Apache Kafka is commonly used for real-time data streaming. It is a distributed event streaming platform that enables the processing of real-time data feeds and events, making it valuable for scenarios that require low-latency data ingestion and processing.

For historical data analysis and trend prediction, MapReduce is a suitable processing method. MapReduce efficiently processes large volumes of data in batch mode, making it well-suited for analyzing historical datasets and deriving insights for trend prediction.

In Apache Flume, the Source is used to extract data from various data sources. It acts as an entry point for data into the Flume pipeline, collecting and forwarding events to the next stages in the pipeline.

In the case of a failed Hadoop job, the 'syslog' is the most useful log information for identifying the root cause. It contains system logs, including error messages and diagnostic information, providing insights into the issues that led to the job failure. Analyzing the syslog is essential for effective troubleshooting.

The primary purpose of Hadoop Streaming API is to allow the integration of non-Java programs for processing data in Hadoop. It enables the use of scripts (e.g., Python or Perl) to serve as mappers and reducers, expanding the flexibility of Hadoop to process data using various languages.

Effective monitoring of Namenode is key for ensuring data security and compliance in Hadoop clusters. The Namenode stores metadata and plays a crucial role in maintaining the integrity and security of the data stored in HDFS.

Hadoop operates on the principle of "Data parallelism," which enables it to process large datasets by dividing the workload into smaller tasks that can be executed in parallel on multiple nodes.

Advanced cluster monitoring in Hadoop involves analyzing log files for predictive maintenance and optimization. Log files contain valuable information about the cluster's performance, errors, and resource utilization, helping administrators identify and address issues proactively.

In Hadoop, YARN (Yet Another Resource Negotiator) plays a critical role in scheduling and coordinating workflow execution in data pipelines. YARN manages resources efficiently, enabling multiple applications to share and utilize resources on a Hadoop cluster.

In Apache Oozie, Decision actions allow conditional control flow in workflows. They enable the workflow to take different paths based on the outcome of a condition, providing flexibility in designing complex workflows.