{"id":66664,"date":"2017-11-30T20:58:41","date_gmt":"2017-11-30T12:58:41","guid":{"rendered":"https:\/\/wp-productionenv-bjg9h2g2bgg5b8aa.southeastasia-01.azurewebsites.net\/news\/digitalglobe-transfers-entire-library-to-amazon-web-services\/"},"modified":"2017-11-30T20:58:41","modified_gmt":"2017-11-30T12:58:41","slug":"digitalglobe-transfers-entire-library-to-amazon-web-services","status":"publish","type":"post","link":"https:\/\/starpath.global\/news\/digitalglobe-transfers-entire-library-to-amazon-web-services\/","title":{"rendered":"DigitalGlobe Transfers Entire Library to Amazon Web Services"},"content":{"rendered":"<\/p>\n<p><strong>DigitalGlobe<\/strong>, now a business unit of <strong>Maxar Technologies<\/strong>, has migrated its entire 100-petabyte imagery library to <strong>Amazon Web Services<\/strong> (AWS), which will give its customers instant access to its vast library of geospatial images, eliminating the need to wait for tapes and disks to be retrieved for content, the company stated. DigitalGlobe, its sister division <strong>Radiant Solutions<\/strong>, and its partner ecosystem also leverage AWS\u2019 frameworks and tools to build machine learning applications that allow their customers to incorporate valuable geospatial information extracted from commercial satellite imagery into their work flows.<\/p>\n<p>DigitalGlobe is using AWS\u2019 suite of machine learning capabilities, including the newly released Amazon SageMaker, to build, train, and deploy machine learning applications. By using Amazon SageMaker\u2019s machine learning algorithms, DigitalGlobe states it can predict what images customers will request next based on their usage patterns. As a result, it can intelligently tier its image library to keep relevant imagery readily accessible in Amazon Simple Storage Service (Amazon S3) and the remainder of its library in AWS\u2019 lower-priced archival service, Amazon Glacier.<\/p>\n<p>In addition to optimizing storage costs, DigitalGlobe\u2019s customers gain faster access to the right images needed to extract actionable intelligence. To enable this, DigitalGlobe built its Geospatial Big Data platform (GBDX) on AWS to provide data curation, analysis, and delivery. DigitalGlobe\u2019s new product, GBDX Notebooks, will integrate Amazon SageMaker to make it easier for customers to build and deploy machine learning models that extract data from DigitalGlobe\u2019s satellite imagery library for detailed business insights.<\/p>\n<p>\u201cFew companies work with the sheer volume of data that DigitalGlobe does. When working at this volume, it\u2019s nearly impossible to scale and rapidly innovate without the cloud,\u201d said Teresa Carlson, vice president of worldwide public sector sales at AWS. \u201cDigitalGlobe was the first customer to use AWS Snowmobile \u2014 AWS\u2019 exabyte-scale data transfer service that uses a 45-foot long ruggedized shipping container pulled by a semi-trailer truck \u2014 to move their massive image library to AWS. Ever since, they have been pushing the boundaries of what\u2019s possible with large data sets.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>DigitalGlobe, now a business unit of Maxar Technologies, has migrated its entire 100-petabyte imagery library to Amazon Web Services (AWS), which will give its customers instant access to its vast library of geospatial images, eliminating the need to wait for tapes and disks to be retrieved for content, the company stated. DigitalGlobe, its sister division [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":66667,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[2],"tags":[],"class_list":["post-66664","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"_links":{"self":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts\/66664"}],"collection":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/comments?post=66664"}],"version-history":[{"count":0,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts\/66664\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/media\/66667"}],"wp:attachment":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/media?parent=66664"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/categories?post=66664"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/tags?post=66664"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}