In Dimensional Modeling, a Star Schema is a schema design where a central fact table is surrounded by dimension tables, resembling a star shape when visualized. Each dimension table is connected to the fact table.

In the context of data warehousing and analyzing sales data, the most suitable data modeling technique for representing relationships between dimensions and measures is the Star Schema. This schema design simplifies data retrieval and analysis by organizing data into dimensions and a central fact table, facilitating efficient querying and reporting.

Talend provides built-in functions for data validation, cleansing, and enrichment. These functions help in ensuring high data quality by performing various operations on the data.

A columnar store is a database design pattern that stores data in columns rather than rows, allowing for faster data loading and retrieval, especially when dealing with analytical queries that involve aggregations or scanning large datasets.

A data security policy typically includes key components such as access controls, encryption mechanisms, and data backup procedures. Access controls regulate who can access data and under what circumstances, while encryption ensures that data remains confidential and secure during storage and transmission. Data backups are essential for recovering lost or corrupted data in the event of a security breach or system failure. Together, these components help mitigate risks and protect against unauthorized access and data breaches.

Metadata in the ETL process plays a crucial role in describing data structures, documenting lineage, defining relationships, and capturing transformations, facilitating efficient data management and governance.

Common challenges in extracting data from heterogeneous sources include data inconsistency, security concerns, and integration complexity due to differences in formats and structures.

Apache Flink is a streaming processing framework that provides fault tolerance and guarantees exactly-once processing semantics. It achieves fault tolerance through its distributed snapshot mechanism, which periodically checkpoints the state of the stream processing application. Additionally, Flink's transactional processing capabilities ensure exactly-once semantics by managing state updates and output operations atomically.

Bulk loading is preferred for its efficiency when dealing with large datasets. It involves loading data in large batches, which reduces overhead and improves performance compared to other loading techniques.

Normalization typically reduces storage space by eliminating redundancy but may lead to more complex queries due to the need for joins. Denormalization increases storage space by duplicating data but simplifies query complexity by reducing the need for joins.

Strategies for optimizing data loading in ETL processes include incremental loading, where only changed data is processed, and parallel processing, which distributes the workload across multiple resources for faster execution.

In Apache Spark, the process of persisting intermediate data in memory to avoid recomputation is known as caching. This technique enhances performance by storing RDDs or DataFrames in memory for reuse in subsequent operations, reducing the need for recomputation.