Apache Oozie integrates with other Hadoop ecosystem components, such as Hive and Pig, through Action Nodes. These nodes define specific tasks, such as MapReduce, Pig, or Hive jobs, and orchestrate their execution as part of the workflow.

The Fairness of a Hadoop cluster indicates the balance of load across its nodes. It ensures that each node receives a fair share of tasks, preventing resource imbalance and improving overall cluster efficiency.

The module in Apache Spark specifically designed for SQL and structured data processing is Spark SQL. It provides a programming interface for data manipulation using SQL queries, enabling users to seamlessly integrate SQL queries with Spark applications.

The correct option is 'SLA (Service Level Agreement).' In advanced Oozie workflows, SLA is used to manage job retries and error handling. It provides a mechanism to define and enforce performance expectations for various jobs within the workflow.

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.