processing needs to be scheduled after data loading is completed, ensuring timely execution based on specified intervals.

The ResourceManager is a key component in a Hadoop cluster for managing and monitoring system health and fault tolerance. It manages the allocation of resources and schedules tasks across the cluster, ensuring efficient resource utilization and fault tolerance.

The use of a Schema Registry in Avro is crucial for ensuring data compatibility across different versions. It acts as a central repository for storing and managing schemas, allowing different components in a Hadoop ecosystem to access and interpret data consistently.

Apache Hive uses a Metastore to store the schema information for tables. The Metastore is a centralized repository that stores metadata, including table schemas, partition information, and storage location. This separation of metadata from data allows for better organization and management of data in Hive.

Resource Containers are key to YARN's ability to support multiple processing models on a single system. They encapsulate the allocated resources and are used to execute tasks across the cluster in a flexible and efficient manner.