The Hadoop Distributed File System (HDFS) is designed to store and manage large datasets across multiple nodes in a distributed environment. It provides fault tolerance and high throughput for handling big data.

To optimize performance in Hadoop data pipelines, shuffling techniques are employed for effective data partitioning and distribution. Shuffling involves the movement of data between the Map and Reduce tasks, facilitating parallel processing and efficient resource utilization.

Hadoop's Rack Awareness feature optimizes cluster efficiency and data locality by strategically placing replicated data blocks on different racks. This reduces the risk of data loss due to rack failure and enhances overall fault tolerance, ensuring that data is available even in the face of hardware failures.

For real-time analysis of streaming data, Spark Streaming is more suitable than traditional MapReduce. Spark Streaming allows processing and analyzing data in real-time batches, making it ideal for scenarios like social media streaming data analysis where quick insights are crucial.

Advanced Hadoop applications often leverage Apache Spark for real-time data processing and analytics. Apache Spark is a powerful open-source data processing engine that provides high-level APIs for distributed data processing, making it suitable for complex analytics tasks.

The choice of file block size impacts Hadoop cluster capacity by influencing the efficiency of data storage and retrieval. Larger block sizes can lead to better storage utilization and reduced metadata overhead, increasing the overall capacity of the Hadoop cluster.

In Scala, the Scalding library is commonly used for interacting with Hadoop and performing big data processing. Scalding provides a higher-level abstraction over Hadoop's MapReduce, making it more convenient for Scala developers to work with large datasets.

For real-time data syncing between Hadoop and RDBMS, Sqoop can be integrated with Apache Kafka. Kafka enables the seamless and real-time transfer of data between Hadoop and relational databases, supporting continuous data integration.

Apache Pig's optimization mechanism is crucial for efficiently processing large volumes of data. It includes various optimizations like predicate pushdown and filter pushdown to enhance the performance of Pig scripts.

In scenarios where data processing efficiency is crucial, MapReduce is often the most effective Hadoop programming paradigm. It excels at processing large datasets in a distributed and parallel fashion, making it suitable for scenarios prioritizing efficiency over real-time processing capabilities.