A global survey of 1,350 business and IT executives conducted by Jigsaw Research on behalf of NTT Ltd., a provider of IT services, suggests organizations are a lot more agile in the wake of the COVID-19 pandemic, but there’s still a significant gap in terms of prioritizing agility.

A full 89% of business and IT leaders agree COVID-19 has caused significant changes to their operating processes, with 87% also agreeing it has accelerated their digital transformation strategy. A third of respondents (33%) also noted they pivoted their technology focus to take advantage of a market opportunity or scale their operations.

However, well over a third of operational executives identified speed and agility as a core component of technology strategy, while only just over half of IT executives agreed (53%).

Overall, the survey found there is a consensus IT remains a key enabler of business efficiency. A total of 91.4% respondents agreed that enabling business efficiency is a key driver for their technology strategy, with 92% of IT respondents saying their overall technology strategy is aligned either fully or partially with the organization’s business strategy needs.

However, there’s a significant divide in terms of the importance of IT’s role in driving business efficiency. A technology strategy that drives business efficiency opportunities is seen as being most crucial by 70% of operations teams, yet only 48% of IT teams said the same.

Sanjay Lakhani, a principal consultant for NTT and head of the IT service management (ITSM) practice for the Americas region said that while the alignment between business operations and IT is not as close as it could be, a lot of improvement has been made. Prior to the COVID-19 pandemic, the divide between IT and the rest of the business was generally wider, noted Lakhani.

Now that more organizations are realizing how dependent they are on software, there’s a lot more focus on application development with business units, he added. There’s also more pressure on IT operations teams, which are being asked to keep pace with the rate at which digital business transformation initiatives have accelerated, added Lakhani.

Unfortunately, the survey also highlighted an emerging technology gap. The survey found 92% of business and IT leaders agreed emerging technology is crucial for their technology strategy, but only two-fifths (41%) believed they have the technology to meet their organization’s immediate objectives. Those result suggest a significant transition is underway, both in terms of how software is constructed and the type of IT infrastructure required.

In the meantime, it remains to be seen to what degree the COVID-19 pandemic has fundamentally altered the DNA of IT operations teams, but it’s apparent enterprise IT will never be the same. Most IT teams have embraced, to varying degrees, agile application development methodologies along with DevOps best practices. The challenge now is aligning the operational cadence IT teams follow with that of the rest of the business in the wake of the pandemic.

Cloud computing skills are in high demand among organizations that are looking to operate and maintain thei organization’s cloud solutions. The pandemic and subsequent ‘new normal’ of remote work are furthering demands for these skills. Many are turning to online learning platforms to up their game and acquire the cloud computing skills most likely to help them stand out. And whether you are looking to acquire those skills for work or for play, this collection of Pluralsight cybersecurity courses will help you learn the ropes so you can pilot some of the most widely used tools in no time!

With this in mind, the editors at Solutions Review have compiled this list of top-rated Pluralsight cloud computing courses to consider taking. This platform is perfect for those looking to take multiple courses or acquire skills in multiple different areas, or for those who want the most in-depth experience possible through access to entire course libraries or learning paths. In sum, Pluralsight offers more than 7,000 expert-led video courses.

Note: Our editors assembled this directory by listing top-rated Pluralsight cloud computing courses in a number of different cloud coverage areas, including Amazon Web Services, Microsoft Azure, Google Cloud Platform, cloud migration and transformation, cloud security, and cloud management.

Architecting for Security on AWS
“AWS security architects need to understand how to build security into every AWS deployment at every level. In this course, Architecting for Security on AWS, you’ll learn how to secure your data and your AWS services and resources at multiple levels using a defense-in-depth approach. First, you’ll learn how to protect your AWS credentials using identity and access management. Next, you’ll see how to capture and analyze logs using CloudTrail, CloudWatch, and Athena. Finally, you’ll learn how to implement network and instance security, encrypt data at rest and in-transit, and set up data backup, replication, and recovery. After finishing this course, you’ll be ready to granularly control access to your AWS resources.”

Cloud Computing: The Big Picture
Description: “You hear a lot about cloud computing these days. Ever wonder what that means, and where it fits into your daily job? That’s what this course is designed to teach you. You’ll explore applications in the cloud (commonly called Software as a Service, or SaaS), an overview of cloud platforms, including Amazon Web Services and Microsoft Azure, as well as private clouds (bringing cloud technology on-premise). By the time you’re done, you’ll know what cloud computing is all about and be ready to start exploring specific implementations.”

Cloud-Native Architecture: The Big Picture
Description: “You’re asked to build more changeable, available, scalable, and secure software. While cloud platforms make some of those characteristics easier to achieve, it’s more about how you build software, not where you run it. In this course, Cloud-native Architecture: The Big Picture, you’ll learn what it means to be cloud-native.”

Cloud Security: Introduction to Certified Cloud Security Professional (CCSP®)
“Many organizations may make the mistake of skipping architecture and going straight to design. This can be avoided by selecting a cloud service provider that fits with your business requirements. In this course, Cloud Security: Introduction to Certified Cloud Security Professional (CCSP), you will learn foundational knowledge of how to translate business requirements into selection criteria for a cloud service provider. First, you will learn how to identify which cloud architecture best meets your business requirements. Next, you will discover how to use security architecture to develop a secure cloud consumption strategy. Finally, you will explore how to select a cloud service provider and meet regulatory requirements. When you’re finished with this course, you will have the skills and knowledge needed to select and consume cloud services securely.”

Foundations for Cloud Architecture
Description: “First, you’ll explore the differences between public, private, and hybrid cloud computing, the business requirements they fill, and their architectural caveats. Next, you’ll learn about the practical variations in public cloud service offerings. Finally, you’ll take a look into how you and your business can benefit from advanced cloud services.”

Fundamental Cloud Concepts for AWS
“AWS certifications add value for both technical and non-technical resources in today’s digital world. In this course, Fundamental Cloud Concepts for AWS, you will walk through the fundamental cloud concepts which explains one of the four major areas that are covered by the AWS Certified Cloud Practitioner Exam. First, you will review the concept of cloud computing and the value it provides to organizations. Next, you will learn the organization of AWS global infrastructure. Next, you will discover the economics of cloud computing. Finally, you will review the tools and services that AWS provides to support infrastructure running on their platform. When you are finished with this course, you will have a clear understanding of the cloud and the fundamentals needed for a portion of the AWS Certified Cloud Practitioner exam.”

Google Cloud Functions Fundamentals
“Developers want to build serverless microservices so they can create new content, reduce maintenance, scale easily, and deliver new features to users faster. In this course, Google Cloud Functions Fundamentals, you will expand your serverless skills to create high-quality Microservices that will enhance the experience of your app or website. First, you will learn to build unique functions that interact with other Google Cloud services such as Cloud Vision and Cloud Datastore. Second, you will discover advanced concepts such as the function’s file system, idempotent function design, and working through memory and timeout issues. Finally, you will explore how to properly log errors and set up monitoring for your functions so you can continue to monitor them once they are in production. When you’re finished with this course, you will have a high-level understanding of Google Cloud Functions that will allow you to create unique experiences for your customers.”

Google Cloud Platform Fundamentals
“Over the last few years, the cloud has proven itself as a successful enabler for organizations of all sizes to improve agility, scale, reliability, and spend management. It has also made available capabilities that have greatly accelerated the adoption of a wide variety of new types of applications – from big data to Internet of Things. This rapidly expanding set of capabilities can be difficult for anyone to keep up with, and that challenge is further compounded when attempting to map capabilities across different public cloud providers. This course, Google Cloud Platform Fundamentals, provides you with an extensive overview of Google Cloud Platform. While the Google Cloud Platform is a more recent offering than some of its competitors, it draws on years of experience running Google’s massive internal infrastructure and exposes a streamlined set of solution-focused capabilities to help you build great systems.”

Microsoft Azure Cloud Concepts
“Over 90% of the Fortune 500 companies use at least one Microsoft cloud service. In this day and age, it’s important for any IT or Business Professional to know what Microsoft offers from a cloud computing perspective. In this course, Microsoft Azure Cloud Concepts, you will focus on the basics of cloud computing by defining at a more general level what the cloud is, why it’s useful, and examine how Microsoft fits in each type of cloud offering. First, you will learn about what the cloud is and why it is important. Next, you will discover the different types of cloud computing. Finally, you will explore the different cloud computing deployment models. When you are finished with this course, you will gain an understanding of why the cloud is so important to today’s businesses and know the differences between, public, private, and community clouds.”

Microsoft Azure: The Big Picture
“Microsoft Azure is a huge collection of services for building applications in the cloud and can often be overwhelming. In this course, Microsoft Azure: The Big Picture, you will learn foundational knowledge to begin planning solutions using Microsoft Azure. First, you will learn about cloud computing and the different ways to run your application code. Next, you will discover the data storage, processing, and analysis capabilities in Auzre. Finally, you will explore how to create networks; integrate, manage, and secure your applications; and develop for Azure. When you’re finished with this course, you will have the skills and knowledge of Microsoft Azure needed to begin working on your cloud solutions.”

Patterns of Cloud Integration
Description: “This course looks at how developers and architectures can use remote procedures calls, asynchronous messaging, shared databases, and file transfers in cloud scenarios. We discuss pitfalls and benefits of each pattern and how the cloud introduces new design aspects. We look at a wide range of technologies from AWS, Microsoft, Salesforce.com, Mulesoft, Informatica, and more.”

CircleCI today previewed the ability to deploy applications on platforms based on Arm processors in a forthcoming release of its namesake continuous integration/continuous delivery (CI/CD) platform.

Tom Trahan, vice president of business development for CircleCI, said that Arm processors are rapidly being embraced by DevOps teams seeking to take advantage of the price/performance advantages they currently have over conventional x86 processors. Amazon Web Services (AWS), for example, makes available an AWS Graviton2 service based on 64-bit Arm Neoverse processors that it claims provides a 40% advantage in overall price/performance.

The Arm community, meanwhile, has adopted many of the libraries employed on x86 platforms, Trahan noted, which is making it simpler to automate the deployment of applications on Arm-based platforms using a CI/CD platform.

It’s not clear how quickly developers are building applications for Arm processors. Historically, Arm processors can be found in a wide range of embedded systems typically deployed at the network edge. However, cloud services based on Arm processors are also becoming more widely available, and it’s only a matter of time before more Arm-based platforms are deployed in on-premises IT environments.

Arm itself is also in the process of being acquired by Nvidia, which envisions running machine learning algorithms, and other classed of advanced workloads, on both Arm processors and graphics processing units (GPUs). Many of the early efforts to build processors optimized for running AI workloads at the network edge are based on Arm processors.

The addition of Arm platforms to any IT environment will naturally create some management challenges for DevOps teams. In some cases, developers may even decide to build applications using, for example, the latest generation of machines from Apple, that are based on Arm processors, to streamline the application development and deployment process. In other cases, however, applications that are intended to be deployed on Arm processors might still be built on x86 systems.

Regardless of approach, the arrival of more Arm-based platforms in the enterprise doesn’t mean other classes of processors are going to fade away. Rather, from a processor perspective, IT environments are about to become a lot more diverse as more workloads are deployed on a wider range of processors. It may be quite a while before Arm-based platforms usurp x86-based systems that still dominate the IT landscape. However, during any turbulent economic time, the number of organizations inclined to reduce the total cost of IT by embracing new platforms always increases.

Fortunately, as more IT infrastructure is managed as code, the easier it becomes to switch platforms. There may even come a day when the decisions about which platform to employ and when is completely automated by machine learning algorithms trained to maximize IT infrastructure efficiency to the fullest extent possible.

In the meantime, DevOps teams should expect, to one degree or another, to revisit their workflows processes in the months ahead. Not only are more workloads being pushed out to edge computing platforms where Arm processors are commonly found, those Arm processors will soon find their way into the rest of the extended enterprise.

The last couple of years have been unlike any others. Digital transformation – be it through the establishment of remote working or the creation of new e-commerce channels – has taken place at lightning speed.

At this time of rapid change, IT teams have operated under extreme conditions. They’ve had to deal with reduced teams and tech budgets, fast-changing organisational priorities, and increased threats in terms of business continuity and information security.

As 2021 progresses, what are the elements that would help CIOs and their teams to deal with the new challenges that lie ahead? Here are five tips that we believe would help tech leaders to create a game-changing IT department.

Clear objectives from the business
Technology is still often implemented in isolation. Rather than being purchased to achieve set aims, IT is bought as a solution looking for a problem. Yet 2020 has seen a step change; IT is now seen as a problem solver. Digitisation helped companies deal with the challenges of coronavirus and now the business wants more from its tech.

With IT budgets still under downward pressure, CIOs and IT teams need direction from their business peers. They need to work in unison to create strongly-defined use cases. With data-led emerging tech, like artificial intelligence and machine learning, moving from the laboratory to the enterprise, that requirement has never been more pressing.

The good news is that CIOs and their IT teams will have spent much of the past 12 months engaging closely with their business peers to ensure that their technology demands are met. Now they must create a clear long-term digital strategy that puts the business first.

More commitment to the cloud
The past 12 months has almost been like a giant beta test for the cloud. At a moment’s notice, companies had to switch to home working, real-time collaboration and online fulfilment. It was a test that the cloud passed with flying colours.

The task for 2021 is to make even more use of the cloud. Global spending on public cloud services is forecast to grow 18.4% next year to total $304.9 billion, up from $257.5 billion in 2020, according to Gartner. The analyst says companies have more IT to do and less money to do it, so they will spend on areas such as the cloud to help accelerate digital business.

Gnarly elements of legacy IT remain in incumbent businesses of all sizes. If CIOs and their tech teams want to help their businesses change directly quickly, then they will need a cloud-first approach that allows the organisation to move with flexibility.

Agile development methodologies
Moving quickly, however, isn’t just about implementing the right technology. Without a people-centred information strategy that allows employees to exploit new opportunities collaboratively, then the promises of big data will go unfulfilled.

That’s where Agile development methodologies come in. Unlike traditional Waterfall approaches, which use a linear approach to task completion, Agile methods rely on iterative collaboration. While these techniques started in the IT department, Agile techniques have moved out of the data centre and out across all lines of business.

Smart companies use Agile techniques to bring in various stakeholders to explore how a business aim can be reached. By working together, cross-organisation teams can take small, iterative steps towards fulfilling data-led business aims in 2021.

Effective data management
Even if you’re moving towards completing business aims in an Agile manner, there’s still one area where your organisation can come unstuck: poor data management. It’s only possible to act on data if the information your business collects is consistent, accurate and secure.

Gartner says CIOs and their IT teams must as a matter of urgency prioritise spending on data management through 2021. The central role of data in the business is only going to increase, as companies rely on key performance indicators to ensure their organisation is making moves into the right market areas.

Your business will need to break down the traditional walls that define it. Rather than holding information in silos, CIOs will need to consolidate information so that decision-makers can make informed and intelligent choices.

Great account management
From the cloud through to data management, organisations needs to rely on the external partners they selects. Building trust is not necessarily straightforward, as CIOs have to use an ecosystem of IT providers with different capabilities for specific areas of provision.

With the business likely to up its demands for digital transformation through 2021, IT departments need partners that can deliver on their operational promises, rather than just hyping their offerings and selling more services when they believe the time is right.

Look for vendors that offer the highest possible level of account management. With IT budgets likely to remain constrained, tech teams need a trusted account managers that can work to help them deliver data-led digital transformation plans.

Summary
The coming months are going to involve building on some of the lessons learnt through 2020. CIOs should try to reflect, reset and reprioritise for the future.

ARB Bhd is eyeing a merger and acquisition (M&A) deal this year, leveraging its current strong net cash position, to expand the group’s cloud computing business.

The group plans to extend ARB’s footprint in the IT segment by targeting an M&A deal with a higher return on investment, which is expected to increase its shareholders’ value.

“I think we are in a good position to expand our business beyond the boundary. Over the last three years, we have solidified our position in the IT business in Malaysia.

“We have been focusing on the field of enterprise resource planning (ERP) and Internet of Things (IoT) solutions as Covid-19 accelerated user demand for cloud-based computing needs,” ARB ED Datuk Seri Larry Liew Kok Leong said in a statement yesterday.

Liew said the company aims to ride on the surge in demand for cloud-based computing as a result of the pandemic, coupled with rising interest from international clients for digitalisation and cost-optimisation efforts.

“IT solutions have been proven to help companies achieve their digitalisation efforts and improve cost optimisation in the long-term.

“We foresee a strong demand for solutions in new categories such as big data analytics, e-commerce solutions, IoT platform for 5G and data cybersecurity.

“The group needs to expand into horizontal markets to provide innovative solutions in this area, but the demand far exceeds our capacity and existing infrastructure.

“This is why the next phase for ARB is to look at M&A potentials and collaboration with our local partners,” he said.

Liew said the potential M&A and collaborations are expected to expand ARB’s geographical presence and access in new sectors while taking advantage of the cross-border business opportunities in 2021.

“Overseas expansion will help build a regional reputation for the group. On the domestic front, ARB will continue to work closely with government-linked companies, multinational corporations and public-listed companies to grow its ERP and IoT business segments,” he said.

The government recently introduced the MyDigital initiative which is intended to spur the country’s digital transformation direction plan and drive the business sector to compete globally. The blueprint targets the local digital economy to contribute 22.6% to the country’s GDP by 2026.

ARB saw its net profit for the financial year 2020 (FY20) rise to RM43.46 million compared to RM34.77 million in FY19, as its revenue more than doubled to RM219.45 million from RM102.64 million.

A new business intelligence report released by HTF MI with title “Global Web Monitoring Software Market Report 2019 – Market Size, Share, Price, Trend and Forecast” is designed covering micro level of analysis by manufacturers and key business segments. The Global Web Monitoring Software Market survey analysis offers energetic visions to conclude and study market size, market hopes, and competitive surroundings. The research is derived through primary and secondary statistics sources and it comprises both qualitative and quantitative detailing. Some of the key players profiled in the study are LogicMonitor, AppDynamics, New Relic, Dynatrace, Solarwinds, SmartBear.

Market Overview of Global Web Monitoring Software
If you are involved in the Global Web Monitoring Software industry or aim to be, then this study will provide you inclusive point of view. It’s vital you keep your market knowledge up to date segmented by Applications [Enterprise IT, Developers & Operations], Product Types [SaaS-based, Websites & Applications, Mobile Apps] and major players. If you have a different set of players/manufacturers according to geography or needs regional or country segmented reports we can provide customization according to your requirement.

This study mainly helps understand which market segments or Region or Country they should focus in coming years to channelize their efforts and investments to maximize growth and profitability. The report presents the market competitive landscape and a consistent in depth analysis of the major vendor/key players in the market along with impact of economic slowdown due to COVID.

The Study Explore the Product Types of Web Monitoring Software Market: SaaS-based, Websites & Applications, Mobile Apps

Key Applications/end-users of Global Web Monitoring Software Market: Enterprise IT, Developers & Operations

Top Players in the Market are: LogicMonitor, AppDynamics, New Relic, Dynatrace, Solarwinds, SmartBear

Region Included are: North America, South America, Asia & Pacific, Europe & MEA (Middle East and Africa)

Important Features that are under offering & key highlights of the report:
– Detailed overview of Web Monitoring Software market
– Changing market dynamics of the industry
– In-depth market segmentation by Type, Application etc
– Historical, current and projected market size in terms of volume and value
– Recent industry trends and developments
– Competitive landscape of Web Monitoring Software market
– Strategies of key players and product offerings
– Potential and niche segments/regions exhibiting promising growth
– A neutral perspective towards Web Monitoring Software market performance
– Market players information to sustain and enhance their footprint

Kubernetes is an open-source container orchestration platform created to automate the scaling, deployment, and management of containerized applications. Google engineers Joe Beda, Brendan Burns, and Craig McLuckie developed this platform before it was open-sourced in mid-2014. Now, it is maintained by the Cloud Native Computing Foundation (CNCF), backed by major tech giants like Google, Microsoft, AWS, Intel, Red Hat, IBM, and Cisco. Many cloud services offer a Kubernetes infrastructure as a service (PaaS or IaaS). In his article, we will see how any user, be it a seasoned IT pro or a newbie, can get started with Kubernetes.

get started with Kubernetes

How Kubernetes works
Before you can get started with Kubernetes, you should know about the general architecture of the platform. Kubernetes architecture uses several concepts and abstractions, some of which are variations of familiar notions (like services, namespaces, etc.), but others are specifically related to Kubernetes, such as Kubernetes clusters, nodes, pods, ingress, volumes, replica sets, pods, and dashboard.

Kubernetes works on top of an operating system and interacts with pods of containers running nodes. The Kubernetes master receives commands from an administrator and sends those instructions to the secondary nodes. It works with many services to automatically determine which node is best suited for a task. It then allocates computing resources and assigns the pods in that node to fulfill the requested work. The control over those app containers is done at a higher level, giving its user better control without needing each separate container or node for micromanaging.

Kubernetes architecture
To get started with a Kubernetes container, you first need to understand the basic terminologies about Kubernetes components and their associations.

get started with Kubernetes
Wikimedia

Cluster: A cluster always needs a master node, where the regulating services (known as the master components) are installed. These services can be grouped on a single machine or over multiple machines for repetition. They control workload, scheduling, and communications.

Kubernetes Master: It is is a combination of three processes that run on a single node in the user’s cluster, which is created as the master node. These processes are Kube-APIserver (that validates and configures data for the API objects), Kube-scheduler (a daemon that embeds with core control loops shipped with Kubernetes), and Kube-controller-manager (a policy-rich and topology-aware, workload-specific function).

Pods: These are a group of one or more app containers that share storage and network. The containers in a pod are deployed and managed as a single unit. If an application is deployed in a more conventional model, the contents of the pod will always be used alongside on the same machine.

Services: The pods are volatile, and Kubernetes does not guarantee a given physical pod will be kept alive. Instead, a service is a logical set of pods and works as a gateway, allowing pods to send requests to the service without a need to keep track of which physical pods make up the service.

Kubernetes proxy service: This proxy service runs on each node and helps make services available for the external hosts. It helps in sending the request to correct containers and is capable of performing simple load balancing. It also makes sure that the networking environment is accessible and predictable, and at the same time, it is isolated as well. It can be used to manage pods on nodes, volumes, secrets, and creating new containers’ health checkups.

Kubernetes volumes: It is just like a container volume in Docker, but a Kubernetes volume is applied on the whole pod and mounted on all containers in the pod. Kubernetes guarantees that data will be preserved across container restarts. The is volume removed only when the pod is destroyed. Also, a pod can have various volumes (could be of different types) associated with it.

Replica sets: This is a grouping mechanism that lets the Kubernetes platform to maintain the number of instances that were declared for a given pod. The Replica Set uses a selector whose evaluation will result in recognizing all pods that are linked with it. The replica sets host a similar data set and gives high availability and redundancy for pods.

Nodes: They work as a worker machine in a Kubernetes cluster environment. The worker machine can be a physical or in most cases, a virtual one. A node includes all the needed services to host a pod. It has two components Kubelet and Kube-proxy. The Kubelet is the leading service on a node, regularly taking in new or modified pod specifications and ensuring that pods and their containers are running in the desired state. Kube-proxy is a proxy service that runs on each worker node to work with individual host subnetting and exposing services to the external hosts. It performs request forwarding to the correct containers/pods across the various isolated networks in a cluster.

How to set up Kubernetes and use it

There are several ways to set up and get started with Kubernetes. Some of these options are:

Minkube
Minkube is a tool that can help run a single-node Kubernetes cluster in a virtual machine on any Linux OS flavors on your personal computer. This cluster will consist of a master and one or more worker nodes. The detailed steps for installing and setting up the Minkube are available here. Once done, the user can deploy a containerized application to their cluster with a deployment configuration. This local-box approach is useful for an introductory and initial practice level hands-on.

Katacoda
Katacoda is another easy option to test drive a Kubernetes cluster. It gives a temporary environment that is recycled when a user finishes using it. It provides several Kubernetes scenarios that can be used right out of the box in the interactive browser-based terminal. A user just needed to sign in, and they can start working on these scenarios. Details steps can be viewed here.

Google Kubernetes Engine
Google Kubernetes Engine (GKE) allows a user to get up-and-running with Kubernetes in no time. It provides a facility to manage and operate the Kubernetes clusters on Google Cloud Platform (GCP) without the need for any installation. A user can create a free tier account, and they can start a multinode cluster in a short time. The details about setting up GKE are available here. With this, users can also take benefits of advanced cluster management features provided by GCP, like automatic load balancing, scaling, and upgrades as well as logging and monitoring.

Microsoft Azure Kubernetes Service
Azure Kubernetes Service (AKS) offers serverless Kubernetes, an integrated continuous integration and delivery (CI/CD) experience with enterprise-level security and governance. A user only needs to create a free-tier account and start with the Kubernetes cluster, while enjoying the native integration with various Microsoft services like Visual Studio Code Kubernetes tools, Azure DevOps, and Azure Monitor. More details are available here.

Amazon Elastic Kubernetes Service
Amazon Elastic Kubernetes Service (EKS) is a managed service that makes it easy for a user to run Kubernetes on AWS without standing up or maintain any Kubernetes control plane. A user can create a free tier account with AWS to create the Kubernetes cluster and work on it. Amazon EKS provides support for both Windows Containers and Linux Containers. Additional details and use cases are available here.

Kubernetes Dashboard
Kubernetes Dashboard is a typical web-based UI that allows users to manage and visualize the resource utilization, deploy new containerized applications into the clusters, and troubleshoot applications. Kubernetes Dashboard UI is not installed by default with the Kubernetes containers, and it needs to be installed separately and configured with the Kubernetes environment. Here are the details about getting started with Kubernetes Dashboards.

Get started with Kubernetes: Why you should use it
As applications grow and more containers keep getting deployed across multiple servers, operating them becomes more complicated and challenging. To make this easier, Kubernetes can be used to manage applications in a microservice (software development technique) architecture. The Kubernetes platform provides an open-source API that regulates how and where those containers will run. This platform also provides the capacity to orchestrate a cluster of virtual machines. It can schedule the app containers to run on those virtual machines based on their available compute resources. Kubernetes can help manage service discovery, track resource allocation, incorporate load balancing, check the health of individual resources, scale based on computing utilization, and enable apps to self-heal by automatically restarting containers.

Whether you’re just beginning with DevOps or you’re pretty far down the road, this way of working is about constant improvement. Check out four big trends

Just the phrase “DevOps 2020” looks and sounds somewhat funny: It’s a milestone reminder that the term – and the culture and practices the term represents – has shed its “new and shiny” status in IT.

That doesn’t make DevOps passé; it means it has become pervasive, and it will mature in various ways in the year ahead.

“In 2020, we will see organizations [continue to] embrace DevOps practices at all levels,” says Tom Iverson, managing principal, cloud and DevOps at RBA.

DevOps will mature in some key ways during the year ahead.
By “levels,” Iverson’s not referring to organizational hierarchies – executive, managerial, and so forth. Rather, he’s noting that every team’s starting point with DevOps was a bit different, including when they got started. Indeed, some organizations are still getting going. More advanced DevOps teams might be innovating and optimizing their continuous delivery processes in the year ahead, for example, while organizations farther back in the adoption curve might still be experimenting in earlier stages of their software pipeline, or grappling with the all-important cultural components of DevOps success.

[ Culture change is the hardest part of digital transformation. Get the digital transformation eBook: Teaching an elephant to dance. ]

Regardless of your particular DevOps phase, Iverson and others anticipate ongoing trends worth watching. While the term might not be as buzzy (thankfully, perhaps) anymore, DevOps is ultimately about ongoing change and improvement. Here are four areas to watch.

1. DevOps maturity will fuel continued interest in cloud-centric tools
As DevOps improves with age, so to speak, it will enable and drive adoption of cloud-centric (or cloud-native, if you prefer the term) technologies. One example in particular stands out:

Expect to see the continued and increased adoption of Kubernetes.
“In 2020 we expect to see the continued and increased adoption of Kubernetes for organizations to deploy services,” says Josh Komoroske, senior DevOps engineer at StackRox. “The combination of its ease of use, depth of features, and integration with major cloud platforms will make it an increasingly appealing choice for developing and deploying software – even more so if your organization already deploys [containerized] software.”

[ Related read: Kubernetes: 3 ways to get started ]

This is a two-way street, too: Just as DevOps culture and practices drive interest in various tools, these technologies will help enable ongoing cultural shifts and process improvements in the year ahead.

“Cloud technologies will continue to grow and a ‘DevOps first’ mindset will be baked into new developments,” Iverson says. “Heavy-handed governance models will turn to more of a custodial approach due to the amount of automation available in the cloud space. For example, IT will look to provide self-service options to support rapid provisioning of temporary resources for proof-of-concept work.”

Moreover, Iverson sees that kind of self-service expansion being brought to teams where they work: “Commodity tools such as office suites, low-code solutions, and ChatOps tools will provide the interface for self-service,” he says.

2. IT leaders will revisit DevOps success metrics
Ongoing measurement has been a key part of DevOps culture and process for a while. That gave way to some common metrics, such as mean time to deploy (MTTD), for quantifying your progress. And this is sensible: If your initial case for DevOps was something like “we’ll be able to deploy faster and more frequently than before,” then bolstering that case requiring showing both claims were actually true.

Expect such “traditional” DevOps metrics to be increasingly joined by the somewhat more subjective measurements that sit under the outsized umbrella of “business value.”

Look for more metrics emphasizing business value and customer satisfaction, as opposed to release cadence.
“We’ve learned that faster is not always better when it comes to software release cycles,” says Justin Donato, VP of IT at Nintex. “As a result, we’re seeing organizations pivoting away from release cadences and emphasizing business value and customer satisfaction.”

You could think of this as an intersection point between DevOps and digital transformation. The point is not to simply operate faster for the sake of it, but to figure out how that increased speed benefits your business. And while “business value” might sound fuzzy, the abstraction is really a placeholder for those specific benefits in your organization. Donato points to accelerated sales cycles or time to revenue as examples of specific ways to measure the business value of DevOps.

“[Companies will increasingly] prioritize those kinds of things ahead of time-to-deploy,” Donato says. “Businesses today aren’t looking so much for fast releases as for quality releases. Competitors can spring up overnight, so it’s key to deliver capabilities that keep your customers satisfied and loyal; you don’t want them looking elsewhere.”

The California-headquartered company last year announced $2 billion investment in India over the next five years towards expanding its facilities, increasing headcount and ramping up research and development activities.

New Delhi: How India digitises and extends more services to a larger section of the population is really important and supporting the country in incorporating these changes offer great opportunities, a top executive of enterprise software major VMware has said.

The California-headquartered company last year announced $2 billion investment in India over the next five years towards expanding its facilities, increasing headcount and ramping up research and development activities.

“We’ve worked with a lot of banks over here with a policy around how do we get more women into the workforce and to allow people to be able to use their phone to do transactions rather than having to physically walk over to the branch office,” David Wakeman, VMware’s Senior Director for End User Computing, Asia Pacific and Japan, told IANS in an interview.

“I’ve talked to insurance agencies who are trying to figure out how they can enable the staff to do more things. So I think how India digitises and extends more services to the population is really important,” he said.

VMware has made its technology available to some of India’s biggest organisations, including the State Bank of India and Bharti Airtel as well as startups such as BookMyShow.

“To look at digitiaation, I think there’s a huge opportunity right now. What I would suggest is in that process, people are still, I believe, are at the centre of digitisation,” Wakeman said.

“So I would encourage organisations to think about how digitalisation improves employee experience, culturally how it allows them to access the workforce, and how it improves some of the other challenges, including those around transport.

“If we just look at digital for digital’s sake and miss the people a bit, that’s a problem. We’re spending a lot of time thinking about cheekily how technology and employee experience can come together,” he added.

Vmware’s Workspace ONE platform, for example, allows organisation to “simply and securely” deliver and manage any app on any device by integrating access control, application management and multi-platform endpoint management. It is available as a Cloud service or for on-premises deployment.

“Indians are avid consumers of technology, so whenever we do something new or different, immediately you will see that it will show up here in India first.

“We have over 1,000 data centres for the Workspace ONE platform globally. We put a data centre in India about three years ago, when we set up our own SAAS (software-as-a-service) ability,” Wakeman said.

However, in terms of Cloud adoption, Wakeman believes India has been a bit slow compared to other markets such as Australia and Japan.

“The reason for that has been twofold — one, for government sector, besides banking, the compliance issues have been stronger. In Australia or Japan for instance, there’s been a move where some of those more conservative or risk averse industries or government sectors have started to make the move to Cloud.

“The second is just the cost benchmarking. Cloud is not necessarily cheaper than what people can do themselves on premise. So I think that the value proposition initially around what Cloud offers was first approached by the Indian market as a lower cost way than us running it, and it’s taken some time for the majority of clients to change and think about Cloud having a different set of value and property than just about the infrastructure being lesser,” Wakeman said.

Commentary: The last decade has been open source’s most productive by far. Find out why Matt Asay considers it a Cambrian explosion of choice and innovation.

DevOps requires monitoring of processes to ensure the adequate performance of systems and software. No one is going to sit in front of an updating dashboard continuously looking at system performance metrics. Likewise, and more commonly, messages or emails sent from systems or people about problems can be ignored easily.

DevOps teams need systematic means of monitoring and alerts to problems as they occur. The threshold needs to be such that they see important issues immediately, without inboxes getting cluttered with too many messages. This sounds like a great use case for artificial intelligence (AI).

AI can be used to trigger alert messages on critical system conditions or problematic errors while excluding or summarizing lower-level issues. To believe the AI hype, you would think some sentient agent would be roaming an enterprise’s network and applications, sniffing out problems before they occur.

Reality Check
AI doesn’t really operate that way. Much of the hype around AI makes it sound like a generalized super-sentient system is coming.
In reality, AI uses historical data to discern patterns and trends. The direct implication is that you must have historical data. Many DevOps situations provide new functionality or new systems. Often, no historical database is available to train the AI models.

Similar projects from the past may have data, but most projects have specific incidents and performance metrics, unlike previous DevOps projects.

Making a Fit
AI is such a trendy topic that many IT organizations and teams feel they have to do something with it. Despite the lack of historical data, AI could have an immediate place in alerts for DevOps.
As more and more enterprise computing functions move to cloud services, the volume of automated alerts is exploding. Hosted services try to err on the side of caution with system alerts—better to notify too much than miss a critical problem. Their default settings send too much information to the subscribing IT department.

Here is the opportunity to apply AI to the alert management process.

Aggregating and Learning
AI needs historical data. You likely already have a set of alerts coming from existing systems that, with a little massaging, could be made into a database for AI algorithms to learn from.
Hand coding the action level or some other classification of previous messages lets AI systems try to pick out patterns and trends associated with different severity levels. Aggregating these from various projects is the basis for a generalized smart alert system.

Imagine all alerts from many different services and vendors go to a common inbox (inbox in this context is just a centralized area). Using the hand-coded action levels for previous alerts, an AI system can be trained to look for specific words, phrases, codes, times, and so on.

Associations between all factors might be used to create a secondary alerting system. This secondary alert management system triages all of the incoming messages from all the different services. The AI system might relay critical messages immediately, wait on less severe problems to occur repeatedly, or group planned maintenance messages into daily summaries, for example.

Communications Management
The AI system can also use multiple channels to communicate with key personnel. For the same message, some might receive immediate alerts via text message on a mobile device while other individuals get the same notification on Slack or Microsoft Teams, and others might see the message in their email inbox.
Although this kind of work could be hardcoded based on agreed-upon rules, the AI system does have an advantage in being able to rerun models to learn from new information. Coding new rules into a database or business intelligence system takes time and effort. Using a self-updating AI system requires less work in the short term.

An AI-centered alert management system is not going to be perfect, but it might let DevOps teams worry less about notifications.

Many companies are turning to software to differentiate themselves and drive success. But with an increased focus on software comes ever-increasing customer expectations on performance and updates. One of the best structures that IT organizations turn to is DevOps—a strategy that is designed to deliver value quickly and strategically.

Being successful in DevOps takes careful planning and execution. Is the organization structured to execute on DevOps principles? Is the company aligned in culture and goals? But just as there are commonalities for success, there also are common threads that can lead to failure. When it comes to DevOps, it indeed holds true that failure is an opportunity to learn.

With that in mind, here are three of the most common mistakes organizations encounter when diving into DevOps.

Misunderstanding Goals and Definitions
Many organizations are still grasping at a firm definition of DevOps and what it means for them. Simply labeling existing IT as DevOps isn’t going to make a difference. Furthermore, having clearly defined goals is key for just about any meaningful business endeavor. How can you know that you have succeeded if you don’t have a clear picture of what success looks like?

Before jumping onto the DevOps train, first understand what it means for your organization and decide on concrete, measurable goals to make sure the transformation is adding value. One of the key benefits of DevOps is it lends itself extremely well to data and measurement. After all, DevOps is about delivering value with velocity. How fast can you get your solutions into the hands of your customers? How close to perfect can you get your releases through automation? This measurability is what makes DevOps so satisfying—embrace it. It is a great feeling to watch the performance of a new streamlined development pipeline improve.

Fear of Change
Being fearful of rapid changes and new technologies is a killer of innovation and success. An overly cautious approach with too much manual intervention will stifle the progress of a DevOps transformation. Understand there will be change and the change may be significant for your organization, particularly depending on the current structure and processes. One of the biggest changes may be culturally—DevOps certainly requires different tools and structures, but also requires a change in understanding about how your organization fundamentally views and delivers software. Use DevOps as a catalyst for change—the speed may be daunting but can reap major long-term benefits.

Overestimating DevOps
DevOps alone can’t re-calibrate applications or change corporate culture—real leadership is required to push through organizational inertia and bring teams together. This goes back to the cultural change mentioned above. Simply putting DevOps into place and then sitting back and waiting for results will not work. Teams will need DevOps champions, not just at the beginning, but always. DevOps is a constantly growing and changing strategy, built to adapt to customer demands. It holds great power to help teams perform at new levels, but people are always the most important. Empower teams to grow with this new strategy and enjoy the benefits of a sound DevOps strategy.

Adopting DevOps is necessary to stay competitive in any market. However, while uniting people, tools and processes sound simple enough it can be a much more challenging feat than meets the eye. A DevOps transformation does not happen overnight and bumps in the road are to be expected. However, keeping in mind some of these common challenges can help keep the journey on the right track.

Bottom line: Take it one day at a time and make sure to optimize a transformation for organizational goals and long-term strategy.

In this blog, I want to discuss what we’re predicting in the area of DevOps for 2020 and, as a bonus, provide a sneak peek at some other DevOps trends that we’ll certainly be digging into in 2020.

So, what are we forecasting for DevOps this year? While our full report is here, here are three predictions:

Chaos engineering as a controls strategy. Yes, you read that right. This crazy “chaos” thing meeting the staid world of governance, risk, and compliance? Yes, because, in fact, that’s what chaos engineering excels at: identifying risk in systems that are too complex to feasibly test in other ways. We’ve been using the concept of “environment” to contain risk, and “testing in production” is one of the worst things a team can be accused of. But what happens when it’s no longer feasible to fully test in pre-production environments? A brave new world awaits.
Business value will overtake velocity as a favored metric. Currently, DevOps teams tend to measure velocity, but business value is a close second. This will change in 2020. The divide between IT and the business is shrinking, and business results will increasingly overrule any proxy metrics. As valuable as velocity may be, it is still just a proxy.
Tools will emerge to help developers keep pace. The new digital operating model has a different approach to staffing. Instead of focusing on skill sets, there’s a growing recognition that team cohesion is an essential quality, and moving people on and off teams should not be done casually. This leads to a renewed emphasis on continuous learning within the context of the team. But learning new development technologies via traditional offline methods is difficult, especially while also trying to perform daily tasks. As companies emphasize continuous team learning (so that teams don’t have to be continuously reorganized), new tools will come to the rescue in 2020 to help developers pull in “just enough” learning while creating software.
Other predictions in our full report address the new vertical market DevOps is gaining traction in and the problem of stuck or even declining release frequency across the industry.

Today In: Innovation
To learn more predictions from Forrester’s DevOps team, download Forrester’s Predictions 2020 guide to understand the major dynamics that will impact firms next year.

This post was written by Principal Analyst Charles Betz, and originally appeared here.

Most organizations know that in order to be successful, they have to be able to react quickly to external forces. And, with the constant uncertainty in today’s global markets, changes in regulatory compliance — such as the recent General Data Protection Regulations (GDPR) and upcoming California Consumer Privacy Act (CCPA) — as well as ongoing competitive pressures, organizations are working to increase their agility in order to adapt to these evolving conditions. This necessitates IT teams to embrace DevOps.

To create greater alignment with the way application development is done, IT managers have to revise how they’ve traditionally approached database change management. To do that, it’s important they have the confidence to bring database development and deployment operations into the agile Continuous Integration and Continuous Deployment (CI/CD) processes.

However, where to start can be a struggle for organizations especially with many of today’s traditional database infrastructure consisting of a hybrid database environment, with both on-premises and cloud platforms, as well as traditional and open source databases, resulting in a potentially complicated infrastructure to manage.

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Typically, database change management operations have quite a long process to get changes from development to production, with release cycles between six weeks and three months on average. While more companies are embracing agile methodologies into their organizations as a way for their business to be more proactive and innovative, incorporating database changes into these methodologies remains anything but easy due to development processes such as the unit testing of procedural code as well as the risk of applying data changes to a live environment.

Even so, there are a number of organizations that have actually embraced DevOps successfully. They are able to spend less time on unplanned rework and ensure release changes are completed faster, in return seeing lower costs and even revenue growth. Moving to a DevOps culture requires executive leadership to step up and make the necessary organizational changes and ensure IT does the research and invests in the right tooling to automate processes that have traditionally been manual.

While most organizations understand that they need to become fully agile, integrating the database into their DevOps CI/CD infrastructure is quite a big step. Here are the three benefits as to the importance of integrating database management into the CI/CD processes:

Reduce outages and errors

Database changes are traditionally done through SQL scripts, with DBAs conducting reviews near deployment, which can cause adjustments and edits to take more time, accrue additional costs, and more likely cause errors in deployment. Integrating the database into the CI/CD infrastructure enables DBAs to better build, deploy, test, and release phases with the use of key enablers within CI/CD processes like version control and build automation. With this, there are fewer errors made and fewer outages that occur.

Improve release management

Embracing DevOps improves release management with better staging environments — environments are standardized in DevOps with a staged environment that closely matches the production database. With this, teams are able to obtain results from test environments that are more in line with what is happening in the production environment. Having this ability means that production deployments are of better quality, performance, and scalability and do not cause surprises like an unexpected outage.

Ensure more concise iterations

DevOps helps collaborative cross-functional teams accomplish smaller batch cycles, initiating more minor changes that are therefore more manageable. By integrating database change management into the CI/CD infrastructure, DBAs can take on and finalize smaller changes in shorter iterations, in turn providing quicker release updates.

Adopting a DevOps culture as a part of your database development cycle means you can more efficiently produce software changes, provide speedy testing, optimize quality assurance and code changes, and confidently deploy changes in production that won’t impact the business.

SEE ALSO: Top programming languages for November 2019: C gets an A+

Before DevOps, the production DBA would be the recipient of any changes that development created and shared and would then have to figure out if the changes would cause the application to break, despite not having insight.

In the new DevOps culture, the development and operations teams, and other functions, collaborate from the beginning and share plans, changes and updates to move them through the pipeline faster. Barriers and silos between the various functional groups are removed and updates occur more quickly and accurately, giving IT teams the ability – and confidence – to support the business more efficiently.

The days of siloed database teams and processes are dwindling and DevOps has become the new reality. The more interconnected development, testing, QA, UAT, and operations teams are, the more effective IT can be. While some may believe that it’s too time-consuming or expensive to make the shift, the reality is that those who don’t convert to DevOps will fall behind.

With over 40,000 companies relying on the firm to change the way they use software, Puppet wants to end repetitive tasks, simplify workflow and bring higher levels of security and infrastructure configuration to organisations through taking advantage of modern tech.

The enterprise has recently announced two new developments. The first being ‘Project Nebula’ and the second being its plan to update its focus on continuous compliance, continuous deployment and incident remediation. Puppet spoke to DevOpsOnline to discuss their latest ideas and what they could mean for the software world.

Briefly tell us about Project Nebula

Project Nebula provides a common way for DevOps teams to build, provision, and deploy cloud-native applications. Nebula’s intuitive design simplifies the most complex deployments and enables developers to rapidly deploy changes to dev, test, and production environments with high confidence and little risk.

Why did you feel this product needed to be created?

Our traditional DevOps customers are looking for ways to accelerate value delivery to the business and cloud-native computing is one of the many ways that they are looking to achieve that objective. Most of us have realized some sort of self-service delivery model and we are practicing continuous testing and delivery for most traditional applications but the next steps to get to continuous deployment can be challenging.

Continuous deployment requires the ability to model environments so that they can be deployed on-demand by each code committer to test out their changes.

Based on our initial private beta, we found two major pain points that were inhibiting folks to apply continuous deployment to cloud-native applications:

• First, they lacked a tool that could automatically compose multiple cloud-native tools together for infrastructure provisioning, application deployment and notifications into an end-to-end deployment.
• And even further, they lacked a cohesive platform for multiple teams to share workflows and best practices to build out their own deployments.

Puppet has always been a leader in providing a common workflow to abstract away complexities through automation and thought this could be a really good problem for us to solve. We borrowed some of the core principles that we used to re-invent configuration management automaton and extended them into the cloud-native world.

Infrastructure developers or DevOps teams, who have already realised the benefits of infrastructure as code will be able to apply similar declarative principals to cloud-native continuous deployment with Project Nebula.

What will it mean for IT teams?

Nebula enables IT teams to adopt cloud-native technologies at scale across their organisation. DevOps teams will now be able to create reusable workflows that application developers and infrastructure admins can use to deploy cloud-native environments on demand for dev, test, and production use cases. We believe that this will unlock even more business value by enabling developers to push their code out to production dramatically faster than before with higher quality.

Why do you think cloud native technologies are playing such a big role in IT today?

Simply put, organisations are looking to adopdt cloud-native architectures and delivery workflows to realise the full benefits of the cloud —faster value delivery, higher resilience, and deeper automation. All companies want to deliver software and features faster for a competitive edge.

What is the biggest challenge that people face in adopting cloud-native technologies?

As we noted above, one of the challenges that we are seeing is being able to adopt cloud-native at scale. People can usually get started initially but when it comes to building and growing out and to multiple teams, there’s a lot of challenges because they are working with new workflows, new technologies – it can be a big learning curb and you can hit a roadblock.

There’s also an ill-conceived notion that cloud-native technologies will simplify your life and when adopting cloud-native technologies, applications developers can now manage everything —that won’t happen. Great app developers don’t want to be saddled with things like security patching, compliance reporting, credential rotation, setting up the right monitoring thresholds and alerts—this list goes on.

Organisations that hold on to these beliefs will make early progress as cloud-native technology projects incubate, but they will flounder under the reality—and complexities—of running and securing software at scale once the reality of production tarnishes the promise of how containers and serverless make everything easy.

What are you doing to remove the roadblocks that teams face?

Management and those who are looking to instrument cloud-native environments need to know that their teams will face more complexity not less and they really need to focus on building out teams with strong infrastructure developers who understand how to manage and build on complexities.

In addition, they’ll need to start looking at tools that help them start automating out complexity in all their environments — including cloud-native — as automation is no longer a nice to have, it’s a must as we start to build diverse, multi-cloud environments.

What are you doing to help modernise companies?

Infrastructure is what holds everything else up, being able to put your infrastructure in a good known state, abstracting away complexities makes applications more portable and infrastructure more aware. This, in turn, helps businesses modernise their infrastructure and applications in a safe and secure manner.

Puppet brought this to the server world and is bringing this into a multi-cloud world, so companies can continue to innovate knowing that their infrastructure is elastic, safe and secure and in the state that they want it to be.

What are you doing to empower your customers?

Puppet’s founder started Puppet back in 2005 with a deep meaning and purpose – to eliminate soul-crushing work. This is still the motto that drives Puppet today. As IT becomes increasingly complex, we are still focused on taking out soul-crushing work through automation, and ensuring that our customers can effectively scale and secure environments so businesses can stay stable and grow.

We are also heavily focused on helping our customers move to the next chapter of a multi-cloud world where they will be managing traditional environments and making moves to cloud, containers and beyond. We are building the tools that help them create continuity and leverage content across these environments so they can more quickly ramp up and handle the changes that they are being asked to make by the C-Suite. Project Nebula is a great example of how we are taking concepts from a traditional environment and building them into cloud-native.

In an ideal world, what would need to be done to completely optimise continuous compliance and continuous deployment?

Compliance comes in many forms when it comes to cloud-native infrastructure. Most folks need to satisfy a set of regulations that are specific to their industry, PCI for retailers as an example. In an ideal world, there would be multiple checks across the delivery pipeline to check for and enforce compliance policy. Scanning the source code is a proactive step that most folks do today, the next step would be to integrate a continuous verification interface into your delivery pipeline, typically during the test and deployment phases. Folks also scan the running apps and infrastructure to detect compliance violations which then automatically triggers a new deploy that includes the fixes required to be back into compliance.

And for all of the above to take place, teams would need to redesign their organisations and processes to fit this automated approach, and trust that those checks against compliance policies are sufficient enough to forego a final human check before deployment.

What are the hopes for the future of Puppet?

Puppet wants to continue to drive success for our customers and community. We’ve been instrumental in many of our community and customer’s careers, and we hope to continue to provide the tools that our community and customers need to be successful at their jobs and have a life outside of the office.

Based on a survey of 371 IT and cybersecurity professionals at organizations in North America, the ESG study finds 55% of respondents have incorporated security into their DevOps processes, with another 22% planning to do so and 20% still in the evaluation stage. However, the study makes it clear that adoption of DevSecOps is still uneven at best. Only 33% are involving cybersecurity teams at the start of the application development process. And 39% of respondents report that members of their cybersecurity team are involved with more than half of cloud-native application projects today—a number that is projected to jump to 78% in the next two years.

The survey also notes only 8% of respondents are securing 75% or more of their cloud-native applications with DevSecOps practices today. Within two years, however, 68% of respondents expect to be securing 75% or more of their cloud-native applications using DevSecOps practices.

Doug Cahill, a senior analyst and group director at ESG, said the study makes it clear cybersecurity increasingly is being baked into applications rather than bolted on. The challenge now is finding a way to accomplish that goal at unprecedented levels of scale, he said.

The platforms on which applications are being built and deployed are more diverse than ever. Organizations are running workloads on virtual machines (34%), bare-metal servers (28%), containers (23%) and serverless computing frameworks (15%), according to the survey results. Only 10% are running more than 50% of those workloads on a public cloud, but that amount is forecasted to jump to 30% in two years.

Not surprisingly, the biggest cybersecurity challenges those organizations face is consistently managing cybersecurity across multiple platforms (43%), the cost and complexity of implementing cybersecurity controls across those platforms (35%) and lack of understanding of the threat vectors cloud-native applications face (35%).

In terms of container security concerns, aligning controls with deployment models (32%) and verifying images in registries meet compliance requirements (32%) were cited as the top two issues. In terms of serverless computing, application programming interface (API) vulnerabilities (32%) were the most cited. As far as budget allocations are concerned, vulnerability scanning of registry-resident container images (26%) narrowly beat out API vulnerabilities tools (25%).

Half of respondents also said they expect their organization will consolidate controls by leveraging suites and platforms procured from a smaller set of vendors or even a single vendor. Nearly three-quarters (73%) said they believe that their organization uses too many specialized products to secure cloud-native applications properly. Nevertheless, 59% said they prefer disparate point controls to achieve more functionality even if it means using multiple controls for different compute types/locations.

The most important attributes of products used to secure cloud-native apps identified by survey respondents included a rich set of pre-deployment capabilities, runtime capabilities and support across a mix of server workload types, with flexible deployment options.

The biggest issue, however, may be just the way these organizations are currently structured. The survey finds 82% of respondents have different teams assigned to secure cloud-native apps. Among those respondents, half plan to merge these responsibilities in the future, while 32% intend to keep them separate.

Put it all together and it’s apparent there’s a long way to go before DevSecOps nirvana might be achieved. The good news is most organizations have a least started the journey.

Cloud computing is an emerging technology in the IT industry for the sharing of resources in order to achieve higher utilization of IT resources. Companies are adopting new technologies, such as internet of things (IoT), artificial intelligence, and mobile app hosting, in order to streamline the development and networking process. Cloud computing offers internet services to any employee of a company with high scale computing power. Cloud networking is a term describing the centralized access of all networking resources using wide area networking. In organizations, there different applications runs on server such as network management, virtual desktop, voice over internet protocol (VoIP), and unified network. Managed cloud networking platform offers security and privacy in data transmission and it also provides the high availability of data to accessible to user all the time. Managed cloud platform allows enterprises to shift their entire network process on cloud computing in order to maintain the network performance on cloud networking. Enterprises are expected to adopt private cloud and public cloud platform to improve their data transmission and performance of communication network. Several companies are offering a managed cloud platform for development, deployment, and management of applications.

Rise in demand for cloud networking in order to improve productivity and simplify the deployment of the application is projected to drive the market. Managed cloud networking platform simplifies networking by reducing complexity and cost of distributed deployments of organization. Cloud networking creates an opportunity for companies to reduce capital expenses on IT networking resources. All data transmitted on cloud network is likely to pose a threat of confidential information leakage, security, and privacy issues for companies. This, in turn, is expected to restrain the managed cloud networking platform market. Several solution providers create opportunities by offering services to developers to create, run, manage, and deploy applications for both on-premises and public cloud environment. Companies are expected to adopt managed cloud platform with advanced features such as hosting, elastic computing, relational database (SQL), object storage, and artificial intelligence. These features are expected to set a new trend in cloud computing application.

The global managed cloud networking platforms market can be segmented based on services type, cloud deployment, enterprise size, end-user, and region. Based on services type, the managed cloud networking platforms market can be classified into business, security, mobility, network, and data center. Based on cloud deployment, the market can be segregated into public and private cloud. Based on enterprise size, the managed cloud networking platforms market can be bifurcated into small-medium enterprises and large enterprises. In terms of end-user, the managed cloud networking platforms market can be divided into telecom and ITES, banking, financial services & insurance (BFSI), healthcare, government & education, retail & consumer, manufacturing & automotive, and media & entertainment.

Based on region, the managed cloud networking platforms market can be segmented into Europe, North America, Middle East & Africa, Asia Pacific, and South America. North America is expected to account for a prominent share of the managed cloud networking platforms market, in terms of revenue, primarily due to the presence of major technology IT companies and an increase in the demand for managed cloud networking for enterprises in the region. Additionally, higher rate of adoption of technology by companies in the region is expected to create a considerable customer base for solution providers. Asia Pacific is expected to be a rapidly expanding market for managed cloud networking platforms due to the surge in adoption of managed cloud networking services in small- and medium-sized enterprises, which in turn is expected to drive the managed cloud networking platforms market in the region. Additionally, rise in investment by Google and IBM in managed cloud services is anticipated to propel the managed cloud networking platforms market in the region.

Key players operating in the managed cloud networking platforms market include Aerohive Networks, Inc., Cisco Systems, Inc., NaviSite Europe ltd, Accenture plc, Interoute Communications Limited, Alcatel-Lucent S.A., Huawei Technologies, International Business Machines (IBM) Corporation, Ericsson, Fujitsu Ltd., NEC Corporation, and NTT Data Corporation.

The report offers a comprehensive evaluation of the market. It does so via in-depth qualitative insights, historical data, and verifiable projections about market size. The projections featured in the report have been derived using proven research methodologies and assumptions. By doing so, the research report serves as a repository of analysis and information for every facet of the market, including but not limited to: Regional markets, technology, types, and applications.

The study is a source of reliable data on: Market segments and sub-segments Market trends and dynamics Supply and demand Market size Current trends/opportunities/challenges Competitive landscape Technological breakthroughs Value chain and stakeholder analysis

The regional analysis covers: North America (U.S. and Canada) Latin America (Mexico, Brazil, Peru, Chile, and others) Western Europe (Germany, U.K., France, Spain, Italy, Nordic countries, Belgium, Netherlands, and Luxembourg) Eastern Europe (Poland and Russia) Asia Pacific (China, India, Japan, ASEAN, Australia, and New Zealand) Middle East and Africa (GCC, Southern Africa, and North Africa)

The report has been compiled through extensive primary research (through interviews, surveys, and observations of seasoned analysts) and secondary research (which entails reputable paid sources, trade journals, and industry body databases). The report also features a complete qualitative and quantitative assessment by analyzing data gathered from industry analysts and market participants across key points in the industry’s value chain.

A separate analysis of prevailing trends in the parent market, macro- and micro-economic indicators, and regulations and mandates is included under the purview of the study. By doing so, the report projects the attractiveness of each major segment over the forecast period.

Highlights of the report: A complete backdrop analysis, which includes an assessment of the parent market Important changes in market dynamics Market segmentation up to the second or third level Historical, current, and projected size of the market from the standpoint of both value and volume Reporting and evaluation of recent industry developments Market shares and strategies of key players Emerging niche segments and regional markets An objective assessment of the trajectory of the market Recommendations to companies for strengthening their foothold in the market   

Note: Although care has been taken to maintain the highest levels of accuracy in TMR’s reports, recent market/vendor-specific changes may take time to reflect in the analysis.

In early June 2019, Google Cloud suffered a cascading set of faults that rendered multiple service regions unavailable for a number of hours.

This by itself isn’t totally unprecedented; what made it significant was the way it propagated through the very software that was designed to contain it. Moreover, engineers’ initial attempts to correct the issue were thwarted by the failure of that same software architecture. It was the interconnectedness and interdependencies of Google’s management components that contributed to the outage.

The outage

To understand this situation more fully, the following is a short summary of what happened. A maintenance “event” that normally wouldn’t be such a big deal triggered a reaction in GCP’s network control plane, further exacerbated by a fault in that code enabling it to stop other jobs elsewhere in Google’s infrastructure.

The distributed nature of a cloud platform means that although clusters in one area are designed to be maintained independently of clusters in another, if those management processes leak across regions, the failure spreads like a virus. And because these controllers are responsible for routing traffic throughout their cloud, as more of them turned off, network traffic just became that much more constrained, leading to even more failures. In technical terms:

• Network control-plane jobs were stopped on multiple clusters, across multiple regions at the same time
• Simultaneous packet loss and error rates increased across multiple regions
• As a result, key network routes became unavailable, increasing network latency for certain services
• Tooling to troubleshoot the issue became unusable because tooling traffic competed with service traffic

Once the root cause was identified, remediating the failures required unraveling the management paths to take the right processes offline in the right order and apply the necessary fixes:

• Google engineers first disabled the automation tool responsible for performing maintenance jobs
•  Server cluster management software was updated to no longer accept risky requests which could affect other clusters
• Updates were made to store configurations locally, reducing recovery times by avoiding the latency caused by rebuilding systems automatically
• Network fail-static conditions were extended to allow engineers more time to mitigate errors
• Tooling was improved to communicate status to impacted customers even through network congestion

Ultimately, the fault did not lie in Google’s willingness or ability to address issues, but rather in a systemic problem with how the platform reacted to unforeseen events. In a real-time computing environment, there is no margin during which management systems can be offline to fix a different problem located in the other system used to apply fixes to the first one.

So, what does this teach us about modern IT? It proves the theory that operations need to be centralised across footprints and infrastructure with bias towards platforms over point tools. Here are three specific features you want in a modern IT operations strategy.

A global view for local action

Having a global view of a highly distributed infrastructure is critical. Addressing issues in isolation has the potential of propagating faults into areas not currently under test. You need to create points of observation across all regions simultaneously in order to aggregate management data, thus enabling unified analysis to avoid compounding disparate events. This will also help you build a global service performance model by understanding activity throughout your infrastructure.

You must also consider how management tasks are carried out when systems become unresponsive; protect tooling traffic from service traffic and don’t share the network capacity between operational data and system status / events.

The ability to see impact, impactfully

Your enterprise should design a global impact model. IT operations personnel need to understand topology-driven impact before making changes – even automated changes. Google’s automation did not consider an impact model, and instead was straight policy-driven automation that scaled mistakes automatically. It’s equally important to understand service dependencies via topology mapping, thus enabling impact analysis to take into consideration the cascading effects of policy-driven automation.

Configuration stores you can count on

Lastly, “backing up the backups” by storing configuration data locally instead of relying on a distributed hierarchy can reduce service restoration time. Retrieving this data regionally will increase recovery latency since competing network traffic during a fault will restrict bandwidth available for management tasks.

This outage was essentially a gift to enterprise-class IT operations teams everywhere who think they are prepared for any inevitable service disruptions. It’s taught us all the value in building an IT ops management strategy that includes a singular, global view of dependencies and impacts. Every business thinks they want to become Google. But this is one way you don’t.