To enhance cluster performance, Compression is a technique used to optimize data read/write operations in HDFS. Compressing data reduces storage space requirements and minimizes data transfer times, leading to improved overall performance.

Small files in Hadoop can lead to increased NameNode overhead, affecting cluster performance. To mitigate this impact, techniques like Hadoop Archives (HAR) or combining small files into larger ones can be employed. This reduces the number of metadata entries and enhances overall Hadoop cluster performance.

In a secure Hadoop environment, the Key Management Server (KMS) is used to manage and distribute encryption keys. KMS is a critical component for ensuring the confidentiality and security of data by managing cryptographic keys used for encryption and decryption.

In Hadoop, the Snappy compression codec is often used for its splittable property, enabling efficient parallel processing. Snappy is known for its fast compression and decompression speed, making it suitable for big data applications where performance is crucial.

Advanced Hadoop administration involves the use of SSL/TLS for securing data transfers within the cluster. Implementing secure socket layer (SSL) or transport layer security (TLS) protocols helps encrypt data during transit, ensuring the confidentiality and integrity of sensitive information.

In Java, the JobConf class is essential for configuring and executing Hadoop jobs. It allows developers to specify job-related parameters and settings for MapReduce tasks.

In real-time data transformation scenarios, Apache Storm is a suitable Hadoop ecosystem component. Apache Storm is designed for processing streaming data in real-time, making it effective for continuous and low-latency data transformations in Hadoop environments.

The 'COGROUP' operation in Apache Pig is significant for data grouping. It groups data from multiple relations based on a common key, creating a new relation with grouped data. This operation is crucial for aggregating and analyzing data from different sources in a meaningful way.

The default block size in HDFS for Hadoop 2.x and later versions is 128 MB. This block size is a critical parameter influencing data distribution and storage efficiency in the Hadoop Distributed File System.

In Big Data, 'Veracity' refers to the trustworthiness and reliability of data, ensuring that data is accurate and can be trusted for analysis.