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.

Data Replication is a key feature in Hadoop that ensures data processing continuity in the event of a DataNode failure. Hadoop replicates data across multiple nodes, and in case one node fails, the processing can seamlessly continue with a replicated copy from another node.

Data compression is crucial for managing and handling large datasets effectively in Hadoop development. Compression reduces the storage space required for data, speeds up data transmission, and enhances overall system performance by reducing the I/O load on the storage infrastructure.

In Hadoop, Apache Kafka is used for efficient, distributed, and fault-tolerant streaming of data. It serves as a distributed messaging system that can handle large volumes of data streams, making it a valuable component for real-time data processing in Hadoop ecosystems.

Apache Pig is a scripting language in Hadoop used for complex data transformations. It simplifies the development of MapReduce programs and is particularly useful for processing and analyzing large datasets. Pig scripts are written using the Pig Latin language.

To ensure data integrity, Hadoop employs checksums to detect and correct errors during data transmission. Checksums are used to verify the integrity of data blocks, reducing the chances of data corruption during storage and transfer.