Spark Structured Streaming in Apache Spark is used for processing large-scale streaming data in real-time. It provides a high-level API for stream processing with the same underlying engine as batch processing, offering ease of use and fault-tolerance.

The Credential Store component in Hadoop's security architecture is responsible for storing and managing secret keys. It helps secure sensitive information such as credentials and encryption keys, enhancing the overall security of the Hadoop ecosystem.

Optimizing MapReduce job performance involves considering the format of input data. Using input compression, such as Hadoop's default compression codecs, can reduce the amount of data transferred between nodes, improving job efficiency.

Advanced use of Hadoop Streaming API involves the implementation of MapReduce for efficient data sorting and aggregation. MapReduce is a key processing model in Hadoop, and integrating it with Streaming API allows for complex data processing tasks, including sorting and aggregation, in a distributed fashion.

In a sudden performance degradation scenario, the primary focus should be on memory usage. High memory consumption can lead to performance issues as it affects task execution and overall cluster efficiency. Analyzing memory usage can help identify resource bottlenecks and optimize performance.

For advanced Hadoop development, Apache Spark is crucial for integrating custom processing logic. Spark provides a powerful and flexible platform for big data processing, supporting advanced analytics, machine learning, and custom processing through its rich set of APIs.

The Snappy compression codec in Hadoop is renowned for its high compression ratio and fast decompression speed. It is particularly suitable for scenarios where low latency is crucial, making it a popular choice for big data processing.

Large tables in HBase are divided and distributed across the cluster based on Row-Key Partitioning. Rows with similar Row-Key values are grouped together, and the distribution is determined by the Row-Key, facilitating efficient data retrieval and parallel processing.

The approach for building a Hadoop data pipeline for real-time analytics from social media data streams involves using Apache Sqoop for ingestion, Apache Storm for processing real-time data, and Apache HDFS for storage. This combination ensures efficient data transfer, real-time processing, and scalable storage.

Sqoop uses the Generic JDBC connector for efficient data import/export between Hadoop and specific Relational Database Management Systems (RDBMS). It provides a generic interface to interact with various databases, making it versatile and widely applicable.