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.

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.

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.

Advanced disaster recovery in Hadoop may involve using DistCp (Distributed Copy) for cross-cluster replication. DistCp is a Hadoop tool specifically designed for efficiently copying large amounts of data between clusters. It can be employed to replicate data for disaster recovery purposes, ensuring data consistency across different Hadoop clusters.

The Hadoop Federation feature contributes to disaster recovery and data management by improving fault tolerance. Hadoop Federation allows the distribution of namespace across multiple NameNodes, reducing the risk of a single point of failure. In the event of a NameNode failure, other NameNodes can continue to operate, contributing to a more robust disaster recovery strategy.

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.

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.

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.

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 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.