Sharding is a technique used in distributed database systems to horizontally partition data across multiple servers or nodes. Each shard contains a subset of the data, allowing for parallel processing and improved scalability as the dataset grows. It helps distribute the workload evenly and can enhance performance by reducing the data retrieval time.

Real-time data processing offers the advantage of immediate insights and responses to incoming data, allowing organizations to react quickly to changing conditions, detect anomalies, and capitalize on opportunities as they arise. Unlike batch processing, which involves processing data in large volumes at scheduled intervals, real-time processing enables continuous data analysis and decision-making, leading to enhanced agility, competitiveness, and customer satisfaction.

Apache Flink is primarily used for real-time stream processing, enabling the processing of continuous streams of data with low latency, high throughput, and exactly-once semantics.

Data latency is a crucial metric monitored to ensure data pipeline reliability. It measures the time taken for data to travel from the source to the destination, indicating the efficiency and responsiveness of the pipeline. Monitoring data latency helps detect delays and bottlenecks, enabling timely optimizations to maintain pipeline reliability and meet service-level agreements (SLAs).

A composite index includes additional columns beyond those in the index key, allowing queries to retrieve necessary data directly from the index without accessing the table data, enhancing query performance.

ETL optimization techniques primarily focus on boosting data processing speed. This involves refining the Extract, Transform, and Load (ETL) processes to make them more efficient, reducing overall execution time.

When selecting between data modeling tools like ERWin and Visio, considerations should include evaluating their feature set, compatibility with existing systems, cost, and the availability of support and documentation to meet the project's requirements effectively.

Dimensions in Dimensional Modeling are categories or entities that provide context to the facts stored in the Fact Table. They contain descriptive attributes that help in analyzing and understanding the data.

A conceptual data model represents the high-level structure and relationships between data entities. It is independent of any specific database management system and focuses on the business concepts and rules.

In an RDBMS, a view is a virtual table that represents the result of a database query. It provides a way to present data in a structured manner without storing the actual data, thus simplifying data access and enhancing security.

Leveraging physical data modeling involves optimizing table indexes, partitioning data appropriately, and organizing the physical layout of data to enhance performance and address specific performance issues in the database application.

Direct extraction involves querying a database directly to retrieve specific data sets based on defined criteria. This method is often used when only a subset of data is required for analysis or processing.