Flume agents are composed of sources, sinks, and channels, which are responsible for data flow. Sources collect data, channels store and transport the data between sources and sinks, and sinks deliver the data to the destination. Channels act as the conduit for the data flow within Flume.

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.

Ticket Granting Ticket (TGT) is a mechanism in Hadoop security that allows users to obtain and renew tokens for accessing various cluster services. It plays a crucial role in the Kerberos authentication process.

Avro is a commonly used file format in Hadoop for efficient large-scale data processing. Avro's compact binary format and schema evolution capabilities make it suitable for storing and exchanging data between Hadoop components. It is particularly useful in scenarios where flexibility and efficiency in handling complex data structures are essential.

In a scenario of slow data processing, examining the configuration parameter related to MapReduce Slots is crucial. MapReduce slots determine the parallelism of data processing tasks, and adjusting this parameter can optimize the performance of the Hadoop cluster.

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.