Denormalization increases redundancy by adding redundant data to improve query performance, while normalization reduces redundancy by organizing data into multiple related tables to ensure data consistency.

Normalization increases data integrity by reducing redundancy and ensuring that each piece of data is stored in only one place, reducing the risk of inconsistencies. Denormalization may introduce redundancy, leading to potential data integrity issues.

In the context of database systems, "vertical scaling" refers to increasing the capacity of a single server to handle more workload and data. This typically involves upgrading the server's hardware components, such as CPU, RAM, and storage, to accommodate growing demands. Vertical scaling offers simplicity in management as it involves managing a single server but may have limitations in terms of scalability compared to horizontal scaling, where additional servers are added to distribute the workload.

Apache NiFi offers lineage for data provenance, which enables users to track the origin and transformation history of data, crucial for data governance and troubleshooting purposes.

Challenges associated with real-time data processing include scalability, as systems need to handle increasing data volumes without sacrificing performance; latency, as there's a need for quick data processing to meet real-time requirements; and data consistency, ensuring that data remains accurate and coherent across distributed systems despite concurrent updates. Addressing these challenges is crucial for maintaining the reliability and effectiveness of real-time processing systems.

Version control in data modeling enables multiple team members to collaborate efficiently, track changes, revert to previous versions, and maintain a history of modifications, thereby enhancing productivity and quality.

Utilizing data lineage and metadata management involves tracing data lineage to identify the root cause of the anomaly, analyzing metadata to understand data transformations, and collaborating with relevant teams for swift resolution. This approach ensures that corrective measures are implemented effectively, addressing the issue's underlying cause and minimizing the impact on critical business operations.

In normalization, the process of breaking down a large table into smaller tables to reduce data redundancy and improve data integrity is called normalization. It involves organizing data to minimize redundancy and dependency.

Ensuring consistency and accuracy across datasets involves several steps, including standardizing data formats and units to facilitate integration. Identifying and resolving duplicates help eliminate redundancy and maintain data integrity. Entity resolution resolves conflicting records by identifying and merging duplicates or establishing relationships between them. Implementing data validation checks ensures that incoming data meets predefined standards and quality criteria.

In Apache Kafka, a topic is a category or feed name to which records are published. It serves as the high-level namespace for the data streams being processed by Kafka, allowing messages to be organized and managed.