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The way developers design, build, and run software has changed significantly with the evolution of microservices and containers. These modern architectures use new primitives that require a different set of practices than most developers, tech leads, and architects are accustomed to. With this focused guide, Bilgin Ibryam and Roland Huß from Red Hat provide common reusable elements, patterns, principles, and practices for designing and implementing cloud-native applications on Kubernetes.
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Download this whitepaper to learn the cloud-native app development journey and the eight steps to cloud-native application success.
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Containers are transforming the way we think about application architecture and the speed at which teams can deliver on business requirements. They promise application portability across hybrid cloud environments and allow developers to focus on building a great product, without the distraction of underlying infrastructure or execution details.
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An effective guide to designing, building, and deploying enterprise Java microservices with Eclipse MicroProfile.
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Learn why golden records are the key to solid data quality and why your approach to achieving the single customer view (SCV) is equally important in this whitepaper by noted SQL Server MVP Stephen Wynkoop.
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Migrating existing applications into containers provides better manageability and greater portability. This e-book outlines specific, technical recommendations and guidelines for container migration, ranging from image build procedures to production best practices. Also included are technical checklists for architecture, security, and performance.
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With the growing adoption of cloud models, many organizations are seeking ways to move to cloud-native development. New applications can be developed entirely using cloud models and services, exploiting microservices, autonomous development teams, agile and continuous deployment, and containerized and orchestrated cloud deployments. Unfortunately, completely rewriting all legacy applications is seldom feasible due to the required time and cost. This guide covers the "lift-and-shift" modernization model, a first step to cloud-native development.
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Your organization’s current—and future—digital transformation is based on your culture and technology choices. It is an evolutionary process, and the stages of change and the final result look different for each organization. This e-book examines the stages of "digital Darwinism" and helps you determine how your organization should evolve to effectively control your digital transformation—taking your technology "elephant" and teaching it to be agile, process-driven, and adaptive.
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It can be difficult to bring new solutions to your boss. Budget, security, and the task of maintaining existing systems are factors, and management is just trying to keep things up and running. What if you could highlight the benefits of how change would make life easier for you and your manager? Read this checklist to learn 5 key points to bring to your boss about developing applications and microservices on containers to increase your rate of innovation and competitiveness.
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A successful data-driven organization has to provide the right tools for its data analysts, developers, and business end-users. Increasingly, this means leveraging open-source software. But for all their benefits, popular open-source data programs also come with potentially time-consuming drawbacks. This paper will discuss how to handle these drawbacks, and get the right data tools to the right people, by leveraging popular open source tools with IBM® SPSS® Modeler software.
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Data science and AI simplified for you. The maturity of AI and machine learning (ML) use has reached a point where businesses of all sizes are using the technology as key strategic technology enablers. As an analytic and data science professional, you’re in control. You can empower people in centers of excellence (COE) and line of business (LOB) to C-level executives with scalable insights with trust and transparency. Data science competency is critical for your business to increase predictability, optimize operations and govern use of AI. To succeed, leaders of your organization must harness the power of machine and human intelligence—data, talent and tools—while extracting actionable insights faster than ever. However, you have to bring your AI and ML experimentation into production to drive results while tackling your data and talent challenges. The question is, how will you innovate and move your business forward while preparing your data and analytics practice to harness the power of AI?
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ESG recently completed testing of IBM Decision Optimization for Watson Studio, which is designed to enable organizations to accelerate the value they can extract from AI more easily. Testing examined how IBM Watson Studio with Decision Optimization collects data, organizes an analytics foundation, and analyzes insights at scale—with a focus on the ease of operationalizing AI and data science to improve trust, simplify compliance, and speed monetization.