<![CDATA[Rainfall is one of the most important climatological parameters that plays a significant role in supporting agriculture, water resource management, and hydrometeorological disaster mitigation in Indonesia. As the volume of meteorological data continues to increase, technologies capable of processing large-scale data efficiently are required. This study aims to analyze rainfall patterns in Indonesia using a Big Data approach by utilizing Apache Spark as the primary data processing framework. The data used in this research consist of daily rainfall data from 34 provinces in Indonesia obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) in CSV format. This research employs a descriptive quantitative method consisting of data ingestion, data cleaning, data transformation, and descriptive statistical analysis using Apache Spark with PySpark. The analysis was conducted to obtain information regarding the number of valid records, average rainfall, maximum rainfall, and minimum rainfall values for each province. The results indicate that Apache Spark is capable of processing rainfall data in a structured, efficient, and automated manner. Based on the analysis, West Sumatra Province recorded the highest average rainfall at 16.01 mm/day, while Central Sulawesi Province recorded the lowest average rainfall at 0.30 mm/day. In addition, the highest maximum rainfall was observed in Bengkulu Province at 186.4 mm. The use of Spark DataFrame and aggregation functions such as count(), avg(), max(), and min() proved effective in supporting data processing and analysis activities. The findings demonstrate that Apache Spark can serve as an effective solution for large-scale climatological data processing and provide valuable information to support decision-making in agriculture, water resource management, and disaster mitigation in Indonesia.]]>
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