Hadoop Streaming API's performance in processing real-time data can be improved by integrating Apache Kafka. Kafka provides high-throughput, fault-tolerant, and scalable messaging, making it a suitable choice for streaming data integration with Hadoop.

Rack Awareness is a critical consideration in Hadoop cluster capacity planning for ensuring high availability and fault tolerance. It involves the awareness of the physical location of nodes in racks, allowing Hadoop to replicate data across racks to enhance fault tolerance and reduce the risk of data loss.

The Columnar Storage feature of Sqoop would be most appropriate for extracting specific columns efficiently from a large database table. It optimizes the storage and retrieval of columnar data, enhancing performance for selective column extraction.

HBase ensures data integrity during write operations through the Write-Ahead Log (WAL). Before making changes to the data store, HBase writes the modifications to the WAL. In the event of a failure, the system can recover the changes from the WAL, ensuring data consistency and durability.

To troubleshoot connectivity issues between nodes, a Hadoop administrator should check the network configurations. It involves examining network settings, ensuring proper communication, and resolving any network-related problems that may impact the performance of the Hadoop cluster.

The correct option is 'Bundle.' In Oozie, a Bundle is a key feature that allows the integration with systems outside of Hadoop for triggering workflows. It helps in managing and coordinating multiple workflows as a single unit, facilitating more complex data processing scenarios.

In Oozie, the component responsible for executing a specific task within a workflow is the Oozie Action. It represents a unit of work, such as a MapReduce job or a Pig script, and is defined within an Oozie workflow.

An 'FileNotFound' exception in Hadoop is often caused by an incorrect input path specified in the job configuration. It's essential to verify and provide the correct input path to ensure that the Hadoop job can locate and process the required data.

In scenarios requiring large-scale data transformation, Apache Spark is often chosen for optimal performance. Spark's in-memory processing and efficient data processing engine make it suitable for handling complex transformations on large datasets with speed and scalability.

The Hadoop cluster configuration file that must be appropriately set up for efficient data processing is core-site.xml. This file contains configurations for the Hadoop core components and settings such as I/O settings and default filesystem name.