Sqoop's Export tool is used to export data from HDFS back to a relational database. This tool is essential for moving data from Hadoop to a relational database for further analysis or reporting.

Apache Hive is primarily used for data processing in a Hadoop environment. It provides a SQL-like interface to query and analyze large datasets stored in Hadoop. It translates SQL queries into MapReduce jobs, making it easier for analysts and data scientists to work with big 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.

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.