Data Replication is the process in Hadoop that ensures no data loss in case of a DataNode failure. Hadoop replicates data across multiple DataNodes, and if one node fails, the replicated data on other nodes can be used, preventing data loss.

The integration of Apache Pig with Apache Spark allows for enhanced data processing and analysis in Hadoop. Apache Spark provides in-memory processing and advanced analytics capabilities, complementing Pig's data processing capabilities and enabling more sophisticated data workflows.

In complex data transformation tasks involving multiple data sources, Apache Sqoop is a preferred approach. Sqoop facilitates efficient and accurate data transfer between Hadoop and relational databases, ensuring seamless integration of diverse data sources for comprehensive transformations.

Load Balancing is key to maintaining the efficiency of a Hadoop cluster as data volume grows. It ensures that the computational load is evenly distributed among the nodes in the cluster, preventing any single node from becoming a bottleneck.

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.

In a scenario involving streaming data, the Avro file format would be most efficient. Avro is a binary serialization format that supports schema evolution and is suitable for streaming data due to its compact structure and efficient serialization, making it well-suited for real-time data processing in Hadoop.

Authentication is the process by which Hadoop ensures that a user or service is actually who they claim to be. It involves verifying the identity of users or services before granting access to the Hadoop cluster.

HDFS ensures data integrity during transmission through checksum verification. Each block of data is associated with a checksum, and the checksums are verified during read operations to detect and correct any data corruption that may have occurred during transmission. This mechanism enhances the reliability of data stored in HDFS.