With the accelerating generation of enormous datasets, data processing tools are also increasing in computational efficiency as well as accuracy. Along with data mining and clustering techniques, tools that enable data visualization are also becoming mainstream mechanisms for extracting the maximum available useful information out of data. This is especially true for financial systems. Analyzing all available information and predicting short and long-term market trends with their fluctuations has been the cornerstone of trading strategies and giving investment advice for years. One of the interesting financial instruments, which can potentially utilize big data analysis and algorithms in several different ways are weather derivatives. Unlike other financial instruments, their research combines both market and weather analysis. This is quite complex and fits perfectly as an area we can explore big data processing and visualization capabilities. In this paper, we analyze the relationship between weather derivatives and weather and investigate how predicting the weather leads to predicting index values and therefore prices for weather derivatives. In pursuing this, we develop a web-based tool that employs weather prediction tools and use it to visualize and predict weather future prices. This research provides an example of how converging data streams can be efficiently analyzed.