Amid rising energy costs and geopolitical tensions, oil and gas companies face challenges in increasing production to meet demand, partly due to inefficient data management. The complexity of production and processing facilities, compounded by the vast amount of data generated, often leads to inefficiencies and underutilized capacity, with typical offshore platforms operating at only 77% of their potential. Leveraging data analytics, including predictive analytics and machine learning, can bridge these performance gaps by providing insights into equipment conditions and potential failures, thus enhancing efficiency and production. Edge computing emerges as a pivotal solution, enabling real-time data processing closer to the source, even in remote locations, thereby reducing latency, bandwidth costs, and dependency on internet connectivity while ensuring data privacy and reliability. A distributed cloud-to-edge database architecture is crucial for synchronizing and processing data effectively, allowing oil and gas operations to respond swiftly to dynamic conditions, ultimately contributing to increased production and potentially lower fuel prices.