Reflection is a key feature in Avro that facilitates data serialization and deserialization in a distributed environment. It enables automatic generation of code for serialization and deserialization, simplifying the process of working with complex data structures.

ResourceManager is used for scheduling and managing user jobs in a Hadoop cluster. It works in conjunction with the NodeManagers to allocate resources and monitor the execution of tasks on the cluster.

Speculative Execution is a technique employed for time-sensitive data processing in MapReduce. It involves running duplicate tasks on different nodes and using the result from the first one to finish. This helps mitigate delays caused by slow-performing tasks.

SecurityManager is a critical component in Hadoop's architecture, responsible for ensuring secure authentication and authorization within the Hadoop cluster. It plays a crucial role in protecting the integrity and confidentiality of the data.

In Spark, Resilient Distributed Datasets (RDDs) are immutable collections of data items distributed over a cluster. RDDs are the fundamental data structure in Spark, providing fault tolerance and parallel processing capabilities.

The 'SORT BY' clause in Apache Pig is used for sorting data in a dataset based on one or more fields. It arranges the data in ascending or descending order, providing flexibility in handling sorted data for further processing.

The default block size used by Hadoop Distributed File System (HDFS) for storing data is 128 MB. This block size is configurable but is set to 128 MB in many Hadoop distributions as it provides a balance between storage efficiency and parallel processing.

Spark is a popular Scala-based tool for interactive data analytics with Hadoop. It provides a fast and general-purpose cluster computing framework for big data processing, making it suitable for various data processing tasks.

The Failover Controller in a Hadoop cluster is responsible for detecting and handling the failure of DataNode machines. It ensures that data availability is maintained by redirecting tasks to healthy DataNodes when a failure occurs.

Apache Sqoop achieves efficient data transfer through parallel processing. It divides the data into smaller chunks and transfers them in parallel, utilizing multiple connections to improve performance and speed up the data transfer process between Hadoop and relational databases.