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.

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

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.

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.

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.

Shuffle in Apache Spark involves re-distributing data across partitions, often required after certain transformations like groupBy or sortByKey, making it an expensive operation due to data movement across the cluster.

The main difference between symmetric and asymmetric encryption lies in the use of keys. Symmetric encryption employs the same key for both encryption and decryption, making it faster and more efficient for large volumes of data. On the other hand, asymmetric encryption uses a pair of keys: a public key for encryption and a private key for decryption, offering better security but slower performance.

When handling missing values in the "Age" column, one approach is to impute the missing values based on other demographic information such as gender, location, or income. This method utilizes existing data patterns to estimate the missing values more accurately. Replacing missing values with the mean or median can skew the distribution, while removing rows may result in significant data loss. Flagging missing values for further investigation allows for manual review or additional data collection if necessary.

Rebuilding indexes to remove fragmentation and reorganize storage is a common strategy for addressing high index fragmentation. This process helps to optimize storage and improve query performance by ensuring that data pages are contiguous and reducing disk I/O operations.

Data profiling plays a crucial role in the data cleansing process by analyzing the structure, content, and quality of data to identify issues, anomalies, and inconsistencies. It involves examining metadata, statistics, and sample data to gain insights into data patterns, distributions, and relationships. By profiling data, data engineers can discover missing values, outliers, duplicates, and other data quality issues that need to be addressed during the cleansing process. Data profiling helps ensure that the resulting dataset is accurate, consistent, and fit for its intended purpose.

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.

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.