Security News > 2020 > October > AWS launches Amazon Timestream, a serverless time series database for IoT and operational applications
Amazon Timestream addresses these challenges by giving customers a purpose-built, serverless time series database for collecting, storing, and processing time series data.
Amazon Timestream integrates with popular data collection, visualization, and machine learning tools that customers use today, including services like AWS IoT Core, Amazon Kinesis and Amazon MSK, Amazon QuickSight, and Amazon SageMaker, as well as open source, third-party tools like Grafana and Telegraf.
"What we hear from customers is that they have a lot of insightful data buried in their industrial equipment, website clickstream logs, data center infrastructure, and many other places, but managing time series data at scale is too complex, expensive, and slow," said Shawn Bice, VP, Databases, AWS. "Solving this problem required us to build something entirely new. Amazon Timestream provides a serverless database service that is purpose-built to manage the scale and complexity of time series data in the cloud, so customers can store more data more easily and cost effectively, giving them the ability to derive additional insights and drive better business decisions from their IoT and operational monitoring applications."
"Cloud Native and IoT are both core competencies for us, so we're very pleased to see that Amazon Timestream is 100% serverless, and that it has tight integration with AWS IoT Core rule actions to easily ingest data without any custom code. Organizations who have a use case to capture and process time series data should consider using AWS Timestream as a scalable and reliable solution."
"When we heard AWS was building a time series database service-Amazon Timestream-we signed up for the preview and started testing our workloads. We've worked very closely with the AWS service team, giving them feedback and data on our use case to help ensure Amazon Timestream really excels in production for the size and scale of time series data we're dealing with."
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