![]() ![]() ![]() Now, with Redshift Spectrum, analyzing all of this data is as easy as running a standard Amazon Redshift SQL query. However, as the cost of data storage has continued to drop, customers are increasingly storing vast amounts of data in Amazon S3 “data lakes,” including unstructured data that may never make it into a data warehouse. Īmazon Redshift is one of AWS’s fastest-growing services because it allows customers to perform complex queries on petabytes of structured data stored on high-performance local disks and get superfast performance – all for a tenth of the cost of traditional data warehouses. To get started with Redshift Spectrum, visit. Redshift Spectrum applies sophisticated query optimization, scaling processing across thousands of nodes so results are fast – even with large data sets and complex queries. With Redshift Spectrum, customers can extend the analytic power of Amazon Redshift beyond data stored on local disks in their data warehouse to query vast amounts of unstructured data in their Amazon S3 “data lake” – without having to load or transform any data. 19, 2017- Today, Amazon Web Services, Inc. (AWS), an company (NASDAQ: AMZN), announced Amazon Redshift Spectrum, a new feature that allows Amazon Redshift customers to run SQL queries against exabytes of their data in Amazon Simple Storage Service (Amazon S3). ![]() New capability allows Amazon Redshift customers to run analytic queries quickly and inexpensively against exabytes of data in Amazon S3 ![]()
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