Dragonfly's SSD Data Tiering feature innovatively expands in-memory databases by integrating SSD storage to manage massive datasets cost-effectively, maintaining high performance and low latency. This hybrid approach addresses the limitations of RAM's high costs by utilizing SSDs, which have become faster and more affordable, to store less frequently accessed data on disk while keeping frequently accessed data in memory. Dragonfly uses a shared-nothing architecture where each thread manages its own storage file, storing only entry data on disk while metadata remains in RAM, ensuring efficient data retrieval. It employs io_uring for asynchronous I/O operations, bypassing the Linux page cache to optimize memory usage and prevent redundant data copying. The system intelligently identifies and manages "hot," "cold," and "cooled" entries, with a background process offloading rarely accessed data to disk and promoting it back to RAM when needed, thus balancing speed with capacity. Benchmarks show that Dragonfly outperforms ElastiCache in read throughput and maintains lower latency under load, highlighting its potential for large-scale data management with reduced infrastructure costs.