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.

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.

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.

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.

When migrating large datasets to Hadoop, considerations for data integrity and efficiency should include ensuring data consistency and validation. It involves verifying that data is accurately transferred, maintaining its integrity during the migration process.

In the context of dealing with skewed data in MapReduce, Speculative Execution is a technique that helps distribute the load more evenly across reducers. It involves launching backup tasks for slow-running tasks on different nodes to ensure timely completion.