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.

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.

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.

The concept of Multitenancy in YARN allows multiple data processing frameworks to use Hadoop as a common platform. It enables the sharing of resources among multiple applications and users.

In Apache Flume, a Channel can be configured to enhance data ingestion performance. Channels act as buffers that temporarily store and process events before they are transmitted to the next stage in the Flume pipeline. Proper configuration of channels is crucial for optimizing the data flow in Flume.

Hive's vectorization feature ensures efficient processing for complex joins and aggregations by performing operations in batch mode, reducing the need for row-wise processing and improving overall performance. It utilizes CPU instructions more effectively, making Hive queries faster.

If a Hadoop job fails due to a specific node repeatedly crashing, the diagnostic action that should be prioritized is checking the node logs for errors. This helps identify the root cause of the node's failure and allows for targeted troubleshooting and resolution.

The primary role of Apache Oozie in Hadoop data pipelines is workflow coordination. Oozie allows users to define and manage workflows that coordinate the execution of Hadoop jobs, making it easier to schedule and manage complex data processing tasks in a coordinated manner.