When data arrives as a succession of regular measurements, it is known as time series information. Processing of time series information poses systems scaling challenges that the elasticity of AWS services is uniquely positioned to address.
This elasticity is achieved by using Auto Scaling groups for ingest processing, AWS Data Pipeline for scheduled Amazon Elastic MapReduce jobs, AWS Data Pipeline for intersystem data orchestration, and Amazon Redshift for potentially massive-scale analysis. Key architectural throttle points involving Amazon SQS for sensor message buffering and less frequent AWS Data Pipeline scheduling keep the overall solution costs predictable and controlled.
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