Data Compression is an essential step in data loading to optimize the storage and processing of large datasets in Hadoop. Compression reduces the storage space required for data and speeds up data transfer, improving overall performance in Hadoop clusters.

The 'Reduce' method in the Reducer class is essential for aggregating the outputs generated by the Mapper tasks. It processes the intermediate key-value pairs, performs the required operations, and produces the final result of the MapReduce job.

Parquet is a column-oriented file format in Hadoop designed for optimal query performance on large datasets. It organizes data in a columnar fashion, allowing for efficient compression and improved read performance, making it suitable for analytical workloads.

In Hadoop Streaming, the InputFormat serves as a connector between the script and the Hadoop framework. It defines how the data is read and presented to the mapper for processing. The InputFormat specifies the input data's structure and how it should be split and processed.

In Hadoop, the process of adding more nodes to a cluster is known as Scaling Out. This involves increasing the number of nodes in the cluster to handle growing data volumes and enhance processing capabilities. Scaling out is a key strategy to accommodate the scalability requirements of big data applications.

In Hive, Predicate Pushdown is a mechanism that enables more efficient data retrieval by pushing filtering conditions closer to the data source. It helps to skip over irrelevant data early in the query execution process, improving performance.

When planning the capacity of a Hadoop cluster, network bandwidth is a critical metric for balancing the load across DataNodes. It ensures efficient data transfer and prevents bottlenecks in the network, optimizing the overall performance of the cluster.

Heap dumps in Hadoop applications can be utilized for analyzing memory issues. By capturing and analyzing heap dumps, developers can identify memory leaks, inefficient memory usage, and other memory-related issues, facilitating advanced debugging and optimization of the application's memory footprint.

Partitioning in Apache Hive is significant for improved query performance. By partitioning data based on certain columns, Hive can skip unnecessary data scans during query execution, resulting in faster query performance and reduced resource consumption.

Advanced Sqoop integrations often involve Apache Hive for optimized data transfers and transformations. Hive provides a data warehousing infrastructure on top of Hadoop, allowing for SQL-like queries and efficient data processing.