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.

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.

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.

Avro's Schema Evolution feature allows the modification of data structures without requiring changes to the entire dataset. This flexibility is crucial for handling evolving data in Big Data environments.

Spark supports integration with AI frameworks through Spark MLlib. MLlib provides a scalable machine learning library that integrates seamlessly with Spark, enabling data analytics projects to incorporate machine learning capabilities.