Apache Flume's architecture supports distributed data collection through an agent-based model. Agents are responsible for collecting, aggregating, and transporting data across the distributed environment. This approach enables flexibility and scalability in handling diverse data sources and destinations.

The implementation of a Combiner in a MapReduce job impacts performance by reducing the intermediate data volume. A Combiner combines the output of the Mapper phase locally on each node, reducing the data that needs to be transferred to the Reducer. This minimizes network traffic and improves overall job efficiency.

Apache Kafka handles high-throughput data streaming through the feature of topic partitioning. This allows Kafka to divide and parallelize the processing of data across multiple partitions, enabling scalability and efficient data streaming in Hadoop environments.

The choice of file format, such as Parquet or ORC, impacts performance through the storage format. These formats optimize storage and retrieval, affecting factors like compression, columnar storage, and efficient data serialization. The right format can significantly enhance query performance in analytics workloads.

Hadoop administrators manage data replication and distribution through block placement policies. These policies determine how Hadoop places and replicates data blocks across the cluster, optimizing for fault tolerance, performance, and data locality. Manual configurations, automatic balancing, and compression techniques are also essential aspects of data management in Hadoop.

When facing a sudden increase in data volume, horizontal scaling is recommended. This involves adding more nodes to the existing cluster, distributing the data processing load, and ensuring scalability by increasing the overall cluster capacity.

Hive's LLAP (Live Long and Process) feature enhances query performance and enables the handling of large-scale data warehousing jobs by providing low-latency query responses.

In a real-time Big Data processing scenario, Apache Kafka is recommended for efficient data ingestion. Kafka is a distributed streaming platform that can handle large volumes of real-time data and provides reliable and scalable data ingestion capabilities, making it suitable for real-time processing scenarios.

HBase achieves scalability through horizontal scaling. It distributes data across multiple nodes, allowing the system to handle larger datasets by adding more machines to the cluster. This approach ensures that as the data grows, the system can scale out effortlessly.

In a large-scale Hadoop deployment, Data Replication is critical for maintaining optimal data storage and processing efficiency. Replicating data across multiple nodes ensures fault tolerance and high availability, reducing the risk of data loss in case of hardware failures.