Denormalization involves combining tables to increase query performance by reducing the need for joins, which can be resource-intensive. However, this may lead to data redundancy and decreased data consistency.

Apache ORC (Optimized Row Columnar) file format stands out in big data storage solutions due to its columnar storage approach, which organizes data by column rather than by row. This enables efficient compression and encoding techniques tailored to columnar data, leading to improved query performance and reduced storage footprint. Unlike row-based formats, ORC allows for selective column reads, enhancing query speed for analytical workloads commonly found in big data environments.

The relationship between "Product" and "Category" entities in this scenario is Many-to-Many, as each product can belong to multiple categories, and each category can have multiple products, forming a many-to-many relationship.

The Circuit Breaker pattern is a fault-tolerant design pattern used to manage failures in distributed systems. It temporarily blocks access to a service experiencing a failure, preventing cascading failures and allowing the service to recover. By detecting and isolating faulty components, the Circuit Breaker pattern promotes system stability and resilience, improving overall reliability and performance.

The procedural aspect of a data governance framework focuses on defining the processes, procedures, and workflows for managing data within an organization. This includes establishing roles and responsibilities, defining decision-making processes, and outlining procedures for data quality management, data security, and compliance. A robust procedural framework ensures that data governance policies are implemented effectively, leading to improved data quality, consistency, and reliability.

Determining the effectiveness of the data cleansing process involves measuring various data quality metrics such as completeness, accuracy, consistency, and timeliness. Comparing data quality metrics before and after cleansing helps assess the impact of cleansing activities on data quality improvement. Data profiling and outlier detection identify anomalies and discrepancies in the data. Soliciting feedback from stakeholders provides insights into their satisfaction with the data quality improvements.

Imputation is a data cleansing technique that involves filling in missing values in a dataset based on statistical methods such as mean, median, or mode imputation. It helps maintain data integrity and completeness by replacing missing values with estimated values derived from the remaining data. Imputation is commonly used in various domains, including data analysis, machine learning, and business intelligence, to handle missing data effectively and minimize its impact on downstream processes.

In Dimensional Modeling, a Fact Table stores quantitative, measurable facts about a business process or event. It typically contains foreign keys that reference dimension tables for context.

Apache Airflow is a widely used open-source workflow orchestration tool in the field of data engineering. It provides a platform for defining, scheduling, and monitoring workflows as directed acyclic graphs (DAGs). With features like task dependencies, parallel execution, and extensibility through plugins, Apache Airflow is well-suited for orchestrating data pipelines and managing data workflows in various environments.

Apache Hive provides a SQL-like interface called HiveQL for querying and analyzing data stored in Hadoop. This interface simplifies data querying for users familiar with SQL.