The mapred.reduce.tasks property in MapReduce allows for the customization of the number of Reduce tasks. This property can be set to control the parallelism of the Reduce phase based on the characteristics of the data and the cluster.

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.

The Partitioner in MapReduce determines how the data output from Mappers is distributed to Reducers. It partitions the data based on a specified key, ensuring that all data for a given key is processed by the same Reducer. This influences the way data is grouped and processed during the shuffle phase in the MapReduce job.

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 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.