Real-time data processing differs from traditional methods in that it analyzes data as it is generated, allowing for immediate insights and actions, whereas traditional methods involve batch processing of historical data, leading to delayed insights. Real-time processing is essential for applications requiring instant responses to data changes, such as monitoring systems or streaming analytics, while traditional methods are suitable for tasks like periodic reporting or data warehousing.

Denormalization involves intentionally introducing redundancy into a database design for performance optimization purposes. It may be used when read performance is critical or when data retrieval needs are complex.

While data timeliness, integrity, and consistency are common data quality dimensions, data diversity is not typically considered a primary dimension. Data diversity refers to the variety of data types, formats, and sources within a dataset, which may affect data integration and interoperability but is not a direct measure of data quality.

Denormalization in a physical data model is often employed to enhance query performance by reducing the need for joins and simplifying data retrieval, albeit at the potential cost of some redundancy.

Apache Spark utilizes HDFS (Hadoop Distributed File System) for fault-tolerant storage of Resilient Distributed Datasets (RDDs). HDFS provides the necessary durability and fault tolerance required for distributed processing in Spark.

When planning a migration to a Data Lake architecture, considerations should include scalability to handle large volumes of data, robust data governance practices to ensure data quality and compliance, stringent data security measures to protect sensitive information, and thoughtful data structure design to enable efficient data processing and analysis.

One drawback of using indexes is the potential for decreased performance due to the additional overhead incurred during data modification operations. This overhead can slow down insert, update, and delete operations.

While an infinite retry mechanism may seem appealing for its potential to automatically resolve transient errors, it can introduce significant drawbacks. Delayed detection and resolution of underlying issues are major concerns. If the root cause of an error is not addressed promptly, it can lead to prolonged system instability and potential cascading failures. Additionally, an infinite retry mechanism can mask systemic problems, making it difficult to identify and address issues effectively.

HBase is a distributed, Key-Value database that runs on top of Hadoop. It provides real-time read/write access to large datasets, making it suitable for applications requiring low-latency data access.

Alerts in data pipelines can help identify various issues, including high latency, data corruption, and resource exhaustion. High latency alerts indicate delays in data processing, potentially affecting downstream applications. Data corruption alerts notify about anomalies or inconsistencies in the processed data, ensuring data integrity. Resource exhaustion alerts warn about resource constraints such as CPU, memory, or storage, preventing pipeline failures due to insufficient resources. By promptly identifying and addressing these issues, alerts contribute to maintaining the reliability and performance of data pipelines.