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Cost-effective High Availability with Veritas Cluster Server

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Step-by-Step: Configuring Amazon EC2 for Business-Critical Applications

This e-book provides a step-by-step guide for configuring Amazon EC2 to support business-critical applications using Windows Server Failover Clustering (WSFC). It covers creating a virtual private cloud (VPC) and subnets across multiple availability zones, provisioning virtual machines, setting up Active Directory, and configuring a SQL Server failover cluster instance (FCI).

Key highlights include:

• Using AWS services like Amazon FSx and SIOS DataKeeper for a SANless cluster for SQL Server FCI
• Extending the cluster to a third node in a different AWS region for disaster recovery
• Optimizing multi-subnet cluster configurations for connectivity

Learn to build a highly available SQL Server FCI in AWS.

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    In the realm of cloud services, selecting the right technology for specific needs is crucial. That’s why this in-depth comparison of SIOS DataKeeper and Amazon FSx is designed to help you evaluate high availability options for your critical cloud applications and determine which best suits your needs.

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    Access the full white paper now to find out how you can enable superior performance, reliability, and cost savings for your cloud-based SQL Server FCI.

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  • Validating & Testing Your Kubernetes Cluster

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  • Multi-Cloud Kubernetes Operations and Observability with VMware Tanzu

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  • Quick Steps to Engineer a Bespoke, Multi-Cluster Kubernetes Platform

    Delivering Kubernetes on demand sounds simple. But it gets complicated fast. - Most solutions are pretty opinionated, and (for public clouds especially) hard or impossible for users to change. - Managing infrastructure (e.g., VMs) under Kubernetes still needs to be done (and for multi-cluster setups, it needs to be done a lot), which is slow and tends to be failure-prone. - Updating and lifecycle-managing multiple clusters is not a picnic -- on public clouds, okay, but on private clouds? You have 99 problems. What if we told you none of that was necessary? That you could -- in just a few steps: - Build a system to deliver bespoke Kubernetes clusters on demand, fast, with huge flexibility in cluster composition, and with wild freedom to explore use-cases demanding worker/control plane separation, workers that run remote from backplanes, workers that run on IoT hardware? - Make all these K8s clusters painlessly self-updating, self-scaling on private and/or public clouds, and easy to operate - And do all this in an entirely Kubernetes-native, declarative, infra-as-code way that leverages Kubernetes itself to keep multiple clusters healthy, secure, performant, and (as relevant) resource- and cost-optimized? You instantly gain speed, simplified operations, improved security, lower costs, and massive flexibility for new use cases. In this webinar, Daniel Virassamy from Team k0s explains: - What k0s and k0smotron are and how they work - Real life use cases for multi-cluster Kubernetes, and how they map to a k0s/k0smotron architecture on various (or multiple) infrastructures - Components required: k0s, k0smotron operator, k0s Autopilot, k0s CAPI operator - How to make it yours: Incredibly-simple automation recipes for making these moving parts work together -- all of which integrate with tools you're already using

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  • Accelerate Your Hybrid Cloud Adoption on Azure

    Organizations are increasingly looking to hybrid cloud as a solution for a range of business and IT challenges that range from datacenter transformation and application modernization projects, and increasing the cost efficiencies in application and infrastructure management. An effective hybrid cloud enables applications to be run across private and public clouds, all operated as a single cloud. During Microsoft Ignite 2022, Nutanix and Microsoft announced the general availability of Nutanix Cloud Clusters (NC2) on Microsoft Azure, a simpler, faster, more cost-effective path to Azure. Learn all that’s new on offer and why Nutanix hybrid cloud is so compelling to organizations. Hear from Nutanix and Microsoft Product Management on how the solution helps customers dramatically reduce the operational complexity, risks and costs of migrating, extending, or bursting applications on-premises and in the public cloud. Watch a solution demo to see just how it’s done. Presenters: Marc Trouard-Riolle, Director, Product Marketing S. Ashok, Principal Product Manager for Nutanix Cloud Clusters on Azure Dwayne Lessner, Principal Technical Marketing Engineer, Nutanix Swapna Kotagiri, Principal Product Manager, Microsoft

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  • How to achieve full situational awareness of multiple Kubernetes clusters

    Join us for an informative and engaging webinar featuring Lens, the world's most popular Kubernetes IDE. Designed to streamline the Kubernetes learning and development experience, Lens empowers developers and operators by simplifying complex tasks. In this demo-rich webinar, a member of the Lens development team will provide invaluable insights, practical tips, and best practices for effectively working with multiple Kubernetes clusters. Gain a comprehensive understanding of the following key topics: - Attain Full Situational Awareness: Unlock the power of Kubernetes by gaining complete situational awareness. Learn how to navigate and manage your clusters with ease, ensuring you have a holistic view of your environment. - Real-Time Cluster Observability: Discover how Lens provides real-time insights into your Kubernetes clusters. Explore advanced observability features that enable you to monitor and troubleshoot your applications effectively. - Streamline Performance Evaluation: Uncover the secrets of accessing and evaluating cluster performance effortlessly. Learn how Lens simplifies performance analysis, allowing you to optimize your clusters for optimal efficiency. - Multi Cluster Management: Dive into the complexities of working with multiple Kubernetes clusters. Discover practical strategies and techniques for managing and orchestrating your clusters seamlessly. Don't miss this opportunity to level up your Kubernetes expertise with Lens, the ultimate Kubernetes IDE. Register now to secure your spot and gain valuable insights from our Lens expert.

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  • Unwrapping Multi-Cluster Security: One Layer At A Time

    Just like a well-constructed burger requires carefully layering the right ingredients, securing multi-cluster deployments demands a thoughtful, layered approach. In this webinar, we'll deconstruct the multi-cluster burger, peeling back each layer to reveal security strategies for the infrastructure, applications, data, networking, observability, and overall security of components. Key Takeaways - Clarity on the different components or "layers" that make up a typical multi-cluster architecture. - Insights into adopting a layered approach to security in multi-cluster and hybrid environments. They'll learn the importance of implementing security controls and best practices at each layer. - Strategies for enforcing consistent security policies, access controls, and governance across all clusters and components in a multi-cluster setup. - Need for centralized monitoring, logging, and observability tools to gain end-to-end visibility into the security posture and health of the entire stack.

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  • Improve Big Data Performance on Google Dataproc: Best Practices

    Managing your Google cloud costs and recapturing resource waste can be challenging. The promise of autoscaling is that workloads receive exactly the cloud computational resources they require at any given time, and you only pay for the server resources you need, when you need them. However, most auto scaling features aren’t granular enough to address today’s variable workload and application needs. Without granular and automatic control for your cloud instance resources you may be paying more than you should for your workloads. Autoscaling typically works well on clusters that process many jobs as well as single-job clusters. While some applications are constant and predictable, others are bound by CPU or memory, or are “spiky” in nature. Amazon EMR, Azure HDInsight, and Google Cloud Dataproc all provide autoscaling for big data and Hadoop, but with different approaches. Estimating the right number of cluster nodes for a workload can be difficult; user-initiated cluster scaling requires manual intervention, and mistakes are often costly and disruptive. Join Pepperdata Field Engineer Kirk Lewis for this discussion about operational challenges associated with maintaining optimal big data performance in the cloud with a focus on Google Dataproc, what milestones to set, and best practices for managing a successful cloud framework.

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