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 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.

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.

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.

When designing data transformation pipelines for real-time processing, scalability and latency control are key considerations to ensure the system can handle varying workloads efficiently and provide timely results.

An index seek operation utilizes seek predicates to locate the desired rows quickly based on the index key values, resulting in efficient data retrieval compared to scanning the entire table.

Apache Hive facilitates data warehousing and querying in the Hadoop ecosystem by providing a SQL-like interface for managing and querying large datasets stored in HDFS or other compatible file systems.

Achieving data consistency in a distributed database system requires employing various techniques. Some common approaches include the two-phase commit protocol, which ensures all nodes commit or abort a transaction together, maintaining consistency across distributed transactions. Quorum-based replication involves requiring a certain number of replicas to agree on an update before committing, enhancing fault tolerance and consistency. Vector clocks and version vectors track causality and concurrent updates, enabling conflict resolution and consistency maintenance in distributed environments. These techniques play a vital role in ensuring data integrity and coherence across distributed systems.

In a graph NoSQL database, relationships between data entities are represented using nodes. Nodes represent entities, and relationships between them are established by connecting these nodes through edges. This graph-based structure enables efficient traversal and querying of interconnected data.

HBase is a distributed, scalable, NoSQL database built on top of Hadoop. It provides real-time read/write access to large datasets, making it suitable for applications requiring random, real-time access to data.