The FETCH statement in DB2 cursor operations retrieves rows from result sets. It is used to fetch the next row or a specified number of rows from the result set associated with a cursor. FETCH allows applications to retrieve and process data row by row, providing control over the data retrieval process. 

In a partitioned database environment, DB2 handles data distribution through hash-based distribution. This approach involves distributing data across partitions based on a hash function applied to one or more columns, known as partitioning keys. Hash-based distribution ensures even distribution of data across partitions, which helps in achieving balanced query processing and optimal performance in parallel processing environments. 

VARCHAR data type in DB2 is suitable for storing textual data of varying lengths, such as names, addresses, or descriptions. It allows for efficient storage and retrieval of character data. 

The DELETE FROM statement in SQL is used to delete all records from a table. It removes all rows from a table without deleting the table itself or its structure. This operation is irreversible, so it's important to use it with caution to avoid accidental data loss. 

DB2 Connect is a software product developed by IBM to allow applications to connect to DB2 databases. While it's versatile in its connectivity, one of its key focuses is integration with cloud systems like AWS and Azure. This integration enables seamless interaction between on-premises DB2 databases and cloud-based applications, fostering flexibility and scalability in the IT infrastructure. Cloud integration ensures that DB2 databases can adapt to modern cloud computing environments, supporting organizations in their journey towards digital transformation. 

Advanced techniques for performance tuning in DB2 include query optimization, which involves analyzing and fine-tuning SQL queries to improve execution efficiency. It often involves creating or modifying indexes, adjusting buffer pool sizes, and using advanced SQL features like common table expressions (CTEs) or window functions to optimize query execution plans. 

DB2's data compression feature offers efficient storage utilization by compressing data at the row and page levels. By eliminating redundant or repetitive data patterns, compression reduces the amount of storage space required to store large datasets. This results in cost savings and improved performance, as fewer disk I/O operations are needed to access compressed data. Additionally, DB2's data compression feature is transparent to applications and does not require changes to existing code or queries, making it easy to implement and manage. Overall, DB2's data compression feature effectively addresses the storage utilization requirements of organizations with large datasets. 

Denormalization increases query performance by reducing the number of joins required to retrieve data. By storing redundant data, it eliminates the need for joins, thus improving performance. 

Visual Explain provides comprehensive insights into query execution, aiding DB2 DBAs and developers in understanding how queries are executed, optimizing their performance, and identifying potential areas for improvement. 

The Health Monitor typically tracks metrics related to performance, usage, availability, and resource utilization. Performance metrics help in assessing the efficiency of database operations, usage metrics provide insights into the frequency of database access, availability metrics gauge the accessibility of the database system, and resource utilization metrics monitor the consumption of system resources such as CPU and memory.