For integrating streaming data into Hadoop data pipelines, Kafka is a widely used tool. Kafka provides a distributed and fault-tolerant platform for handling real-time data feeds, making it suitable for streaming data integration with Hadoop.

The efficiency of data processing in Hadoop Streaming can be increased by using HashPartitioner for data partitioning. HashPartitioner ensures an even distribution of key-value pairs across the reducers, optimizing parallel processing.

Sqoop handles the import of large tables into Hadoop by splitting the data into smaller chunks. This process helps in parallelizing the import operation, making it more efficient and faster, especially when dealing with large datasets.

The most beneficial feature in processing large-scale log data with Hadoop Streaming API is Custom Script Execution. It allows users to write custom mappers and reducers in any programming language, facilitating flexible and efficient processing of log data based on specific requirements.

Compaction in HBase is the process of merging smaller HFiles into larger ones, reducing the number of files and improving read and write performance. It is essential for efficient space utilization and maintaining optimal performance in HBase clusters over time.

In a real-time data analysis project, Hadoop Streaming API can be effectively utilized by implementing short batch intervals. This approach reduces latency by processing data in smaller, more frequent batches, enabling near-real-time insights and analysis.

Apache Flume's Retry mechanism allows for backoff and retry in the event of sink failures. This enhances the robustness of the system by providing a mechanism to handle temporary failures and reattempt the operation.

Apache HBase enhances Hadoop's capabilities by providing real-time access to Big Data. Unlike HDFS, which is optimized for batch processing, HBase supports random read and write operations, making it suitable for real-time applications and scenarios requiring low-latency data access.

YARN enables dynamic resource allocation, allowing it to allocate resources efficiently between batch and real-time processing applications. This flexibility ensures that the cluster can adapt to varying workloads and allocate resources based on the specific needs of each application.

Checksums are used during data loading in Hadoop to ensure data integrity across the cluster. Hadoop calculates and verifies checksums for each data block, identifying and handling data corruption issues to maintain the reliability of stored data.

The Comparator in MapReduce determines the order in which the output keys are sorted before they are passed to the Reducer. It plays a crucial role in arranging the intermediate key-value pairs for effective data processing in the Reducer phase.

In a scenario requiring the analysis of large datasets with minimal latency, Impala would be the preferable choice. Unlike Hive, which operates on a batch processing model, Impala provides low-latency SQL queries directly on the data stored in HDFS, making it suitable for real-time analytics.