Highlights of the Azure stack include Azure Synapse Dedicated, Azure Synapse SQL Pool, Azure Data Factory, Azure Stream Analytics, Azure Synapse Spark, Azure Synapse Serverless, Power BI Professional, Azure Machine Learning, Azure Active Directory P1, and Azure Purview. AWS was 19% higher, while Google and Snowflake were more than double the cost. It had a three-year cost of $3M to purchase the analytics stack for a “medium-size” organization. When almost every additional demand of performance, scale, or analytics can only be met by adding new resources, it gets expensive.īased on our approach described in the next section, and using the assumptions listed in each section mimicking a medium enterprise application, Azure was the lowest-cost platform. Some architectures look integrated but, in reality, may be more complex and more expensive. We have learned that the cloud analytic framework selected for an enterprise and an enterprise project matters in terms of cost.īy looking at the problem from a cost perspective, we’ve learned to be wary of architectures that decentralize and decouple every component by business domain, which enables flexibility in design, but blows up the matrix of enterprise management needs. We decided to take four leading platforms for machine learning under analysis. Greater complexity will lead to more technical debt and administrative burden as organizations cobble together and maintain the flow of data between point solutions. As more components are added, and more integration points among those components arise, complexity will increase substantially. A key differentiator among platforms is the overarching management, deployment, governance, billing, and security of those services, which can reduce complexity in administration and scaling data pipelines. The chosen platform should bring a multitude of data services onto a single, cohesive space. They also need a selection that allows a worry-less experience with the architecture and its components. They need a platform designed to address multifaceted needs by offering multifunction data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. Contact us Looking for the best way to get in touch with us?.Careers Explore career opportunities to join GigaOm.Press Room Stay up to date with the latest company announcements.Partners Explore opportunities to partner with GigaOm.Why GigaOm Learn why GigaOm is right for you and your organization.Vendor Marketing Content Review Get the word out while maintaining the GigaOm brand.GigaOm Research FAQs Learn about participating in GigaOm research.Case Studies Learn about participating in GigaOm research.Research Calendar In-depth reports on this year’s topics and technology needs.Analyst Videos Explore our video library of analyst appearances.Blog Read GigaOm blogs for quick insight into your industry.Value Engineering Help prospective customers to adapt their workflows to new technology.GigaBrief Incisive roadmaps that move the needle with IT technology buyers.Research Subscription Gain first-mover advantage with insights from our research community.Advisory Services Build your strategy with Fortune 500 CIO and CTO decision makers.Business & Technology Impact Earn confidence with real-world field reports showing your solution in action.Key Criteria In-depth decision-making frameworks that enlighten and guide IT acquisitions.Radars Prove your mettle with competitive analysis that spotlights your solutions.TCO & Benchmarks Shorten your sales cycle with rigorous, test-driven reports that quantify value.Security & Risk Learn key criteria for evaluating Security & Risk.People, Processes, & Applications Learn key criteria for evaluating Applications.Network & Edge Learn key criteria for evaluating Edge solutions.DevOps Learn key criteria for evaluating DevOps solutions.Data, Analytics, & AI Learn key criteria for evaluating Data Infrastructure. Cloud, Infrastructure & Management Learn key criteria for moving and operating in the cloud.
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