In data modeling, "Normalization" refers to organizing data in a structured manner by reducing redundancy and dependency, leading to an efficient database design that minimizes data anomalies.

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.

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).

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.

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.

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.

Database optimization involves various techniques such as creating indexes, rearranging tables, and tuning database parameters to enhance the performance of SQL queries.

Apache Flink supports programming languages like Java, Scala, and Python, providing developers with flexibility and ease of integration for building stream processing applications.

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.

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.