
AWS Lambda and Step Functions enable parallel extraction of time series data from large satellite weather datasets by processing daily partitions simultaneously, with AWS Glue repartitioning results by geographical point.


<div><p>This article demonstrates how to extract time series from satellite weather data using AWS Lambda, Step Functions, and AWS Glue for serverless ETL processing.</p><ul><li>Use Lambda functions to process multidimensional satellite data in parallel by day</li><li>Step Functions orchestrates 365 parallel Lambda invocations for year-long dataset processing</li><li>Each Lambda function extracts data for 100 geographical coordinates from daily weather files</li><li>AWS Glue repartitions output data from day-based to geographical point-based partitioning</li><li>Solution processes ~100GB satellite dataset in 1-2 minutes using serverless architecture</li><li>Lambda requires 2048MB memory, 5-minute timeout, and netcdf4/pandas libraries as layers</li><li>Final output: one folder per geographical point containing full-year time series data</li></ul><p>This serverless approach enables scalable, cost-effective ETL for large multidimensional datasets without managing infrastructure.</p></div>


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