Rack-awareness in HDFS is significant for enhanced data locality. It ensures that data replicas are stored across different racks within the same data center, minimizing network traffic and reducing data transfer times. This improves overall performance and fault tolerance.

In Hive, the storage of metadata is managed by the Metastore component. Metastore stores metadata information such as table schemas, column types, and storage location. It plays a crucial role in ensuring the integrity and organization of metadata for efficient querying in Hive.

In the case of a catastrophic data center failure, implementing geo-redundancy is the most effective recovery strategy. Geo-redundancy involves maintaining copies of data in geographically diverse locations, ensuring data availability and resilience in the face of a disaster affecting a single data center.

For large-scale Hadoop deployments, Snapshot strategies are essential for efficient and rapid disaster recovery. Snapshots capture the current state of the Hadoop file system, allowing administrators to create a point-in-time copy of the data. This enables quick recovery in case of data corruption or loss, ensuring minimal downtime.

The Secondary NameNode in HDFS is not a backup node but is responsible for performing periodic checkpoints of the file system metadata. It creates a merged, updated checkpoint from the edits log and the current metadata, reducing the time needed for the NameNode to recover in case of failure.

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.