Virtual Desktop Strategies
Article | July 26, 2022
Learn setting up a Docker and Kubernetes environment with the right considerations and choose the best-suited software from ten leading tools, softwares and platforms for your business needs.
The blog discusses how Kubernetes and Docker can boost software development and deployment productivity. In addition, it covers the benefits of the role of Kubernetes in orchestrating containerized applications and best practices for implementing these technologies to improve efficiency and streamline workflows. Docker and Kubernetes are both essential containerization ecosystem utilities. Kubernetes, an excellent DevOps solution, manages and automates containers' deployment and scaling, along with operating across clusters of hosts, whereas Docker is used for creating and operating containers. The blog covers tips to consider while choosing tools/platforms. It further enlists ten platforms providing Kubernetes and Docker, featuring their offerings.
1. Considerations While Setting Up a Development Environment with Kubernetes and Docker
1.1 Fluid app delivery
A platform for application development must provide development teams with high velocity. Two factors contribute to high velocity: rapid application delivery and brief development cycles. Application platforms must support build processes that start with source code. The platforms must also facilitate the repetitive deployment of applications on any remote staging instance.
1.2 Polyglot support
Consistency is the defining characteristic of an application platform. On-demand, repetitive, and reproducible builds must be supported by the platform. Extending a consistent experience across all languages and frameworks elevates the platform experience. The platform must support a native build process and the ability to develop and customize this build process.
1.3 Baked-in security
Containerized environments are secured in a significantly different manner than conventional applications. A fundamental best practice is to utilize binaries compiled with all necessary dependencies. The build procedure should also include a directive to eliminate unnecessary components for the application's operation. Setting up a zero-trust architecture between platform components that orchestrate deployments significantly improves the workloads' security posture.
1.4 Adjustable abstractions
A platform with paved paths and the flexibility to accommodate the requirements of software engineering teams has a greater chance of success. Open-source platforms score highly in this regard, particularly those with modular architectures that allow the team to swap out parts as they adjust.
2.Top Tips to Consider While Choosing Tools and Platforms for Kubernetes and Docker
Configuring Kubernetes or Docker can be complex and resource-intensive. A production-ready platform will ensure having the necessary fully automated features without the need for configuration. Security is an essential aspect of production readiness. Additionally, automation is critical, as production readiness requires that the solution manage all cluster management duties. Automated backup, recovery, and restore capabilities must be considered. Also, ensure the high availability, scalability, and self-healing of the cluster's platform.
As the cloud and software evolve, a system's hosting location may affect its efficacy. The current trend is a multi-cloud strategy. Ensure that the platform can support abstracting from cloud or data center providers and building a shared infrastructure across clouds, cloud regions, and data centers, as well as assist in configuring them if required. According to a recent study, nearly one-third of organizations are already collaborating with four or more cloud service providers. (Source: Microsoft and 451 Research)
2.3 Ease of Administration
Managing a Docker or Kubernetes cluster is complex and requires various skill sets. Kubernetes generates a lot of unprocessed data, which must be interpreted to comprehend what's happening with the cluster. Early detection and intervention are crucial to disaster prevention. Identifying a platform that eliminates the issue of analyzing raw data is essential. By incorporating automated intelligent monitoring and alerts, such solutions can provide critical status, error, event, and warning data to take appropriate action.
2.4 Assistance and Training
As the organization begins to acquire Kubernetesor Docker skills, it is essential to have a vendor that can provide 24/7 support and training to ensure a seamless transition. Incorrect implementation will add a layer of complexity to infrastructure management. Leverage automation tools that offer the support needed to use Kubernetes and Docker without the management burden.
3. 10 Tools and Platforms Providing Kubernetes and Docker
3.1 Aqua Cloud Native Security Platform:
Aqua Security provides the Aqua Cloud Native Security Platform, a comprehensive security solution designed to protect cloud-native applications and microservices. Aqua offers end-to-end security for applications operating on Docker Enterprise Edition (Community Edition), protecting the DevOps pipeline and production workloads with complete visibility and control. It provides end-to-end security across the entire application lifecycle, from development to production, for both containerized and serverless workloads. In addition, it automates prevention, detection, and response across the whole application lifecycle to secure the build, cloud infrastructure, and operating workloads, regardless of where they are deployed.
3.2 Weave Gitops Enterprise
Weave GitOps Enterprise, a full-stack, developer-centric operating model for Kubernetes, creates and contributes to several open-source projects. Its products and services enable teams to design, build, and operate their Kubernetes platform at scale. Built by the creators of Flux and Flagger, Weave GitOps allows users to deploy and manage Kubernetes clusters and applications in the public or private cloud or their own data center. Weave GitOps Enterprise helps simplify Kubernetes with fully automated continuous delivery pipelines that roll out changes from development to staging and production. Weaveworks has used Kubernetes in production for over eight years and has developed that expertise into Weave GitOps Enterprise.
3.3 Mirantis Kubernetes Engine
Mirantis provides the Mirantis Kubernetes Engine, a platform designed to help organizations deploy, manage, and scale their Kubernetes clusters. It includes features such as container orchestration, automated deployment, monitoring, and high availability, all designed to help organizations build and run their applications at scale. Mirantis Kubernetes Engine also includes a set of tools for managing the lifecycle of Kubernetes clusters, including cluster deployment, upgrades, and patching. It also has security scanning and policy enforcement features, as well as integration with other enterprise IT systems such as Active Directory and LDAP.
3.4 Portworx by Pure Storage
Portworx's deep integration into Docker gives Portworx container data services benefits directly through the Docker Swarm scheduler. Swarm service creation brings the management capability of Portworx to the Docker persistent storage layer to avoid complex tasks such as increasing the storage pool without container downtime and problems like stuck EBS drives. Portworx is also a multi-cloud-ready Kubernetes storage and administration platform designed to simplify and streamline data management in Kubernetes. The platform abstracts the complexity of data storage in Kubernetes. Additionally, it serves as a software-defined layer that aggregates Kubernetes nodes' data storage into a virtual reservoir.
Platform9 provides a powerful IDE for developers for simplified in-context views of pods, logs, events, and more. Both development and operations teams can access the information they need in an instant, secured through SSO and Kubernetes RBAC. The industry’s first SaaS-managed approach combined with a best-in-class support and customer success organization with a 99.9% consistent CSAT rating delivers production-ready K8s to organizations of any size. It provides services to deploy a cluster instantly, achieve GitOps faster, and take care of every aspect of cluster management, including remote monitoring, self-healing, automatic troubleshooting, and proactive issue resolution, around the clock.
3.6 Kubernetes Network Security
Sysdig provides Kubernetes Network Security, a solution that offers cloud security from source to run. The product provides network security for Kubernetes environments by monitoring and blocking suspicious traffic in real time. It helps organizations protect their Kubernetes clusters against advanced threats and attacks. The product and Sysdig Secure offer Kubernetes Network Monitoring to investigate suspicious traffic and connection attempts, Kubernetes-Native Microsegmentation to enable microsegmentation without breaking the application, and Automated Network Policies to save time by automating Kubernetes network policies.
3.7 Kubernetes Operations Platform for Edge
Rafay delivers a production-ready Kubernetes Operations Platform for Edge, streamlining ongoing operations for edge applications. It provides centralized multi-cluster management to deploy, manage, and upgrade all Kubernetes clusters from a single console across all edge nodes. In addition, it offers comprehensive lifecycle management, with which users can quickly and easily provision Kubernetes clusters at the edge, where cluster updates and upgrades are seamless with no downtime. Furthermore, the KMC for Edge quickly integrates with enterprise-class SSO solutions such as Okta, Ping One, and Azure AD, among others. Other features include standardized clusters and workflows, integration and automation, and centralized logging and monitoring.
3.8 Opcito Technologies
Opcito provides simplified container management with efficient provisioning, deployment, scaling, and networking. Its application containerization expertise helps containerize existing and new applications and dependencies. Opcito is well-versed in leading container orchestration platforms like Docker Swarm and Kubernetes. While it helps choose the container platform that best suits specific application needs, it also helps with the end-to-end management of containers so clients can release applications faster and focus on innovation and business. The container management and orchestration services include: building secured microservices, Enterprise-scale Container Management and Orchestration, Orchestration, and Container Monitoring.
3.9 D2iQ Kubernetes Platform (DKP)
D2iQ (DKP) enables enterprises to take advantage of all the benefits of cloud-native Kubernetes while laying the groundwork for intelligent cloud-native innovation by simplifying Kubernetes deployment and maintenance. It simplifies and automates the most difficult parts of an enterprise Kubernetes deployment across all infrastructures. DKP helps enterprises easily overcome operational barriers and set them up in minutes and hours rather than weeks and months. In addition, DKP simplifies Kubernetes management through automation using GitOps workflow, observability, application catalog, real-time cost management, and more.
Spektra, by Diamanti, a multi-cluster management solution for DevOps and production teams, provides centralized multi-cluster management, a single control plane to deliver everything needed to provision and manage the lifecycle of multiple clusters. Spektra is built to cater to business needs, from air-gapped on-prem deployments to hybrid and multi-cloud infrastructures. It also enables stretching resources across different clusters within the tenant. Furthermore, it allows you to move workloads and their associated data from one cluster to another directly from its dashboard. Spektra integrates with lightweight directory access protocols (LDAP) and Active Directory (AD) to enable user authentication and streamline resource access. In addition, it offers application migration, data mobility, and reporting.
It is evident that Kubernetes and Docker can significantly boost software development and deployment productivity. By adopting appropriate containerization platforms and leveraging Kubernetes for orchestration, organizations can streamline workflows, improve efficiency, and enhance the reliability of their applications. Furthermore, following the tips to choose the tools or platform carefully can further improve productivity.
VMware, Vsphere, Hyper-V
Article | May 2, 2023
Why Should Companies Care about Data Virtualization?
Data is everywhere. With each passing day, companies generate more data than ever before, and what exactly can they do with all this data? Is it just a matter of storing it? Or should they manage and integrate their data from the various sources? How can they store, manage, integrate and utilize their data to gain information that is of critical value to their business?
As they say, knowledge is power, but knowledge without action is useless. This is where the Denodo Platform comes in. The Denodo Platform gives companies the flexibility to evolve their data strategies, migrate to the cloud, or logically unify their data warehouses and data lakes, without affecting business. This powerful platform offers a variety of subscription options that can benefit companies immensely.
For example, companies often start out with individual projects using a Denodo Professional subscription, but in a short period of time they end up adding more and more data sources and move on to other Denodo subscriptions such as Denodo Enterprise or Denodo Enterprise Plus. The upgrade process is very easy to establish; in fact, it can be done in less than a day once the cloud marketplace is chosen (Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). In as little as six weeks companies can realize real business benefits from managing and utilizing their data effectively.
A Bridging Layer
Data virtualization has been around for quite some time now. Denodo’s founders, Angel Viña and Alberto Pan, have been involved in data virtualization from as far back as the 1990’s. If you’re not familiar with data virtualization, here is a quick summary.
Data virtualization is the cornerstone to a logical data architecture, whether it be a logical data warehouse, logical data fabric, data mesh, or even a data hub. All of these architectures are best served by our principals Combine (bring together all your data sources), Connect (into a logical single view) and Consume (through standard connectors to your favorite BI/data science tools or through our easy-to-use robust API’s).
Data virtualization is the bridge that joins multiple data sources to fuel analytics. It is also the logical data layer that effectively integrates data silos across disparate systems, manages unified data for centralized security, and delivers it to business users in real time.
Economic Benefits in Less Than 6 weeks with Data Virtualization?
In a short duration, how can companies benefit from choosing data virtualization as a data management solution?
To answer this question, below are some very interesting KPI’s discussed in the recently released Forrester study on the Total Economic Impact of Data Virtualization. For example, companies that have implemented data virtualization have seen an 83% increase in business user productivity.
Mainly this is due to the business-centric way a data virtualization platform is delivered. When you implement data virtualization, you provide business users with an easy to access democratized interface to their data needs.
The second KPI to note is a 67% reduction in development resources. With data virtualization, you connect to the data, you do not copy it. This means once it is set up, there is a significant reduction in the need for data integration engineers, as data remains in the source location and is not copied around the enterprise.
Finally, companies are reporting a 65% improvement in data access speeds above and beyond more traditional approaches such as extract, transform, and load (ETL) processes.
A Modern Solution for an Age-Old Problem
To understand how data virtualization can help elevate projects to an enterprise level, we can share a few use cases in which companies have leveraged data virtualization to solve their business problems across several different industries.
For example, in finance and banking we often see use cases in which data virtualization can be used as a unifying platform to help improve compliance and reporting. In retail, we see use cases including predictive analytics in supply chains as well as next and best actions from a unified view of the customer. There are many uses for data virtualization in a wider variety of situations, such as in healthcare and government agencies. Companies use the Denodo Platform to help data scientists understand key trends and activities, both sociologically as well as economically.
In a nutshell, if data exists in more than one source, then the Denodo Platform acts as the unifying platform that connects, combines and allows users to consume the data in a timely, cost-effective manner.
Virtual Desktop Tools, Virtual Desktop Strategies
Article | June 8, 2023
With cloud computing on the path to becoming the mother of all transformations, particularly in IT's ways of development and operations, we are once again confronted with the problem of conversion errors, this time a hundredfold higher than previous moves to dispersed computing and the web.
While the issue is evident, the remedies are not so obvious. Cloud complexity is the outcome of the fast acceleration of cloud migration and net-new innovation without consideration of the complexity this introduces in operations.
Almost all businesses are already working in a multi-cloud or hybrid-cloud environment. According to an IDC report, 93% of enterprises utilize multiple clouds. The decision could have stemmed from a desire to save money and avoid vendor lock-in, increase resilience, or businesses might have found themselves with several clouds as a result of the compounding activities of different teams. When it comes to strategic technology choices, relatively few businesses begin by asking, "How can we secure and control our technology?"
Must-Follow Methods for Multi-Cloud and Hybrid Cloud Success
Data Analysis at Any Size, from Any Source:
To proactively recognize, warn, and guide investigations, teams should be able to utilize all data throughout the cloud and on-premises.
Insights in Real-Time:
Considering the temporary nature of containerized operations and functions as a service, businesses cannot wait minutes to determine whether they are experiencing infrastructure difficulties. Only a scalable streaming architecture can ingest, analyze, and alert rapidly enough to discover and investigate problems before they have a major impact on consumers.
Analytics That Enables Teams to Act:
Because multi-cloud and hybrid-cloud strategies do not belong in a single team, businesses must be able to evaluate data inside and across teams in order to make decisions and take action swiftly.
How Can VMware Help in Solving Multi-Cloud and Hybrid-Cloud Complexity?
VMware made several announcements indicating a new strategy focused on modern applications. Their approach focuses on two VMware products: vSphere with Kubernetes and Tanzu.
Since then, much has been said about VMware's modern app approach, and several products have launched. Let's focus on VMware Tanzu.
Tanzu is a product that enables organizations to upgrade both their apps and the infrastructure that supports them. In the same way that VMware wants vRealize to be known for cloud management and automation, Tanzu wants to be known for modern business applications.
Tanzu uses Kubernetes to build and manage modern applications.
In Tanzu, there is just one development environment and one deployment process.
VMware Tanzu is compatible with both private and public cloud infrastructures.
The important point is that the Tanzu portfolio offers a great deal of flexibility in terms of where applications operate and how they are controlled. We observe an increase in demand for operating an application on any cloud, and how VMware Tanzu assists us in streamlining the multi-cloud operation for MLOps pipeline. Apart from multi-cloud operation, it is critical to monitor and alarm each component throughout the MLOps lifecycle, from Kubernetes pods and inference services to data and model performance.
Virtual Desktop Tools
Article | July 7, 2022
The early 2000s were milestone moments for the cloud. Amazon Web Services (AWS) entered the market in 2006, while Google revealed its first cloud service in 2007. Fast forward to 2020, when the pandemic boosted digital transformation efforts by around seven years (according to McKinsey), and the cloud has become a commercial necessity today. It not only facilitated the swift transition to remote work, but it also remains critical in maintaining company sustainability and creativity. Many can argue that the large-scale transition to the cloud in the 2010s was necessary to enable the digital-first experiences that remote workers and decentralized businesses need today.
Multi-cloud and hybrid cloud setups are now the norm. According to Gartner, most businesses today use a multi-cloud approach to reduce vendor lock-in or to take advantage of more flexible, best-of-breed solutions.
However, managing multi-cloud systems increases cloud complexity, and IT concerns, frequently slowing rather than accelerating innovation. According to 2022 research done by IntelligentCIO, the average multi-cloud system includes five platforms, including AWS, Microsoft Azure, Google Cloud, and IBM Red Hat, among others.
Managing Multi-Cloud Complexities Like a Pro
Your multi-cloud strategy should satisfy your company's requirements while also laying the groundwork for managing various cloud deployments. Creating a proactive plan for managing multi-cloud setups is one of the finest features that can distinguish your company. The five strategies for handling multi-cloud complexity are outlined below.
Managing Data with AI and ML
AI and machine learning can help manage enormous quantities of data in multi-cloud environments. AI simulates human decision-making and performs tasks as well as humans or even better at times. Machine learning is a type of artificial intelligence that learns from data, recognizes patterns, and makes decisions with minimum human interaction.
AI and ML to help discover the most important data, reducing big data and multi-cloud complexity. AI and machine learning enable more simplicity and better data control.
Integrated Management Structure
Keeping up with the growing number of cloud services from several providers requires a unified management structure. Multiple cloud management requires IT time, resources, and technology to juggle and correlate infrastructure alternatives.
Routinely monitor your cloud resources and service settings. It's important to manage apps, clouds, and people globally. Ensure you have the technology and infrastructure to handle several clouds.
Developing Security Strategy
Operating multiple clouds requires a security strategy and seamless integration of security capabilities. There's no single right answer since vendors have varied policies and cybersecurity methods. Storing data on many cloud deployments prevents data loss.
Handling backups and safety copies of your data are crucial. Regularly examine your multi-cloud network's security. The cyber threat environment will vary as infrastructure and software do. Multi-cloud strategies must safeguard data and applications.
Multi-cloud complexity requires skilled operators. Do you have the appropriate IT personnel to handle multi-cloud? If not, can you use managed or cloud services? These individuals or people are in charge of teaching the organization about how each cloud deployment helps the company accomplish its goals. This specialist ensures all cloud entities work properly by utilizing cloud technologies.
Traditional cloud monitoring solutions are incapable of dealing with dynamic multi-cloud setups, but automated intelligence is the best at getting to the heart of cloud performance and security concerns. To begin with, businesses require end-to-end observability in order to see the overall picture. Add automation and causal AI to this capacity, and teams can obtain the accurate answers they require to better optimize their environments, freeing them up to concentrate on increasing innovation and generating better business results.