Data-driven applications utilize two primary systems: Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP). OLTP systems are designed for real-time, day-to-day transactional processing, supporting activities like retail purchases and banking transactions with a focus on speed and reliability. In contrast, OLAP systems aggregate and analyze historical data to uncover trends and support strategic decision-making, often used in business intelligence and data mining. OLTP uses relational or NoSQL databases optimized for high-speed, concurrent transactions with strict data integrity, while OLAP employs denormalized schemas for efficient read-heavy queries across large datasets, often organized in data warehouses. While OLTP systems maintain dynamic, real-time data through continuous updates, OLAP systems manage large, historical datasets updated periodically. Organizations typically employ both systems together: OLTP for operational data generation and OLAP for comprehensive analysis, forming a feedback loop that enhances business operations and strategy.