{"id":6878,"date":"2019-02-28T10:43:39","date_gmt":"2019-02-28T03:43:39","guid":{"rendered":"https:\/\/news.adeptasiaconsulting.com\/?p=6878"},"modified":"2019-02-20T10:28:06","modified_gmt":"2019-02-20T03:28:06","slug":"microsoft-azure-sets-its-sights-on-more-analytics-workloads","status":"publish","type":"post","link":"https:\/\/news.adeptasiaconsulting.com\/microsoft-azure-sets-its-sights-on-more-analytics-workloads\/","title":{"rendered":"Microsoft Azure sets its sights on more analytics workloads"},"content":{"rendered":"
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Enterprises now amass huge amounts of data, both from their own tools and applications, as well as from the SaaS applications they use. For a long time, that data was basically exhaust. Maybe it was stored for a while to fulfill some legal requirements, but then it was discarded. Now, data is what drives machine learning models, and the more data you have, the better. It\u2019s maybe no surprise, then, that the big cloud vendors started investing in data warehouses and lakes early on. But that\u2019s just a first step. After that, you also need the analytics tools to make all of this data useful.<\/p>\n

Today, it\u2019s Microsoft <\/a> turn to shine the spotlight<\/a> on its data analytics services. The actual news<\/a> here is pretty straightforward. Two of these are services that are moving into general availability: the second generation of Azure Data Lake Storage for big data analytics workloads and Azure Data Explorer<\/a>, a managed service that makes easier ad-hoc analysis of massive data volumes. Microsoft is also previewing a new feature in Azure <\/a> Data Factory, its graphical no-code service for building data transformation. Data Factory now features the ability to map data flows.<\/p>\n

Those individual news pieces are interesting if you are a user or are considering Azure for your big data workloads, but what\u2019s maybe more important here is that Microsoft is trying to offer a comprehensive set of tools for managing and storing this data \u2014 and then using it for building analytics and AI services.<\/p>\n

\"\"<\/p>\n

(Photo credit:Josh Edelson\/AFP\/Getty Images)<\/p>\n<\/div>\n

\u201cAI is a top priority for every company around the globe,\u201d Julia White, <\/a> Microsoft\u2019s corporate VP for Azure, told me. \u201cAnd as we are working with our customers on AI, it becomes clear that their analytics often aren\u2019t good enough for building an AI platform.\u201d These companies are generating plenty of data, which then has to be pulled into analytics systems. She stressed that she couldn\u2019t remember a customer conversation in recent months that didn\u2019t focus on AI. \u201cThere is urgency to get to the AI dream,\u201d White said, but the growth and variety of data presents a major challenge for many enterprises. \u201cThey thought this was a technology that was separate from their core systems. Now it\u2019s expected for both customer-facing and line-of-business applications.\u201d<\/p>\n

Data Lake Storage helps with managing this variety of data since it can handle both structured and unstructured data (and is optimized for the Spark and Hadoop analytics engines). The service can ingest any kind of data \u2014 yet Microsoft still promises that it will be very fast. \u201cThe world of analytics tended to be defined by having to decide upfront and then building rigid structures around it to get the performance you wanted,\u201d explained White. Data Lake Storage, on the other hand, wants to offer the best of both worlds.<\/p>\n

Likewise, White argued that while many enterprises used to keep these services on their on-premises servers, many of them are still appliance-based. But she believes<\/a> the cloud has now reached the point where the price\/performance calculations are in its favor. It took a while to get to this point, though, and to convince enterprises. White noted that for the longest time, enterprises that looked at their analytics projects thought $300 million projects took forever, tied up lots of people and were frankly a bit scary. \u201cBut also, what we had to offer in the cloud hasn\u2019t been amazing until some of the recent work,\u201d she said. \u201cWe\u2019ve been on a journey \u2014 as well as the other cloud vendors \u2014 and the price performance is now compelling.\u201d And it sure helps that if enterprises want to meet their AI goals, they\u2019ll now have to tackle these workloads, too.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Enterprises now amass huge amounts of data, both from their own tools and applications, as well as from the SaaS applications they use. For a long time, that data was basically exhaust. Maybe it was stored for a while to fulfill some legal requirements, but then it was discarded. Now, data is what drives machine […]<\/p>\n","protected":false},"author":4,"featured_media":6877,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false},"categories":[6],"tags":[476,4635,127,644,1244,1903,973,4636,4332,4637,324,443,1865],"acf":[],"yoast_head":"\nMicrosoft Azure sets its sights on more analytics workloads - Adept Asia Consulting<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/news.adeptasiaconsulting.com\/microsoft-azure-sets-its-sights-on-more-analytics-workloads\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Microsoft Azure sets its sights on more analytics workloads - Adept Asia Consulting\" \/>\n<meta property=\"og:description\" content=\"Enterprises now amass huge amounts of data, both from their own tools and applications, as well as from the SaaS applications they use. 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