Excessive denormalization in a database can lead to data redundancy, which means the same data is stored in multiple places. This redundancy can result in increased storage requirements and data inconsistency, as updating data in one place may not update it in others. While it may enhance query performance, it can complicate data maintenance and integrity.

Stateful applications, unlike stateless ones, retain client session data. This can make load balancing complex because the session data must be maintained consistently, potentially limiting scalability. Stateful applications often require additional strategies for handling session data, making them more challenging in terms of scalability.

A line chart is the most suitable visualization for displaying trends over time, making it easy to observe how a specific metric, like monthly sales data, changes over a period. It connects data points with lines, allowing for a clear view of trends.

Enterprise Data Modeling is focused on the conceptual design of data and includes high-level constructs that define the business. It provides an abstract representation of data elements and relationships without delving into specific technical details, making it a valuable starting point for data warehousing projects.

Partitioning large datasets during the extraction phase is used to break down the data into smaller, manageable subsets. This allows for parallel processing, which significantly enhances extraction performance by distributing the workload across multiple resources. It is especially beneficial when dealing with massive datasets.

As organizations moved from traditional data warehousing to more modern architectures, they encountered challenges in processing real-time streaming data. Stream Processing, often implemented with technologies like Apache Kafka, emerged as a solution. It allows organizations to process and analyze data as it is generated in real-time, enabling timely insights and decision-making from streaming data sources.

The main purpose of database normalization is to minimize data redundancy by structuring the database in a way that eliminates or reduces duplicate data. This reduces the risk of data anomalies, ensures data integrity, and makes data maintenance more efficient.

Association rule mining is a data mining technique used to discover interesting patterns or associations in a dataset, such as identifying sets of items that frequently co-occur. This is valuable for tasks like market basket analysis and recommendation systems.

An in-memory data warehouse stores the active dataset in RAM (Random Access Memory) instead of on disk. This design choice significantly accelerates query performance since RAM access is much faster than disk access. As a result, queries can be processed more rapidly, leading to improved data retrieval and analytics capabilities.

Data consistency in data warehousing ensures that data remains consistent and unchanged during a user query, even if the underlying data changes. This is typically achieved through techniques like snapshot isolation or locking mechanisms to maintain data integrity for concurrent user queries.