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Organizations considering to migrate from mainframe to Azure must select the right architectural pattern during migration. When migrating to cloud, it is important to understand how applications in mainframes map to their equivalent architecture on Azure. This whitepaper is an aimed at providing customers with references and aid their migration journey from the rich experience that Microsoft and Infosys have gathered from their past mainframe migration experiences.
Azure SQL Database Edge: A data engine optimized for IoT workloads on edge devices
Organisations rely on data science to support innovation, competitive advantage and efficiency, and the data scientist role is vital to this practice. But to put data science into production at scale, you need skills and methods that go beyond the scope of the data scientist. The role of data engineer has emerged to ensure that predictive models are ready for production. The technological requirements of data science have also evolved. The cloud data warehouse has been developed to address the scalability, availability and budgetary issues that arise as the volume of data dramatically increases. Read The scientist, the engineer and the warehouse white paper to learn what it takes to put cloud analytics into practice.Understand the distinct roles of the data scientist vs data engineer.Find out how these roles work together with a cloud data warehouse.Learn how Azure SQL Data Warehouse is uniquely suited to address the need for governance, manageability and elasticity at any scale.See how SQL Data Warehouse fits into an effective architecture for cloud analytics.
Becoming data-driven is now a matter of urgency for organizations to stay competitive with challenges ranging from customer engagement to risk management, cybersecurity, and fraud detection, and to operational excellence. The cloud is playing a growing role in helping enterprises benefit from data as it bypasses many of the cost and organizational bottlenecks associated with implementing new capacity in on-premises data centers. The results are borne out with big data. Big data workloads are moving to the cloud. According to Ovum, 27.5% of big data workloads are running in the cloud, with the rate currently growing by over 20% annually. Ovum predicts that the cloud will account for over half of new big data workloads by 2019. For most organizations, hybrid deployment spanning the data center and cloud will become the new reality. While few are likely to migrate 100% of their data, applications, or platforms to the cloud, the cloud will play a big role in analytics and business agility – going beyond test/development to quickly on-ramping new workloads. But not all cloud services are alike. How can enterprises choose the right cloud infrastructure service (IaaS) provider and the right big data platform to run in that cloud environment? These should be considered separate decisions.
How the .NET Engineering Services team streamlined developer collaboration to accelerate open-source innovation on GitHub through shared tooling and standardizing continuous integration across a wide, complex project spanning many git repositories. Learn about the challenges the team faced, their journey and the results they have seen in the adoption of DevOps practices.
How the Microsoft One Engineering System (1ES) team guides internal teams at Microsoft to become high-performing through tooling and adopting a DevOps-driven culture of continuous learning. Learn about the challenges the team faced, their journey and their approach to guiding engineering teams inside Microsoft.
How the Manageability Platforms team at Microsoft transitioned from traditional, centralized IT monitoring to enabling a distributed, self-service model of cloud operations. See how the move to the cloud helped the team evolve it’s operation model and reshape the relationship with the development teams they work with. Learn about the challenges the team faced, their journey and the results they have seen in the adoption of DevOps practices.
High performing teams deliver better products, faster and to happier customers. But helping teams become high performing may require a deep culture shift and the adoption of a growth mindset. As many leaders know, driving organizational culture change can be very hard, especially in a large enterprise that has existed for a long time. The One Engineering System (1ES) team at Microsoft is focused on that challenge – helping internal teams across Microsoft become high performing through culture change. This whitepaper holds some of their learnings and approach on how to lead teams into evolving their practices and mindset through five steps that can be applied to any team.
Solidify your understanding of what blockchain tokens are and how to use them to solve your real-world business challenges. With practical applications ranging from streamlining supply chains to managing retail loyalty points programs, tokenization has enormous potential to simplify and accelerate complex business processes—while also making them more secure. Get this tokenization white paper to learn about: Core concepts and definitions of tokens. Significance of tokens and potential business applications. Real-world use cases of tokens.
Learn how to assess and migrate your A8 - A11 VM workloads to our new, more powerful high-performance computing (HPC) clusters such as H, D, E, and F for better performance with reduced cost. Note that the A8 - A11 VMs will be decommissioned in October 2020. Use this document as your starting point to begin planning for your migration. This white paper was written by KR Kandavel.