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.

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 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.

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.

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.

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.