The HDFS Balancer is a crucial component in monitoring Hadoop clusters. It ensures data replication and consistency by redistributing data blocks across the nodes to maintain a balanced storage load. This helps prevent data skew and ensures optimal performance in the cluster.

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 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.

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.

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.

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.

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.

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.

In advanced data analytics, Hive can be used with HBase for real-time query processing. HBase is a NoSQL, distributed database that provides real-time read and write access to large datasets, making it suitable for scenarios requiring low-latency queries.

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

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.