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.

Profiling is a method used in ETL (Extract, Transform, Load) optimization to identify and eliminate bottlenecks in the data pipeline. It involves analyzing the performance of various components to pinpoint areas that need improvement or optimization.

Document-oriented databases are specifically designed to handle semi-structured data efficiently by allowing flexibility in the schema and supporting nested structures within documents.

When designing a data extraction process for real-time data sources, key considerations include scalability to handle large volumes of data, minimizing latency, and ensuring data consistency across systems.

Change Data Capture (CDC) is responsible for identifying changes in source data since the last extraction, allowing for the extraction of only the modified data. This reduces processing time and resources.

A physical data model addresses the implementation details of how data is stored, accessed, and managed in a database system, whereas a logical data model concentrates on the logical structure and relationships of data.

Data timeliness is crucial for maintaining high data quality as it ensures that the information being used is current and relevant. Timely data allows organizations to make informed decisions based on the most recent information available, improving the effectiveness of business operations and strategic planning. It reduces the risk of using outdated data that may lead to errors or inaccuracies in analysis and decision-making processes.

Data compression techniques in ETL optimization can benefit by reducing storage requirements and facilitating faster data transfer. Compressed data takes up less space and can be transmitted more quickly across the ETL pipeline.

Normalization in database design can lead to increased complexity due to the need for multiple tables and relationships. This can make querying and understanding the database more difficult. Additionally, it can result in slower query performance as joins are required to retrieve related data.