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 capacity planning, Network Bandwidth is essential for ensuring optimal data transfer speeds within a Hadoop cluster. Analyzing and optimizing network bandwidth helps prevent data transfer bottlenecks, enhancing overall cluster efficiency.

Data Cleaning is a critical step in Hadoop data pipelines, ensuring data quality and usability. This process involves identifying and rectifying errors, inconsistencies, and inaccuracies in the data, making it suitable for analysis and reporting.

The process of replicating data blocks to multiple nodes in Hadoop is known as Replication. This practice helps in achieving fault tolerance and ensures that data is available even if some nodes in the cluster experience failures.

For ensuring data durability in Hadoop, Fault Tolerance is a critical factor in capacity planning. Fault tolerance mechanisms, such as data replication and redundancy, help safeguard against data loss and enhance the system's ability to recover from failures.

The Bundle job is a special type of Oozie job designed to run workflows based on time and data triggers. It allows you to schedule and manage the execution of multiple workflows in a coordinated manner.

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.