Impala achieves faster query performance compared to Hive by utilizing in-memory processing. Unlike Hive, which relies on MapReduce and disk-based processing, Impala keeps frequently accessed data in memory, reducing query latency and improving overall performance.

For large-scale Hadoop deployments, predictive analytics is crucial for proactive cluster health and performance management. Predictive analytics leverages historical data and machine learning models to predict potential issues, allowing administrators to take preventive measures and optimize the cluster's overall performance.

In Crunch, a PCollection is used to represent a distributed dataset in Hadoop. It is a parallel collection of data, and Crunch provides a high-level Java API for building data processing pipelines.

Hive is the component in the Hadoop ecosystem primarily used for data warehousing and SQL queries. It provides a high-level language, HiveQL, for querying data stored in Hadoop's distributed storage, making it accessible to analysts familiar with SQL.

In scenarios involving the joining of large datasets, the optimization features of Apache Pig, such as query optimization and parallel execution, significantly improve data processing efficiency. These optimization techniques help in handling large-scale data transformations more effectively, ensuring better performance in complex processing tasks.

Data replication is crucial in Hadoop administration for ensuring data availability and system reliability. Hadoop replicates data across multiple nodes in the cluster to provide fault tolerance. If a node fails, the data can still be retrieved from its replicated copies on other nodes.

For log file processing in Hadoop, the TextInputFormat is commonly used. It treats each line in the input file as a separate record, making it suitable for scenarios where log entries are present in a line-by-line format.

To troubleshoot network bandwidth issues in a Hadoop cluster, an advanced technique involves the use of Jumbo Frames. Jumbo Frames allow the transmission of larger packets, reducing overhead and improving network efficiency, which is crucial for optimizing data transfer in a Hadoop environment.

Clustering algorithms are essential in Big Data for extracting patterns and insights from large, unstructured datasets. They group similar data points together, revealing inherent structures in the data.

Apache Flume's architecture is based on the Pub-Sub (Publish-Subscribe) model. It involves the flow of data from multiple sources (publishers) to multiple destinations (subscribers), providing flexibility and scalability in handling diverse data sources in Hadoop environments.