Kerberos plays a crucial role in Hadoop cluster security, providing strong authentication and authorization mechanisms. It ensures that only authorized users and processes can access Hadoop resources, enhancing the overall security of the cluster.

The heartbeat status is crucial for assessing the health of a DataNode in a Hadoop cluster. DataNodes send periodic heartbeats to the NameNode to confirm their availability. If the NameNode stops receiving heartbeats from a DataNode, it may be an indication of a node failure or network issues.

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.

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.

The most effective combination for real-time log file ingestion and analysis in Hadoop is Kafka for data streaming and Spark Streaming for real-time data processing. Kafka provides high-throughput, fault-tolerant, and scalable data streaming, while Spark Streaming allows processing and analyzing data in near-real-time.