devopsy.kinsta.cloud

Category: IT as Code

  • GigaSpaces Embraces Kubernetes Alternative for DataOps

    GigaSpaces Embraces Kubernetes Alternative for DataOps

    GigaSpaces has added a DataOps framework to its in-memory computing platform that provides an alternative to Kubernetes by employing Nomad software from HashiCorp as a scheduler.

    Yoav Einav, vice president of product for GigaSpaces, said many enterprise IT organizations aren’t in a position to adopt Kubernetes, yet they still need a means to embrace best DataOps practices to process massive amounts of data in real-time.

    Version 15.2 of the company’s namesake platform adds a GigaOps Stack module based on ElasticGrid, an alternative to Kubernetes; and Ops Manager; gsctl; and a monitoring tool. GigaSpaces already natively supports Kubernetes, but Einav said there is a significant number of IT teams that don’t have the skills required to deploy and manage Kubernetes clusters.

    DataOps has emerged as a corollary to DevOps in that it defines a set of practices for automating the provisioning of storage systems that makes data readily accessible to applications. The need to automate those processes has become more acute as organizations build and deploy applications infused with machine learning algorithms, noted Einav.

    ElasticGrid is designed to provide a more declarative alternative to achieve that goal without having to learn how to programmatically spin up Kubernetes clusters, Einav said. That approach makes ElasticGrid more accessible to the average IT administrator, he added.

    In addition to being able to support both virtual machines and containers, Einav said ElasticGrid is approximately 20% faster than Kubernetes.

    Other capabilities added to the latest release of GigaSpaces include a stream iterator optimization tool that increases throughput for read-intensive workloads by a factor of two and support for memory-level encryption and compression per property to better adhere to data privacy regulations.

    GigaSpaces is making the case for an in-memory computing platform that is optimized to run applications in near real-time. As organizations embrace a wide range of digital business applications, legacy IT infrastructure that was optimized to process applications in batch mode needs to be updated to support applications that need to process data in memory to support applications that are processing data in near real-time.

    It’s not clear to what degree organizations will be investing in new platforms during the economic downturn brought on by the COVID-19 pandemic. There is no doubt some organizations will pull back on those investments. However, a significant number of organizations are also accelerating the rate at which they are building digital business applications to drive a newly minted business continuity strategy. Recognizing the need for more resilient applications that can be accessed by customers and suppliers from anywhere, many organizations are accelerating the deadlines for delivering many of these applications in the wake of the pandemic.

    Of course, there are multiple options available when it comes to deploying an in-memory computing platform to support these applications. The challenge many organizations will encounter, however, is finding an in-memory computing platform that also addresses the inherent challenges associated with moving large amounts of data in and out of the platform on-demand.

  • Managing a New Kind of Complexity in Software-Defined Networking

    Managing a New Kind of Complexity in Software-Defined Networking

    Software-defined networking (SDN) has moved up the enterprise IT agenda in recent years. And it’s easy to see why–in theory, SDNs are far quicker and easier to control and alter than traditional networks. By using open protocols to apply controls from the network edge, SDNs enable network engineers to shape traffic from a single centralized console, rather than working with individual switches across the network.

    In turn, this makes software-defined networks far more agile than traditional networks, with opportunities for automatic load balancing, streamlined processes, on-demand provisioning of new applications and traffic flows–in short, a network that works much harder for the organization. So, it’s no surprise that organizations are embracing SDN. A 2019 Verizon study found that while just 15% of respondents said their companies had implemented SDN, 57% expected to do so within two years.

    However, deploying SDNs can introduce new challenges for network staff revolving around complexity–in particular, complexity in relation to managing network security. Let’s take a closer look at what this means.

    A Shift in Complexity

    Any organization that moves to a software-defined environment essentially moves from datacenter-focused firewalls into a model where its security policies are defined by software within its fabric. This requires a far more granular level of security policies, on a much larger scale and with far more agility, than in traditional networks. Why? Because security controls are much more diverse.

    In a traditional setup, network security policy is relatively monolithic. A set of servers is protected by a perimeter firewall, filtering so-called north-south traffic that enters the network from the outside. Traditionally, east-west traffic within the datacenter itself is not subject to any filtering, which introduces security risks–particularly in terms of enabling malicious parties to laterally explore the environment once they have compromised a single endpoint.

    By contrast, in an SDN environment, built-in firewalls are considered part of the infrastructure. It is likely that the organization will have multiple tenants, each containing a unique set of granular security policies dictating which assets can connect to which other assets within the SDN fabric. An SDN environment is likely, for example, to incorporate one contract with Cisco Application Centric Architecture (ACI), VMWare NSX distributed firewalls and so on. There is a lot of complexity to manage.

    Ultimately, the organization in question needs to identify which elements within the newly software-defined network need to connect to each other. Then, create granular security policies that enforce this, dividing the network into smaller zones to prevent lateral infiltration by malicious parties. In short, they need to introduce micro-segmentation. 

    Managing Micro-Segmentation

    There are two main challenges associated with micro-segmentation from a security point of view. First, the organization needs to define the micro-segmented zones, and second, it needs to enforce and maintain the security policies that enable that micro-segmentation.

    How do we define micro-segmentation zones? This is all about understanding the assets within your environment, which databases contain the most sensitive data and therefore need to be segmented off from each other, which assets are talking to each other and how traffic is flowing throughout the network. Crucially, all this should be contextualized in terms of business applications–in other words, you need to understand the traffic that makes business applications work. This enables you to design a micro-segmentation architecture–and the security roles to enable it–that are centered around your business-critical services. Solutions that automatically discover and map all of the traffic flows within and through a datacenter are invaluable at this point.

    Then we move onto enforcement and maintenance of those rules on an ongoing basis, and this is where a security policy management solution is truly critical. All this complexity and dynamism cannot be managed manually. Each time a new business application is introduced, or an existing one removed or amended, the security policies also need to change–and these changes could be happening on a daily or even an hourly basis in a large organization.

    Automating the Management Process

    So, given the flexibility and rapid changes that SDN enables, how should organizations approach managing and maintaining security policies across their entire enterprise network? The most effective way is with an automation solution that holistically supports the SDN environment and its security controls, alongside existing traditional on-premise firewall estates.

    It’s important to note that the SDN deployment will be subjected to the same compliance and auditing requirements as existing networks. The security management solution must be capable of providing visibility across both physical and virtual network functions so that the overall compliance status can be centrally monitored and logged for audit purposes.

    Automated security policy management solutions generate filtering policies based on enabling only the traffic required for every application in the datacenter. This is a way of approaching intent-based networking, which comes back to looking at the components of each individual application and how they are communicating with each other. Furthermore, automated security policy management solutions audit and record every single change that is made across the network, making it easier to demonstrate compliance continuously.

    With the right automation solution, IT and security teams can eliminate time-consuming, error-prone manual security processes, such as connectivity discovery and mapping, migrating and ongoing maintenance of their environments. This frees up the teams to strategically maximize the benefits of the SDN deployment, and reap its rewards of increased flexibility and enhanced network security.

  • Accelerating IoT by Switching Gears to 5G

    Accelerating IoT by Switching Gears to 5G

    Unprecedented breakthroughs in technology have often resulted in the emergence of disruptive technologies and outstanding innovations. A major development in one technology always fuels the growth and advancement of several other technology domains and industries that take advantage of it, resulting in a need to transform businesses to address new opportunities.

    The evolution and development of various generations of mobile wireless technologies—mobile phones, mobile application development, eCommerce, banking, transportation, hotels, etc.—have seen a cascading impact on the growth and advancement of various applications. We are about to witness a similar multi-dimensional growth in several industries with the advent of the 5G wireless technology. With speeds that could be at least 10 times faster than 4G networks, 5G is expected to create pathbreaking opportunities for innovation and development in a variety of domains including but not limited to education, IoT, artificial intelligence (AI) and health care.

    5G networks offer three major benefits, bigger channels, reduced latency and scalability, which allow the connection to a large number of sensors and devices. This combination will enable the government and other industries to introduce advanced digital initiatives involving AI, machine learning (ML), Industrial IoT (IIoT) and a variety of other data intensive applications.

    This article focuses on the effect of 5G networks on IoT, and the role that DevOps could play in this mix.

    Where Does 5G Fit with Regards to IoT?

    At a high level, the focus areas for the five generations of mobile network technologies can be summarized as: 1G, voice; 2G, voice and texting; 3G, voice, texting and data; 4G, everything as in 3G but much faster; and 5G is expected to be everything in 4G but extremely faster. 

    IoT devices employ a wide range of wireless technologies such as WiFi and Bluetooth, in addition to cellular technologies such as GSM, LTE, 4G and 5G. In the field of IoT, cellular technologies such as 4G, have helped a great deal with regards to device provisioning and device management. These technologies have also improved and ensured coverage, performance, reliability and security of IoT devices and applications.

    5G brings in a variety of other benefits that are not available with 4G. The speed, higher capacity and low latency of 5G will make it more ideal for simultaneously connecting a large number of devices. Further, additional enhancements made by some 5G service providers aim at expanding the frequencies for data transfer by digital cellular technologies. These features will also make 5G a major enabler in applications that use IoT where real-time performance and decision making are critical. Remote medical diagnosis and remote surgery are some examples where 5G’s ultra-low latency will help improve the virtual and augmented reality experience, making them more powerful tools in the medical industry—especially at a time of crisis like the currently prevalent COVID-19 pandemic. 

    The bandwidth reliability and resiliency offered by 5G will pave the way for several new applications for IIoT systems. For example, factors such as operator productivity in manufacturing, reduction in cost of quality management and enhanced quality through continuous monitoring and improvement, are some examples where IIoT can be made more effective with the use of 5G.

    The volume of real-time data exchanges with IIoT devices will be greatly increased with 5G. This helps with obtaining more frequent and reliable data on several KPIs in manufacturing, worker productivity, root-cause analysis, etc. Predictive maintenance, which is turning out to be a major use case for 5G and IIoT, aims at predicting failures before they occur by leveraging AI.

    The Difference that 5G Makes on IoT Data 

    Prior to the advent of 5G, technology required to offer a higher bandwidth and reliable network connectivity were quite limited and have further prevented from realizing the fullest potential of several edge devices. These devices can potentially generate and transmit large volumes of data that can be processed and analyzed by cloud-based applications. With 5G, there will be a sharp increase in the capture and use of valuable data from edge devices leading to greater insights and predictive analytics on IoT implementations.

    5G technology offers a significant increase in transmission bandwidth, theoretically up to 10 Gbps, as opposed to 4G which only offers 100 Mbps. This increase in bandwidth will result in significant growth in the volume and quality of IoT data and a proportional growth in AI applications leveraging ML and big data. The mass volumes of data created by IoT under the influence of 5G have the power to revolutionize everything, from education to healthcare to manufacturing and even autonomous vehicles.

    With 5G, there will be a surge in the volume of data generated by sensors, cameras, drones and edge IoT devices. Integration of data from different sources will lead to the development of several large data-intensive applications that help draw valuable insights regarding the domains in which they are implemented. The convergence of 5G technology, IoT and data analytics will also pave the way for innovation and several new business opportunities. IoT applications leveraging 5G technology will proliferate wearables, smart home appliances, industrial automation and also future self-driven vehicles.

    All Things Considered

    There is absolutely no doubt that IoT is already taking the world by storm. The power of IoT, when harnessed with the power of 5G technology, will certainly lead to several innovative approaches and open up new business opportunities. Overall, the speed, reliability and low-latency offered by 5G makes it ideal for connecting a large number of IoT devices.

    IIoT devices will find a great use for 5G as they can now collect and report large volumes of real-time data from manufacturing units and sensors, helping companies obtain valuable insights and take well informed decisions. The large bandwidth supported by 5G additionally provides edge devices the ability to transmit larger volumes of data more frequently to applications that can process and use the data effectively for AI and ML.

    We will definitely see the industry witness a sharp growth in IoT, AI and ML applications relatively soon, given the fast growth of 5G and the enhancements that several service providers are already making in this technology.

  • AWS Adds GUI to Snow Edge Computing Family

    AWS Adds GUI to Snow Edge Computing Family

    Amazon Web Services (AWS) this week announced it has added a graphical user interface (GUI) tool dubbed AWS OpsHub for Snow Family to provide an alternative to the command-line interface (CLI) tool it makes available to manage its family of Snow appliances deployed in on-premises environments.

    At the same time, AWS announced it is making available additional Snowball storage appliances that are 25% faster than previous generations thanks to additional memory, support for more virtual CPUs and 100GB networking.

    Finally, AWS is also making it possible to control access to Snow appliance via AWS Identity and Access Management (IAM) and the ability to automate tasks using AWS Systems Manager.

    Bill Vass, vice president of technology for AWS, said organizations can now build storage clusters that are now as large as 1.4PB using Snow appliances.

    AWS originally rolled out Snow appliances to provide an easier way to upload massive amounts of the data to a public cloud. Once an organization loads data on to an appliance, it is physically shipped to AWS. Once there, AWS personnel transfer that data on to the AWS cloud.

    However, with the rise of edge computing, AWS is now also employing Snowball appliances to drive hybrid applications in internet of things (IoT) and other environments. AWS now makes available a Greengrass framework for building hybrid cloud applications. The Snow appliance family provides a more efficient and secure alternative to the rival edge computing platform from Dell Technologies, said Vass.

    The GUI from AWS is intended to make it easier for the average IT administrator to provision Snow appliances, which previously would have been accessible only to IT professionals who had mastered the Snow CLI. AWS is even going so far as to include cartoons that demonstrate how to provision its appliances.

    While AWS clearly dominates the public cloud, it is not clear to what degree it will be able to extend that dominance to edge computing. AWS is counting on the fact that large numbers of edge computing applications will need to be tightly integrated with application workloads running on its public cloud. Providers of traditional data center platforms are also vying to become the providers of those same platforms, all of which now can be centrally managed via the cloud.

    Edge computing platforms will, of course, vary widely in size depending on the use case. However, there may come a day soon when the total number of workloads deployed at the edge exceeds the number of workloads in the cloud. The amount of compute and storage horsepower at the edge will be crucial as organizations look to deliver next-generation applications that process and analyze data real-time, especially as 5G wireless computing networks simultaneously make more network bandwidth available.

    AWS may not be the first IT vendor that comes to mind when it comes to edge computing platforms, but it is signaling its intention to be a lot more than just a provider of cloud computing services.

  • Embracing the Journey to Public Cloud

    Embracing the Journey to Public Cloud

    One of the fascinating competitions in today’s business world is the one between the digital disruptors—companies whose business vision is rooted in technology and driven by the cloud—and the so-called incumbents.

    One sector where this competition is particularly intense is financial services. In this sector, the digital-first companies typically don’t compete with banks and other financial institutions directly. Instead, they target specific functions based on a clear—and often data-driven—understanding of what customers want. This strategy has proven particularly successful with millennials. For example, until recently the norm for giving money to a friend or family member was to write a check. Now, almost two-thirds of 20- and 30-year-olds use payment apps, such as Venmo.

    In healthcare, a different pattern is emerging, one where digital-first giants either buy or innovate their way into the sector. Examples include Amazon’s billion-dollar acquisition of the online pharmacy PillPack and the new Apple Watch, which is capable of taking electrocardiograms.

    Whatever the sector, digital disruptors have one characteristic in common. They leverage the capabilities of the cloud to the maximum extent possible. A shift in power toward digital-first companies is underway, and the cloud plays a major role in helping these companies establish themselves.

    Many incumbents are just now taking their first steps on the journey to the cloud, and still working on the challenges involved in migrating their existing legacy applications to more scalable and agile public cloud environments. While they’re engaged in this process, digital insurgents are already exploiting the full potential of the cloud to deliver solutions that meet emerging customer needs associated with today’s evolving lifestyles.

    In highly regulated industries, such as financial services and healthcare, major brands will not necessarily see an immediate decline in their customer base, nor profits. Nonetheless, digital-first companies are capturing millions of customers annually, and this growth poses a considerable challenge over time. Embracing the journey to public cloud is crucial for legacy enterprises.

    The most successful cloud-based disruptors use experimental sandboxes as a critical component of development. The public cloud can drive progress toward almost any business goal involving IT—operational transformation, new market penetration, new product launches, automation.

    Digital transformation to the public cloud is also the only way companies can leverage storage scalability and the essential innovations of the open-source community. There are still lingering concerns among executives in highly regulated, data-sensitive industries concerning privacy and regulatory compliance, and these can lead to hesitation. The face is, public clouds have been in place for over two decades now, and their security is often superior to older on-premise security. Incumbents can, therefore, tap into the public cloud to ensure safety for mission critical workloads.

    To accelerate the transition to the public cloud, consider the following best practices.

    Learning to Fail Fast and Learn from Experience More Quickly

    The Silicon Valley maxim to fail fast may run counter to incumbent corporate culture, but it is the best path to success in the new digital reality. The key is being able to learn from failures quickly and adjust accordingly.

    Partnering for Progress

    A digital native approach to business is often lacking in the corporate culture of incumbents. Cloud partners that have a digital disruption in their DNA can play an important role in driving cultural renewal at every level of the organization. A reliable cloud service partnership can accelerate concepts, pilots, staffing and other mechanisms designed to deliver value quickly and securely.

    Re-Examining Compliance Processes

    Operations should be streamlined to transform enterprise IT to be well-suited for the cloud. Companies can apply artificial intelligence to supplement human intervention, and encourage cloud adoption among employees. First, building a solid foundation, then working your way up, enables businesses to make important changes that transform the enterprise into an environment that is compliant by design.

    Successfully competing with digital natives means using the cloud to find new opportunities to streamline and better comprehend customer needs and behaviors. It also means being unafraid to change time-honored business practices. At the same time, in highly regulated and data-sensitive industries, compliance and security must be prioritized. Cloud transformation has quickly become vital for companies in all sectors and represents an essential foundation for future business success.

  • VMware Makes vRealize Automation Framework Available via the Cloud

    VMware Makes vRealize Automation Framework Available via the Cloud

    VMware this week announced it has made a software-as-a-service (SaaS) edition of its vRealize automation platform generally available.

    Taruna Gandhi, senior director for product and technical marketing at VMware, said VMware vRealize Operation Cloud will make IT automation more accessible to a broader swath of organizations that might not be able to implement IT automation on their own.

    The vRealize Operations Cloud service provides all the same capabilities as the on-premises edition of vRealize with the added benefit of being integrated with other VMware Cloud services, including VMware Cloud on AWS.

    In general, Gandhi said VMware is seeing increased adoption of IT automation frameworks occurring in three phases. In the first phase, IT teams are automating internal processes primarily for their own benefit. In the second phase, IT operations teams set up self-service portals for developers. In the third phase, organizations embrace DevOps processes to provide developers with the equivalent of a cloud computing experience spanning both on-premises and public cloud computing resources.

    Most existing VMware customers are still in the first or second phases, but over time Gandhi said VMware expects organizations to adopt DevOps practices as part of a more aggressive effort to reduce costs during the current economic downturn brought on by the COVID-19 pandemic while accelerating the rate at which applications are deployed and updated.

    The VMware vRealize Operations Cloud will play a significant role in accelerating that transition because it makes IT automation available as a service rather than a platform that needs to be deployed and maintained by an internal IT department. The beta test for vRealize Operations Cloud spanned dozens of customers that put more than 130,000 virtual machines under management across 120-plus vCenters and more than 10 VMware Cloud on AWS deployments, Gandhi said, noting the next phase will to see IT organizations begin to centralize the management of multiple cloud silos that have sprung up over the last several years within most organizations.

    There are, of course, no shortage of options these days when it comes to IT automation frameworks. VMware is making a case for vRealize as a natural extension of the VMware management tools that many organizations employ to manage VMware virtual machines that are widely used across most enterprise IT environments. VMware is now extending the reach of those tools to enable IT teams to also manage containers and even rival virtual machine platforms, noted Gandhi.

    Most of those IT organizations are now struggling to manage IT environments that have become more complex at a time when the pressure to employ higher levels of automation to rein in costs is rising sharply. It remains to be seen how IT organizations will respond to that imperative as pressure to either cap or reduce IT headcount continues to mount. Of course, not every sector of the economy is experiencing the same pressure. There are high-growth segments involving, for example, educational or entertainment services. Regardless of industry, however, it’s clear IT teams are being asked to do a lot more sooner with either the same or fewer resources.

    — Mike Vizard

  • Intelligent Virtual Assistants and Their Role in Helping Brands Transform Customer Experiences

    Intelligent Virtual Assistants and Their Role in Helping Brands Transform Customer Experiences

    The relationship between computers and humans has undergone a significant paradigm shift over several decades. From the emergence of the web in 1995 to applications or apps in 2005, technology has grown by leaps and bounds as the years progressed. This growth has brought technology to the modern era, where artificial intelligence (AI) has ultimately begun to come of age.

    Engineers and scientists have invested a great deal of effort, in terms of both energy and time, over the past few decades, which has allowed AI to be adapted into nearly every technological innovation in recent years. One such innovation is the intelligent virtual assistant (IVA).

    An IVA is a conversational AI system that has the capability to emulate human interaction in order to carry out and optimize tasks—customer service, for example. Digital assistants are gaining widespread attention across the industrial landscape, owing to a plethora of technological advancements, including machine learning and deep neural networks, among others. What makes these virtual assistants intelligent is their ability to emulate three key elements that enable customer service agents to solve problems—learning, reasoning and understanding.

    How did these conversational AI technologies achieve these capabilities? To answer this question, it is essential to delve into the evolution of the IVA market.

    Evolution of IVAs

    Although the terms “chatbots” and “intelligent virtual assistant” are often used interchangeably, and despite the fact that these technologies share a core commonality—i.e., their ability to facilitate consumer-enterprise interaction—these conversational AI technologies have some distinct differences. These differences lie mainly in complexity, scope and abilities, which IVAs’ possess more of than chatbots.

    In simple words, IVAs are the result of a decade-long chatbot evolution.

    Chatbots were first introduced in the late 60s and early 70s. These technologies were programmed with rudimentary capabilities, including scripted responses based on user inputs in the form of specific phrases and keywords.

    However, with conversational flows becoming increasingly more complex over time, these chatbots began facing limitations in their scope, mainly owing to their lack of learning process and pre-programmed responses. With no memory or context retention capabilities, the bots were unable to comprehend relevant user information between sessions or even during the conversation.

    However, conversational AI evolution reached a turning point with the emergence of two novel technologies, machine learning (ML) and natural language understanding (NLU).

    NLU gives the digital assistant the ability to comprehend and interpret inputs from users in natural language, which can enable the bot to converse with a customer naturally, as a customer service agent would.

    Meanwhile, ML allows the bots to self-train based on data such as previous customer service interaction transcripts or by observing live customer-agent interactions. This helps the IVAs to cater to customer needs in a more efficient and seamless manner.

    The technological advantages provided by these technologies, therefore, facilitated the gradual evolution of simple chatbots into IVAs, which possess in-depth domain knowledge that they can leverage to understand customer preferences and intents, fulfill their needs and enhance performance over time by gaining new insights from each interaction.

    As a result of these advanced capabilities, automation and customer satisfaction are assured outcomes of IVA adoption. Thus, brands that make use of this technology can make sure that they refocus on their primary objective: ROI.

    The Gradual Shift of IVA Technology from a Personal to a Commercial Landscape

    Starting out as an ideal solution for virtual personal assistants, such as Apple’s Siri which allows users to use voice-technology to perform myriad tasks like texting, scheduling meetings or making phone calls, IVAs are now gaining prominence as crucial tools for brands to implement effective customer engagement.

    AI assistants are rapidly making their way out of personal applications and entering a more business-oriented landscape, for instance, Oracle’s Digital Assistant which is designed to be an ideal platform for sales, marketing and other industry verticals by interfacing with a vast software app portfolio.

    IVA technology is used across a range of industrial applications, for example, taking up the role of sales agents or customer service executives, which makes it a scalable and appealing option for enterprises. Businesses predominantly adopt digital assistants to drive efficiency, enhance ROI, optimize processes and help reduce workloads.

    As the industrial space grows increasingly more competitive, customer engagement has emerged as one of the most significant assets for a successful brand. In fact, studies have shown that almost 96% of customers across the globe believe that customer service is a significant factor contributing to their loyalty to a brand.

    IVAs help brands deliver positive customer experiences by covering a number of different aspects. For instance, AI assistants can quickly solve customer problems through customers’ preferred channels, ensuring speed and convenience. They can also integrate with various systems and databases of brands, thus offering a robust knowledge base to provide tailored solutions to customers. Additionally, they are able to adapt their tone and language to context, which helps them emulate human conversations. All these factors help digital assistants play the role of an efficient and helpful customer service representative.

    How Brands Are Leveraging Conversational AI Assistants to Drive Customer Engagement

    Given the strong capacity of conversational AI technology to engage actively with customers and optimize customer service, more and more companies are adopting the technology as an integral part of their customer experience activities.

    To illustrate, Volvo has plans to develop conversational assistance to guide drivers in the use of various in-car apps to deliver a more effortless connected car experience.

    Likewise, Spotify has also earned positive reviews for its AI-based technology to track user patterns and make accurate recommendations and predictions based on preferences of individual customers, in turn elevating the overall user experience.

    The IVA industry has witnessed several prolific advancements over the years. The future outlook for the technology seems positive, with even more exciting avenues as the years progress, and AI researchers investigating the prospect of enhancing the emotional intelligence of assistants. The ability to comprehend not just the user’s preferences but also their emotions would revolutionize the way customer service is delivered, thus positioning AI assistants as a pivotal component of the future customer experience landscape.

    — Saloni Walimbe

  • Splunk Adds Remote Workforce Metrics Dashboard

    Splunk Adds Remote Workforce Metrics Dashboard

    Splunk has made available a Remote Work Insights dashboard designed to help IT administrators track and identify the most relevant IT metrics for a workforce that is working from home to help combat the spread of the COVID-19 pandemic.

    Company CTO Tim Tully said the dashboard is initially configured to track virtual private network (VPN) traffic as well as access to cloud applications being made via an instance of an identity management platform from Okta.

    In addition, the dashboard tracks the level of usage of Zoom video conferencing services to help determine to what degree employees are continuing to collaborate during the pandemic.

    The level of productivity being achieved in most corporate environments is tied directly to the amount of network bandwidth being made available via a VPN, which can be deployed inconsistently on various client devices. Cloud application experiences, meanwhile, can vary widely based on the geographic region an end user is located.

    The dashboard helps IT teams to determine where there are issues they can address and where there are issues that may need to be addressed by a third-party service provider.

    Built on top of the Splunk Mobile applications, the dashboard provides a way for IT administrators to employ a tablet or smartphone to continuously monitor key remote connectivity metrics, Tully said, noting that capability means IT administrators don’t have to stay tethered to PC all day to monitor their environments.

    Over time, Splunk plans to add support for additional metrics to make it simpler for IT teams to monitor remote workforces, he added.

    Beyond simply monitoring metrics, the Remote Insights Dashboard also makes it possible to send rich notifications based on thresholds defined by the IT team. That approach allows Splunk Mobile to reduce the number of alerts being generated by the various IT platforms being monitored, said Tully. Those notifications then provide the relevant actionable intelligence IT administrators need to share with other IT professionals to remediate an issue.

    It’s not clear to what degree IT teams will work from home after the COVID-19 pandemic subsides. Many organizations may decide it’s less expensive to have IT teams work remotely at a time when many of them will be looking to, for example, reduce the amount of office space they require to reduce costs. Many organizations are about to discover that IT staffs, much like customer support personnel, don’t necessarily need to be in an office to be effective.

    Regardless of where they are located, many IT professionals are about to discover how productive they can be working remotely. That may create some boundary issues between the home and office. However, it should also go a long way toward eliminating the occasional need to go to the office at odd hours of the night, only to determine the source of a problem can only be fixed by a third-party service provider the IT professional could have contacted from home in the first place.

    — Mike Vizard

  • Five Major Mistakes to Avoid in Enterprise Multi-Cloud Strategies

    Five Major Mistakes to Avoid in Enterprise Multi-Cloud Strategies

    Employing a multi-cloud strategy is not as straightforward as it seems for enterprises due to the huge amounts of data and applications involved. Hence, mistakes are common in multi-cloud implementations. What are these mistakes and what can enterprises do to avoid them?

    The Growth of the Multi-Cloud

    The multi-cloud is now a staple in most enterprise IT infrastructures. This is a result of companies having to ramp up their data access and management processes to scale with rapid market demand.

    A study by data analytics firm, Sumo Logic, showed that multi-cloud usage in enterprises grew 50% each year for the past few years. The market is also expected to hit close to $8 billion with an excellent CAGR of 32.1% from 2017 to 2024. 

    Figure 1: The share of cloud environments in enterprises. Source: Forbes.

    This accelerated growth is justified by several factors. For starters, multi-cloud environments save businesses from locking into one vendor for cloud solutions—a common issue enterprises face when dealing with providers. The great bang for the buck ROI and exceptional performance of multi-cloud implementations are also two major contributors to its success.

    The Impacts of Deploying Multi-Cloud Strategies the Wrong Way

    Any tech trend that grows as fast as multi-cloud will come with concerning downsides. Enterprises may not have the skills and resources needed to effectively work with multi-cloud. This can impact performance, increase costs, or worse, create security vulnerabilities that can cripple unprepared organizations.

    The average company uses 16 SaaS tools at one time. While SaaS applications have built-in security, organizations are still responsible for securing their environments, especially for dependencies. A vulnerability in one application can affect the entire ecosystem without adequate recovery measures in place.

    Nominet’s multi-cloud survey respondents said they faced at least one data breach in 2019—50% more than hybrid and single-cloud users. With data breach penalties reaching millions today, enterprises cannot afford to impact their already tight margins by paying unnecessary fines. This is why multi-cloud strategies need to be approached with extra caution.

    Below are the five major mistakes to avoid in enterprise multi-cloud strategies.

    Going Multi-Cloud Without the Required Competency

    Organizations used to single cloud deployments run the risk of failure if they work with the multi-cloud without the required competency. Experience working with SaaS applications is not enough to warrant complete multi-cloud shifts. Enterprises should at least have the right technical expertise, tools and technologies in place.

    Development teams will have to revamp their mentality to work efficiently in multi-cloud environments. Other important business units also need to be reworked to accommodate this change. In other words, your company must establish a multi-cloud-focused culture, not treat it as just another new piece of technology.

    Invest in training to get team members up to speed with necessary multi-cloud skills. It can be expensive, but the ROI generated from productive employees far outweighs the cost of training. Your recruitment process should also prioritize candidates with proven experience in the field to make onboarding easier.

    Work with industry-standard tools to ease development in areas like pipeline automation, CI/CD and monitoring. This reduces the burden of QA while also giving developers peace of mind when working with the multi-cloud due to reduced deployment anxiety. Ultimately, your team should be able to build, test, deploy and monitor code in the cloud with minimal friction.

    Not Addressing Cloud Sprawl

    Unless you’re locked in, you will be dealing with various providers and technology stacks in your multi-cloud. Your environment may be overloaded with unnecessary assets and dependencies, leading to cloud sprawl, which negatively impacts budgets and performance. 

    To overcome cloud sprawl, plan your cloud infrastructure meticulously with respect to organizational goals. Only integrate core applications that fit your requirements to a tee. Nice-to-haves eat up valuable resources and can be postponed for later when your multi-cloud is mature.

    Rightsize your environment to make planning easier. Rightsizing matches resource allocation to your multi-cloud workload. It’s often automated or offered as a service by cloud providers. Enterprises can cut as much as 40% of operating costs by rightsizing their infrastructure—a huge advantage in today’s fierce markets.

    Not Accounting for Complexity

    More assets in your environment means more complexity challenges. Scalability, security and resource optimization are the three key areas where enterprises need to pay close attention. Integration will be tough as well since not all providers offer seamless connections with other tools. 

    Consider working with popular cloud applications if this is the first time your organization has worked with multi-cloud. Bespoke solutions are best implemented in stable environments and when there is a real need.

    Another factor to consider is your infrastructure’s load balancing strategy. Which resources have higher priority and which can be sacrificed when demand peaks? Automation is a must-have for load balancing, especially if your environment consists of thousands of assets—as is common in enterprise systems.

    Other complexity factors to consider in your multi-cloud strategy are:

    • Compliance.
    • Asset and data visibility.
    • Cost management.
    • Performance.
    • System design.

    Paying Too Much for Unused and Underutilized Resources

    Multi-cloud is not as cost-effective as expected if you spend too much on unused and underutilized resources. Fifty-two percent of enterprise cloud resources barely reach their optimal usage while cloud waste hit an all-time high of $14 billion last year.

    Thankfully, cost optimization is achieved as a side effect of mitigating cloud sprawl and rightsizing. Doing these two things, combined with best practices such as streamlining unused EBS volumes and snapshots, will significantly reduce your costs.

    Poor Visibility

    The number of assets in the average multi-cloud environment requires total visibility for your team to navigate through it. Without visibility, developers will have their work disrupted by intricate dependencies and foreign configurations that ruin the productivity of your organization. This can also result in security breaches should a dependency be compromised—how can your team find the root of the problem if they can’t see architecture?

    Map out every asset and dependency with mapping tools. By improving visibility, you bolster the security of your multi-cloud while allowing developers to deploy code with confidence when they have a full overview of your ecosystem. Integrate mapping insights with your organization’s technical documentation to further improve results.

    Bolster Your Multi-Cloud Today

    The easiest way to generate optimal performance from your multi-cloud is to avoid common pitfalls as early as possible. With the tips mentioned above, you can improve scalability, cost and performance without succumbing to complexity challenges.

    — Soumik Sarkar

  • BigPanda Provides Free 90-Day Access to IT Ops Platform

    BigPanda Provides Free 90-Day Access to IT Ops Platform

    BigPanda, a provider of an IT ops platform infused with machine learning algorithms, this week announced it will make available for free for 90 days an instance of its software available to IT professionals working from home to help combat the COVID-19 pandemic.

    Mohan Kompella, vice president of product marketing for BigPanda, said the IT Ops From Home program is intended to make it easier for IT teams to manage IT operations using a platform that was designed from the ground up to remotely manage highly distributed computing environments.

    BigPanda is also providing free 90-day support along with free training via a virtual classroom. Organizations that opt to participate in the program are under no obligation to renew.

    Kompella said the BigPanda platform is designed to correlate events in a way that automates IT incident management. It provides built-in integrations with software and services from Amazon Web Services (AWS), AppDynamics, Cloudwatch, Splunk, Azure, Jira and Slack.

    It may take a little effort for IT organizations to implement a new platform for managing IT in the midst of a pandemic, he conceded, but also noted there are organizations that are finding their existing IT platforms are not designed to enable IT teams to support an entire workforce that is now accessing distributed applications from home. The BigPanda platform makes it possible to unify the management of all the silos that now make up an enterprise IT environment—in fact, Kompella said managing IT environments remotely may soon become the “new normal.” In much the same way many organizations now employ remote workers to provide customer service, Kompella noted it’s possible many IT support staff will now simply work from home. That approach would, among other things, reduce the cost of office space for IT personnel.

    As organizations struggle to implement business continuity plans there’s no doubt many legacy IT operations platforms are being stretched to their limit. Many of the plans organizations had to slowly transition to a modern IT management platform now need to be accelerated, said Kompella.

    There’s no doubt the COVID-10 pandemic will have a profound impact on the way IT is managed. Many processes will be forever altered, especially as organizations embrace DevOps processes to manage application workloads that are increasingly likely to be deployed in the cloud, as part of a concerted effort to make business processes more resilient in the face of global disruptions such as the COVID-19 pandemic. After all, no one is quite sure when the current crisis might subside or return. There may even additional pandemics to contend with in the future.

    Whatever the motivation, an evolutionary approach to modernizing IT ops will be moving forward at a faster pace among organizations that have the wherewithal to stave off the current economic downturn caused by the pandemic. In fact, there may even be a high correlation between organizations that fail to modernize those processes and those organizations that don’t re-emerge stronger in 2021.

    — Mike Vizard