In Sqoop, custom SerDes (Serializer/Deserializer) can be defined to handle complex data transformations during the import process. SerDes are essential for converting data between different formats during data import.

Scalability of a Hadoop cluster refers to its ability to handle the expected volume of data storage. A scalable cluster can easily accommodate growing data without compromising performance, making it a crucial aspect of Hadoop infrastructure design.

In the context of optimizing Hadoop applications, data compression plays a significant role in reducing network traffic. Compressing data before transferring it between nodes reduces the amount of data that needs to be transmitted, resulting in faster and more efficient data processing in the Hadoop cluster.

In the Hadoop Streaming API, custom scripts are often used to optimize the mapping and reducing processes. These scripts, usually written in languages like Python or Perl, allow users to define their own logic for data transformation, filtering, and aggregation, providing flexibility and customization in Hadoop data processing.

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.

HBase's in-memory storage feature makes it suitable for real-time read/write access. The data is stored in memory, enabling faster access for read and write operations, making it well-suited for applications requiring low-latency responses.

The JobTracker is an essential component in Hadoop for tracking job processing and resource utilization. It manages and schedules MapReduce jobs, tracks the progress of tasks, and monitors resource usage in the cluster. It plays a crucial role in coordinating job execution across the nodes.

The number of DataNodes in a Hadoop cluster is crucially influenced by storage capacity. It determines how much data can be stored and processed concurrently across the cluster. Ensuring sufficient storage capacity is essential for optimal performance and data processing capabilities.

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.

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 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.

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.