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 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.

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.

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.

Oozie's bundling feature in complex data pipelines enhances workflow management by consolidating multiple workflows into a single unit. This simplifies coordination and execution, making it easier to manage and monitor complex data processing tasks efficiently.

The command used to import data from a relational database to HDFS in Sqoop is 'sqoop import.' This command initiates the process of transferring data from a source database to the Hadoop ecosystem for further analysis and processing.

DBInputFormat is ideal for processing structured data stored in databases in Hadoop. It allows Hadoop MapReduce jobs to read data from relational database tables, providing a convenient way to integrate Hadoop with structured data sources.

In Cascading, a 'Tap' represents an input source or output sink in the data processing pipeline. It serves as a connection to external data sources or destinations, allowing data to flow through the Cascading application for processing.

Replication is the process by which HDFS ensures that each data block has the correct number of replicas. This helps in achieving fault tolerance by storing multiple copies of data across different nodes in the cluster.

MapReduce handles large datasets in a distributed computing environment through data partitioning. The input data is divided into smaller chunks, and each chunk is processed independently by different nodes in the cluster. This parallel processing enhances the overall efficiency of data analysis.

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 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.