Big Data is playing a major role in many areas like social networks, health, and climate. The modern climate information framework produces several exabytes of data and so it is often called Climate Big Data. Climate Big Data can be used to monitor and predict climate change, in addition to that, it can also be used to predict the possible diseases and climate change scenario. Till recently, GIS technology has been used to store the climate data with the spatial reference, but day-by-day the volume, velocity, and variety of climate data is increasing toward the exabyte. This requires efficient techniques to store and display such huge amounts of spatial climate Big Data to the end users. The intention of this chapter is to propose a Big Data architecture that analyzes the characteristics of spatial climate Big Data and to study the impact of climate changes on the outbreak of dengue. The proposed architecture was developed based on the existing lambda architecture. In addition to the features offered by lambda architecture, the proposed architecture consists of two new layers, namely, data ingestion block and visualizing layer. As like lambda architecture, the proposed architecture consists of a batch, speed, and serving layer. Proposed Big Data architecture mainly focuses on computing the monthly average maximum temperature, minimum temperature, precipitation, wind, relative humidity, and solar, and calculating the Pearson correlation coefficient between monthly average climate parameters and monthly dengue cases. Apache Hadoop MapReduce and Apache Hive are used to implement the batch layer. Implementation of the speed layer is done using Apache Hive streaming, Apache HBase is used to implement the serving layer, and ArcGIS 10.2 is used in this study for visualizing the results. The results show that our proposed architecture performs well in accurately predicting the correlation between the huge size of climate parameters and number of dengue cases. © 2017 by Taylor & Francis Group, LLC.