Hysolate Unveils Isolation Platform 2.0 for Securing Endpoints from Cyber Threats

Server Hypervisors

Article | May 18, 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 Strategies, Server Hypervisors

Article | April 27, 2023

Network virtualization (NV) is the act of combining a network's physical hardware into a single virtual network. This is often accomplished by running several virtual guest computers in software containers on a single physical host system. Network virtualization is the gold standard for networking, and it is being adopted by enterprises of all kinds globally. By integrating their existing network gear into a single virtual network, enterprises can save operating expenses, automate network and security processes, and set the stage for future growth. Businesses can use virtualization to imitate many types of traditional hardware, including servers, storage devices, and network resources. Three Forces Driving Network Virtualization Demand for enterprise networks keeps rising, driven by higher end-user demands and the proliferation of devices and business software. Through network virtualization, IT businesses are gaining the ability to respond to evolving needs and match their networking capabilities with their virtualized storage and computing resources. According to a recent SDxCentral survey, 88% of respondents believe that adopting a network virtualization solution is "mission critical" and that it is necessary to assist IT in addressing the immediate requirements of flexibility, scalability, and cost savings (both OpEx and CapEx) in the data center. Speed Today, consider any business as an example. Everything depends on IT's capacity to assist business operations. When a company wants to 'surprise' its clients with a new app, launch a competitive offer, or pursue a fresh route to market, it requires immediate IT assistance. That implies IT must move considerably more swiftly, and networks must evolve at the rapid speed of a digitally enabled organization. Security According to a PricewaterhouseCoopers survey, the average organization experiences two successful cyberattacks every week. Perimeter security is just insufficient to stem the flood, and network experts are called upon to provide a better solution. The new data center security approach will: Be software-based Use the micro-segmentation principle Adopt a Zero Trust (ZT) paradigm In an ideal world, there would be no difference between trustworthy and untrusted networks or sectors, but a ZT model necessitates a network virtualization technology that allows micro-segmentation. Flexibility Thanks to the emergence of server virtualization, applications are no longer linked to a specific physical server in a single location. Applications can now be replicated to eliminate a data center for disaster recovery, moved through one corporate data center to another, or slipped into a hybrid cloud environment. The problem is that network setup is hardware-dependent, and hardwired networking connections restrict them. Because networking services vary significantly from one data center to the next, as an in-house data center differs from a cloud, you must perform extensive personalization to make your applications work in different network environments—a significant barrier to app mobility and another compelling reason to utilize network virtualization. Closing Lines Network virtualization is indeed the future technology. These network virtualization platform characteristics benefit more companies as CIOs get more involved in organizational processes. As consumer demand for real-time solutions develops, businesses will be forced to explore network virtualization as the best way to take their networks to another level.

Server Virtualization

Article | May 17, 2023

Introduction 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. Skillset Management 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. Closing Lines 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.

Article | April 14, 2021

Danny Cobb, fellow and vice president of engineering for Dell Technologies’ telco systems business, remembers his company cruising into early 2020: Kicking off a new fiscal year with its operating plan in place, supply chain nailed down and factories humming; people coming into the office each day to the usual routine of looking for parking spots and taking laptops down to the cafeteria. Then came March, and the first wave of the Covid-19 pandemic hit U.S. shores. In the course of one weekend, Dell pivoted to having more than 90% of its workforce working from home. That meant a dramatic shift in its network needs and operations – one that was only able to be accomplished so quickly because of virtualized infrastructure.