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.

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.

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.

Sqoop is the tool in the Hadoop ecosystem best known for data ingestion from various sources into HDFS. It simplifies the transfer of data between Hadoop and relational databases, facilitating the import and export of data in a Hadoop cluster.

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.