{"id":27681,"date":"2026-05-30T05:16:25","date_gmt":"2026-05-29T20:16:25","guid":{"rendered":"https:\/\/aireviewirush.com\/?p=27681"},"modified":"2026-05-30T05:16:25","modified_gmt":"2026-05-29T20:16:25","slug":"azure-netapp-recordsdata-for-eda-workloads-from-revolution-to-breakthrough-at-scale","status":"publish","type":"post","link":"https:\/\/aireviewirush.com\/?p=27681","title":{"rendered":"Azure NetApp Recordsdata for EDA workloads: From revolution to breakthrough at scale"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"post-51157\">\n<p>\n\t\tAzure NetApp Recordsdata is redefining what\u2019s potential for EDA within the cloud\u2014delivering scalable, high-performance storage that helps large concurrency, low latency, and constant manufacturing efficiency. With impartial benchmark validation and real-world adoption, organizations can now run EDA workloads at scale with out conventional storage bottlenecks.\t<\/p>\n<p class=\"wp-block-paragraph\" id=\"last-year-we-outlined-how-azure-netapp-files-was-helping-reshape-silicon-design-by-delivering-the-low-latency-high-throughput-storage-required-for-eda-workloads-at-cloud-scale-since-then-we-have-continued-to-extend-performance-and-scalability-today-we-are-advancing-that-progress-with-another-significant-step-forward\">Final 12 months, we outlined how <a href=\"https:\/\/azure.microsoft.com\/blog\/azure-netapp-files-revolutionizing-silicon-design-for-high-performance-computing\/\" target=\"_blank\" rel=\"noopener\">Azure NetApp Recordsdata helped reshape silicon design<\/a> by delivering the low-latency, high-throughput storage required for Digital Design Automation (EDA) workloads at cloud scale. Since then, we&#8217;ve continued to increase efficiency and scalability. Right this moment, we&#8217;re advancing that progress with one other important step ahead.<\/p>\n<p class=\"wp-block-paragraph\">Fashionable semiconductor design is outlined by scale. Hundreds of concurrent EDA jobs spanning simulation, synthesis, and verification run repeatedly towards shared datasets, the place even small variations in storage latency can ripple throughout complete design cycles. For a lot of groups, this has traditionally restricted how far EDA workflows might scale within the cloud. <\/p>\n<p class=\"wp-block-paragraph\">That constraint is now altering.<\/p>\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" id=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" target=\"_blank\" rel=\"noreferrer noopener\">Azure NetApp Recordsdata (ANF)<\/a> is redefining what is feasible for EDA within the cloud by delivering predictable, high-performance shared storage at large concurrency. With new impartial benchmark outcomes and rising adoption by main semiconductor firms, Azure is establishing itself as a viable\u2014and in lots of circumstances superior\u2014platform for contemporary EDA environments. <\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_53 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title \" >Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\" role=\"button\"><label for=\"item-6a20b24ae3cb8\" ><span class=\"\"><span style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input aria-label=\"Toggle\" aria-label=\"item-6a20b24ae3cb8\"  type=\"checkbox\" id=\"item-6a20b24ae3cb8\"><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/aireviewirush.com\/?p=27681\/#Why_EDA_storage_has_been_tough_to_scale_within_the_cloud\" title=\"Why EDA storage has been tough to scale within the cloud\">Why EDA storage has been tough to scale within the cloud<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/aireviewirush.com\/?p=27681\/#A_contemporary_strategy_Azure_NetApp_Recordsdata_for_EDA_at_scale\" title=\"A contemporary strategy: Azure NetApp Recordsdata for EDA at scale\">A contemporary strategy: Azure NetApp Recordsdata for EDA at scale<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/aireviewirush.com\/?p=27681\/#Impartial_validation_SPECstorage%C2%AE_Answer_2020_benchmark_outcomes\" title=\"Impartial validation: SPECstorage\u00ae Answer 2020 benchmark outcomes\">Impartial validation: SPECstorage\u00ae Answer 2020 benchmark outcomes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/aireviewirush.com\/?p=27681\/#Confirmed_in_manufacturing_EDA_workloads_already_operating_on_ANF\" title=\"Confirmed in manufacturing: EDA workloads already operating on ANF\">Confirmed in manufacturing: EDA workloads already operating on ANF<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/aireviewirush.com\/?p=27681\/#How_Azure_helps_EDA_groups_scale_with_confidence\" title=\"How Azure helps EDA groups scale with confidence\">How Azure helps EDA groups scale with confidence<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/aireviewirush.com\/?p=27681\/#Bringing_all_of_it_collectively\" title=\"Bringing all of it collectively\">Bringing all of it collectively<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/aireviewirush.com\/?p=27681\/#Scale_high-performance_EDA_workloads_with_Azure_NetApp_Recordsdata\" title=\"Scale high-performance EDA workloads with Azure NetApp Recordsdata\">Scale high-performance EDA workloads with Azure NetApp Recordsdata<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\" id=\"why-eda-storage-has-been-difficult-to-scale-in-the-cloud\"><span class=\"ez-toc-section\" id=\"Why_EDA_storage_has_been_tough_to_scale_within_the_cloud\"><\/span>Why EDA storage has been tough to scale within the cloud<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"wp-block-paragraph\">EDA workloads mix three traits which have historically challenged cloud storage architectures:<\/p>\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\">Extraordinarily excessive concurrency, with hundreds of jobs accessing shared file methods concurrently.  <\/li>\n<li class=\"wp-block-list-item\">Strict latency sensitivity, the place even minor delays cut back compute effectivity and lengthen runtimes.  <\/li>\n<li class=\"wp-block-list-item\">Intensive shared knowledge entry patterns, creating competition below load.  <\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">Whereas cloud compute scales simply, shared storage has typically launched variability that limits general system effectivity. As concurrency will increase, storage turns into the bottleneck, impacting regression cycles, rising software license prices, and slowing time to tape-out.<\/p>\n<p class=\"wp-block-paragraph\">For EDA groups evaluating cloud transformation, the central query has remained constant: can storage scale with compute whereas sustaining predictable efficiency?<\/p>\n<h2 class=\"wp-block-heading\" id=\"a-modern-approach-azure-netapp-files-for-eda-at-scale\"><span class=\"ez-toc-section\" id=\"A_contemporary_strategy_Azure_NetApp_Recordsdata_for_EDA_at_scale\"><\/span>A contemporary strategy: Azure NetApp Recordsdata for EDA at scale<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" id=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" target=\"_blank\" rel=\"noreferrer noopener\">Azure NetApp Recordsdata<\/a> is designed particularly to deal with this problem. Its structure aligns on to the necessities of extremely parallel, shared workloads like EDA. <\/p>\n<p class=\"wp-block-paragraph\">At its core, ANF allows impartial scaling of compute and storage, so EDA clusters can develop with out storage turning into the constraint, and extra compute nodes don&#8217;t introduce hotspots or competition on the storage layer. It natively helps concurrent metadata operations at scale, dealing with the tens of millions of small file interactions typical of EDA workflows with out degradation. And its service-level efficiency mannequin ensures that throughput and IOPS scale predictably with capability, eliminating the necessity for complicated tuning.<\/p>\n<p class=\"wp-block-paragraph\">Extra not too long ago, improvements resembling massive volumes and enormous volumes breakthrough mode have expanded the concurrency envelope even additional. These capabilities enable hundreds of parallel jobs to share a single storage setting whereas sustaining constant latency below sustained load.<\/p>\n<p class=\"wp-block-paragraph\">This delivers what cloud-based EDA methods have lengthy struggled to supply: constant, repeatable efficiency, not solely at low utilization, but additionally below full manufacturing load.<\/p>\n<h2 class=\"wp-block-heading\" id=\"independent-validation-specstorage-solution-2020-benchmark-results\"><span class=\"ez-toc-section\" id=\"Impartial_validation_SPECstorage%C2%AE_Answer_2020_benchmark_outcomes\"><\/span>Impartial validation: SPECstorage\u00ae Answer 2020 benchmark outcomes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"wp-block-paragraph\">To validate these capabilities in a real-world context, <a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" id=\"https:\/\/azure.microsoft.com\/en-us\/products\/netapp\" target=\"_blank\" rel=\"noreferrer noopener\">Azure NetApp Recordsdata<\/a> was measured utilizing the industry-standard <a href=\"https:\/\/www.spec.org\/storage2020\/results\/eda_blended\/\" id=\"https:\/\/www.spec.org\/storage2020\/results\/eda_blended\/\" target=\"_blank\" rel=\"noreferrer noopener\">SPECstorage\u00ae Answer 2020 EDA_BLENDED<\/a> benchmark. This benchmark simulates real looking EDA workflows by combining metadata-intensive frontend operations with throughput-heavy backend processing, all below strict latency necessities. <\/p>\n<figure class=\"wp-block-table is-style-stripes\">\n<table class=\"has-fixed-layout\">\n<tbody>\n<tr>\n<td><strong>The Azure NetApp Recordsdata massive quantity breakthrough mode scale configuration reached 17,280 SPECstorage\u00ae Answer 2020 EDA_BLENDED JOBS with an general response time of 0.60 milliseconds (ms).<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p class=\"wp-block-paragraph\">These outcomes show a number of necessary traits:<\/p>\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\">The power to maintain very excessive ranges of concurrent EDA workloads.  <\/li>\n<li class=\"wp-block-list-item\">Persistently low response instances below load.  <\/li>\n<li class=\"wp-block-list-item\">Linear scaling conduct as concurrency will increase.  <\/li>\n<li class=\"wp-block-list-item\">No requirement for overprovisioning.<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">\n  Traditionally, prime benchmark outcomes on this class have been related to tightly built-in on-premises methods. This validation underscores a broader shift within the {industry}: when architected accurately, <strong>cloud-based EDA infrastructure cannot solely match on-premises approaches, however in some eventualities surpass them in each scale and operational effectivity.<\/strong>\n<\/p>\n<h2 class=\"wp-block-heading\" id=\"proven-in-production-eda-workloads-already-running-on-anf\"><span class=\"ez-toc-section\" id=\"Confirmed_in_manufacturing_EDA_workloads_already_operating_on_ANF\"><\/span>Confirmed in manufacturing: EDA workloads already operating on ANF<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"wp-block-paragraph\">This efficiency just isn&#8217;t restricted to benchmarks. Organizations resembling AMD and ASML are already utilizing Azure NetApp Recordsdata to run EDA and high-performance design workloads in manufacturing environments.<\/p>\n<p class=\"wp-block-paragraph\">These firms function at the forefront of semiconductor innovation, the place infrastructure should assist each excessive scale and exact predictability. Their adoption of ANF displays a broader {industry} development: transferring EDA workloads to the cloud is not experimental, it&#8217;s turning into a strategic benefit.<\/p>\n<p class=\"wp-block-paragraph\">These clients, together with others, persistently report the identical operational advantages:<\/p>\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\">The power to extend regression concurrency with out efficiency degradation.  <\/li>\n<li class=\"wp-block-list-item\">Improved utilization of compute assets and diminished EDA software license charges.  <\/li>\n<li class=\"wp-block-list-item\">Higher predictability in design cycles, enabling extra assured scheduling of key milestones.  <\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">On this context, storage is not the limiting issue\u2014it turns into an enabler of scale.<\/p>\n<h2 class=\"wp-block-heading\" id=\"how-azure-helps-eda-teams-scale-with-confidence\"><span class=\"ez-toc-section\" id=\"How_Azure_helps_EDA_groups_scale_with_confidence\"><\/span>How Azure helps EDA groups scale with confidence<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"wp-block-paragraph\">Organizations have flexibility in how they deploy EDA environments with Azure NetApp Recordsdata, relying on workload traits and operational priorities.<\/p>\n<p class=\"wp-block-paragraph\">Some groups select a centralized mannequin constructed round a single massive quantity to maximise throughput and tightly management latency. Others undertake a multi-volume strategy to distribute workloads and scale concurrency throughout completely different job varieties. Many enterprises lengthen current on-premises environments into Azure, utilizing cloud capability to soak up peak demand with out everlasting infrastructure growth.<\/p>\n<p class=\"wp-block-paragraph\">Throughout all of those patterns, one precept stays constant: storage efficiency should scale predictably alongside compute. Azure NetApp Recordsdata supplies that basis.<\/p>\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\">Azure NetApp Recordsdata delivers the constant, excessive\u2011throughput NFS efficiency that fashionable EDA workloads demand, shrinking runtimes, accelerating tape\u2011out schedules, and giving chip designers the boldness that storage won&#8217;t ever be the bottleneck.<\/p>\n<p><cite>Srikanth Gubbala, Head of World HPC Infrastructure, Utilized Supplies<\/cite><\/p><\/blockquote>\n<h2 class=\"wp-block-heading\" id=\"bringing-it-all-together\"><span class=\"ez-toc-section\" id=\"Bringing_all_of_it_collectively\"><\/span>Bringing all of it collectively<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"wp-block-paragraph\">The evolution of cloud storage for EDA marks an necessary inflection level for the semiconductor {industry}. What was as soon as thought of a tradeoff\u2014scale versus predictability\u2014is not a constraint.<\/p>\n<p class=\"wp-block-paragraph\">With Azure NetApp Recordsdata, organizations can confidently run extremely concurrent EDA workloads within the cloud, supported by structure designed for his or her particular calls for and validated by impartial benchmarking.<\/p>\n<p class=\"wp-block-paragraph\">For groups exploring the right way to modernize their EDA infrastructure, the trail ahead is more and more clear. Cloud-based storage can now meet the necessities of even probably the most demanding design environments, whereas providing the flexibleness to scale as workloads proceed to develop.<\/p>\n<p class=\"wp-block-paragraph\">For a deeper technical exploration of the benchmark configuration and design concerns, see the companion Azure Tech Group technical weblog:<a href=\"https:\/\/techcommunity.microsoft.com\/blog\/azurestorageblog\/from-scale-to-breakthrough-azure-netapp-files-sets-a-new-cloud-benchmark-for-eda\/4520890\" target=\"_blank\" rel=\"noreferrer noopener\"> <strong>\u201cFrom scale to breakthrough: Azure NetApp Recordsdata units a brand new cloud benchmark for EDA.\u201d<\/strong><\/a><\/p>\n<p class=\"wp-block-paragraph\">For additional info, discover the <a href=\"https:\/\/learn.microsoft.com\/azure\/azure-netapp-files\/\" target=\"_blank\" rel=\"noreferrer noopener\">Azure NetApp Recordsdata documentation<\/a> or electronic mail <a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/azure-netapp-files-for-eda-workloads-from-revolution-to-breakthrough-at-scale\/mailto:askanf@microsoft.com\" target=\"_blank\" rel=\"noopener\">askanf@microsoft.com<\/a>.<\/p>\n<aside class=\"cta-block cta-block--align-left cta-block--has-image wp-block-msx-cta\" data-bi-an=\"CTA Block\">\n<div class=\"cta-block__content\">\n<div class=\"cta-block__image-container\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-1024x683.jpg\" class=\"cta-block__image\" alt=\"CLO20b_Jayesh_001\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-1024x683.jpg 1024w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-300x200.jpg 300w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-768x512.jpg 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-1536x1024.jpg 1536w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2026\/05\/CLO20b_Jayesh_001-2048x1365.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\"\/>\t\t\t<\/div>\n<div class=\"cta-block__body\">\n<h2 class=\"cta-block__headline\"><span class=\"ez-toc-section\" id=\"Scale_high-performance_EDA_workloads_with_Azure_NetApp_Recordsdata\"><\/span>Scale high-performance EDA workloads with Azure NetApp Recordsdata<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"cta-block__text\">Uncover how Azure NetApp Recordsdata delivers predictable, high-performance storage for EDA workloads, enabling large concurrency, low latency, and constant scaling in manufacturing.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/aside>\n<\/div>\n<p><script>\n\t\tfunction facebookTracking() {\n\t\t\t\/\/ If GPC or AMC Signal is enabled, do not fire Facebook Pixel\n\t\t\tif ( navigator.globalPrivacyControl || document.cookie.includes('3PAdsOptOut=1') ) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t\t!function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n\t\t\t\tn.callMethod.apply(n,arguments):n.queue.push(arguments)};if(!f._fbq)f._fbq=n;\n\t\t\t\tn.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0;\n\t\t\t\tt.src=v;t.type=\"ms-delay-type\";t.setAttribute('data-ms-type','text\/javascript');\n\t\t\t\tt.crossOrigin='anonymous';\n\t\t\t\t\t\t\t\tt.integrity='sha384-tD4cFbVGjyc37nwp7\/9yi\/nypu6T4MKXsMkb05x3ZZxceq0X7TDs2obr2FzaMgNb';\n\t\t\t\t\t\t\t\ts=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window,\n\t\t\t\tdocument,'script','https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n\t\t\tfbq('init', '1770559986549030');\n\t\t\t\t\t\tfbq('track', 'PageView');\n\t\t\t\t\t}\n\t<\/script><br \/>\n<br \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Azure NetApp Recordsdata is redefining what\u2019s potential for EDA within the cloud\u2014delivering scalable, high-performance storage that helps large concurrency, low latency, and constant manufacturing efficiency. With impartial benchmark validation and real-world adoption, organizations can now run EDA workloads at scale with out conventional storage bottlenecks. Final 12 months, we outlined how Azure NetApp Recordsdata helped [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":27683,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22],"tags":[],"class_list":["post-27681","post","type-post","status-publish","format-standard","has-post-thumbnail","category-iot"],"_links":{"self":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/27681","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=27681"}],"version-history":[{"count":1,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/27681\/revisions"}],"predecessor-version":[{"id":27682,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/27681\/revisions\/27682"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/media\/27683"}],"wp:attachment":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=27681"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=27681"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=27681"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}