SAN FRANCISCO, Calif., June 9, 2021 (SEND2PRESS NEWSWIRE) — United Private Cloud, a secure enterprise-grade multicloud hosting provider known for designing, building, and managing enterprise-class private cloud infrastructure, has been positioned as “Innovators” in MarketsandMarkets’ Infrastructure as a Service Micro Quadrant Report.

MarketsandMarkets’ report recognizes United Private Cloud as the Multicloud expert that offers its innovative IaaS to global commercial customers.

According to the report, “United Private Cloud is an established player in the global IaaS market space. The company has a robust partner ecosystem in the IaaS market. Its enriched IaaS offering helps customers to add additional storage, compute, and networking resources to meet the increasing business demands.”

United Private Cloud’s infrastructure management services help organizations maximize the benefits of a multicloud environment without compromising on availability, performance, and security. Our managed infrastructure services cover on-premises data centers, private cloud, and leading public clouds such as AWS, Microsoft Azure, and Google Cloud Platform (GCP). United Private Cloud helps monitor, report, and interface with third-party auditors to extract data and run compliance tests on the customer’s cloud.

The software-defined infrastructure management offered by United Private Cloud enables enterprises to modernize their infrastructure and improve their responsiveness, resource utilization, scalability, and agility resulting in better customer experience and faster time to market. United Private Cloud delivers enterprise-grade private clouds, disaster recovery, security, and infrastructure managed services for leading public clouds, including AWS, Azure, and Google Cloud Platform (GCP).

Abhijit Phanse, CEO of United Private Cloud, was ecstatic on the recognition and said, “We at United Private Cloud persistently work to maximize the benefits of our customers’ multicloud environment. With our solutions leveraging enterprise-class server and fully managed bare-metal server, we provide the tailored requirement, enhance the product portfolio, and improve the revenue systems to our customers along with a true hybrid cloud and a vision for a long-term partnership, allowing them to plan for its IT infrastructure needs for today, yet helping them grow as their business evolves over time.”

“United Private Cloud’s G3 IaaS Private Cloud platform has comprehensive private cloud solutions present in the market. Serving hundreds of enterprises and government across the globe, G3 laaS delivers high performance 99.999% availability, locked-down security, software-defined agility, and scalability while delivering greater than 30% cost savings versus leading public clouds and greater than 70% cost savings versus other leading private clouds,” said Ritesh Chawrashe, Assistant Manager at MarketsandMarkets.

United Private Cloud

United Private Cloud is a leading enterprise-grade Software-Defined Private Cloud company delivering enterprise-grade private clouds across 14 data centers over 5 continents globally. United Private Cloud’s G3 IaaS provides high-performance, 99.999% availability, locked-down security, and software-defined agility and scalability while offering greater than 30-70% cost savings than any primary public cloud provider. We are one of the world’s experts at building and operating highly available, secure, and scalable private clouds.

MarketsandMarkets:

MarketsandMarkets™ is the world’s largest revenue impact company, serving over 7500 customers and providing strategic analysis services. Over ten years, MNM has identified and researched all high-growth use cases, technologies, ecosystems converging across multiple industries. 80% of the top 2000 companies globally rely on MarketsandMarkets for determining the new high growth and niche revenue opportunities.

Cloud computing is quickly gaining ground in the business world and Microsoft Azure is taking the lead. About 84% of enterprises had a multi-cloud strategy in 2019. Gartner’s Top Strategic Trends for 2021 has established that organizations require a strong AI strategy to make sure that their projects don’t stall.

With cloud computing showing no signs of reducing in its application and Microsoft taking a strong lead, it is important to examine what Azure Cloud is and how it impacts businesses and what Microsoft would be doing with it in the future.

What is Microsoft Azure?
Azure Cloud is a cloud platform that provides both low-level cloud solutions and high-level infrastructure. The infrastructure makes it possible for companies to run their applications in a scalable, elastic, and redundant environment hosted in the cloud.

Signing up on the Azure Cloud platform gives you access to over 200 products and cloud services that are designed to offer new solutions to your business operations. It is designed to solve modern business challenges and help build a future that would be unimaginable without cloud computing.

With Microsoft Azure, you can build, run and manage applications across multiple clouds, on-premises, and at the edge with great tools of your liking.

Why Azure Cloud holds the future of cloud services?
Virtually everything in the digital world has a connection with the cloud in some way. As tech giants focus on developing new ways of organizing, processing, and presenting data, cloud computing is slowly becoming part and parcel of our everyday lives.

In the recent past, Microsoft Azure, Google Cloud, and Amazon Web Services have provided cloud computing and cloud storage services. Newer entrants such as Vultr and Linode have also made inroads into the space.

What do you think is the future of cloud computing? How will it affect future business strategies, and to what extent? Concepts such as decentralized cloud computing and serverless cloud may start trickling into your mind.

Infrastructure as a Service (IaaS)
Azure today is synonymous with Infrastructure as a Service. IaaS is the primary low-level public cloud service used to provide instant computing infrastructure. It is managed over the internet.

IaaS is unique because it is less expensive and is a simple way of managing physical servers and data center infrastructure. It offers essential storage, computing, and networking resources on demand.

With all the amazing computing benefits that come with it, adopting an IaaS solution for your organization helps reduce maintenance of on-premises data centers, gain real-time business insights and save money that would have been spent purchasing and maintaining hardware.

Organizations are keen on the scalability of their business operations, and more emphasis will be put on this in the future. IaaS solutions give businesses the flexibility to scale their IT resources up or down as their needs change. They also have a provision for you to quickly introduce new applications, further increasing the reliability of your infrastructure.

With IaaS, you will not incur the costs of buying and managing physical servers and data center infrastructure. Besides, you only have to pay for the resources you need since each one of them is offered separately.

Azure Data and Analytics
Azure data and analytics provide unified data governance that helps organizations do away with data challenges and shape their future.

Azure Cloud makes the future look bright by offering the general availability of Azure Synapse Analytics, a limitless analytics service that provides business insights with an emphasis on enterprise data warehousing, Big Data, and data integration.

Azure Synapse Analytics combines various capabilities, among them, machine learning, business intelligence, and data engineering. Simply put, Azure Synapse Analytics securely and seamlessly unifies your company’s data in one location.

You can get started with Azure’s machine learning features in this guide from Iteration Insights.

Data Security
No other player in the tech industry beats Azure Cloud in offering secure cloud computing. Microsoft leverages several years of being a productivity solution provider and being an enterprise operating system to provide top-shelf security to all the information hosted in their cloud products.

Microsoft Azure has put together several resources to ensure organizations’ on-site data centers are secure. They update their systems on a regular basis to make their systems immune to external threats.

Once you have purchased Azure Cloud services, you will gain access to all elements related to the security of your information. All their customers have access to the latest threat detection and prevention technologies.

There is a range of security solutions and architecture that come with Azure Cloud. They include Information Protection which enables organizations to classify and protect specific information, documents, and emails by applying labels to them, and Advanced Threat Protection and Threat Intelligence.

Azure Cloud threat monitoring agents generate real-time security alerts and threat intelligence reports which provide insights into the attackers, their motives, and their tactics.

Improved business sustainability and scalability
Many businesses will be expanding their operations in the future. If they want to do so without major hiccups, they should adopt Microsoft Azure Cloud technology which provides scalability to businesses.

Under normal circumstances, it is quite costly and time-consuming to expand a business and provide it with new infrastructure. Azure Cloud guarantees the availability of services throughout the year, thanks to its many data centers the world over. As if that is not enough, Microsoft is planning to add even more servers.

Azure Cloud boasts of having a physical infrastructure in every continent. This allows for replication in the case where an organization would like to expand beyond the borders of one continent.

Final thoughts
There is no doubt that Azure Cloud is a game-changer in cloud computing. The public cloud service, IaaS, for instance, is making cloud computing very easy while providing a simple way of managing physical servers and data center infrastructure. If you picture the future of cloud computing, you are probably thinking serverless cloud, enhanced data security, improved business analytics, and business scalability. Azure Cloud provides it all. It holds the future of cloud computing in its hands – or in the cloud – so to speak!

Microsoft claims its new PyTorch Enterprise on Microsoft Azure is the first offering from a cloud platform to provide enterprise support for PyTorch, the popular open source deep learning framework.

Along with that enterprise support, it comes with prioritized troubleshooting and also integrates with other Azure solutions, such as Azure Machine Learning.

PyTorch is an open source machine learning/deep learning framework based on the Torch library, used for applications such as computer vision and natural language processing, driven by Facebook’s AI Research lab, according to Wikipedia.

Regular Visual Studio Magazine readers will know that it’s also a favorite tool of our own data science guru, Dr. James McCaffrey of Microsoft Research, who authors regular hands-on PyTorch-based tutorials for our Data Science Lab.

The new offering comes after Microsoft teamed up with Facebook to become a founding member of the PyTorch Enterprise Support Program, which helps service providers develop and offer tailored enterprise-grade support to their customers

The enterprise support initiative was reportedly sparked by feedback from customers, who found it easy to get started with PyTorch but not so easy to implement complicated real-world enterprise production initiatives. Thus Microsoft will provide commercial support for the public PyTorch codebase. “Each release will be supported for as long as it is current,” Microsoft said in a recent blog post. “In addition, one PyTorch release will be selected for LTS every year. Such releases will be supported for two years, enabling a stable production experience without frequent major upgrade investment.”

Supported configurations include:

PyTorch: version 1.8.1 and up.
Libraries: torch, torchaudio, torchvision, torchtext, onnxruntime, and torch-tb-profiler.
Python: version 3.6 and up.
NVIDIA CUDA: versions 10.2 and 11.1.
Operating systems: Windows 10, Debian 9, Debian 10, Ubuntu 16.04.7 LTS, and Ubuntu 18.04.5 LTS (x86_64 architecture only).
It does not, however, support C++ or Java interfaces, or PyTorch libraries and features marked “experimental and subject to change” including TorchServe and Pipeline Parallelism.

To be eligible at no additional cost, enterprise customers must join Microsoft’s Premier or Unified support programs. For such organizations, as part of the aforementioned prioritized troubleshooting, “The dedicated PyTorch team in Azure will prioritize, develop, and deliver hotfixes to customers as needed. These hotfixes will get tested and will be included in future PyTorch releases. In addition, Microsoft will extensively test PyTorch releases for performance regressions with continuous integration and realistic, demanding workloads from internal Microsoft applications.”

Microsoft is heavily invested in the PyTorch ecosystem, noting that the company’s data scientists like Dr. McCaffrey use PyTorch as the primary framework to develop models to enhance Office 365, Bing, Xbox and other offerings. That investment includes projects such as PyTorch Profiler, ONNX Runtime on PyTorch, PyTorch on Windows, DeepSpeed and more.

For now, PyTorch Enterprise is available on Azure Machine Learning and Data Science Virtual Machines (DSVM), coming soon to Azure Synapse Analytics.

The cloud computing market has surged since the outbreak of the pandemic which saw years of digital transformation efforts compressed into months, and in some cases even weeks, as the world moved online.

As business confidence in the UK hits a five year high and economies start to reopen, this growth shows no sign of slowing down. Gartner predicts that the public cloud computing market will be worth $397 billion by 2022 and could be as high as nearly $700 billion by 2026.

Behind this huge figure is a vast ecosystem of potential partners, with a variety of solutions and services. Of these providers, the hyperscale cloud providers – such as Microsoft Azure, Amazon Web Services and Google Cloud – all have the scale, expertise and cutting-edge innovation capabilities to ensure businesses can take advantage of all that the cloud has to offer. But, with so many options and a unique solution for every single company, it can be a difficult decision to navigate.

Not your typical procurement exercise
The first step is to shift your mindset towards your cloud provider and remember that this isn’t a one-way street. The hyperscale providers are betting on growth from their customers too. Organisations should therefore try not to see their cloud provider as a typical supplier, but rather a business partner. This means the relationship should be elevated right up to the C-suite.

These partnerships are likely to last for years, so businesses need to have a clear strategy and do their research to find out what fits their requirements. Asking several key questions upfront can help crystallise this strategy:

What are your unique needs?
The majority of your fundamental requirements can likely be met by each major player, so you need to think carefully about what unique needs your business has. Some providers are well-established in particular industries, offering out-of-the-box solutions you can get up and running quickly and cheaply. Others might have more advanced analytics and artificial intelligence offerings that could give you a leg-up over the competition in the long-run. Look at the roadmap of each provider over the next 12-18 months and evaluate which best matches your own vision.

Your current technology environment will also guide your decision and is unique to every business. The same goes for data sovereignty, depending on the regions you operate in, as well as other regulatory factors you need to adhere to, such as GDPR requirements.

How far do you go in on the platform?
Do you plan to use hyperscaler platform services (PaaS) or build your own applications on its infrastructure (IaaS)? Do you plan to adopt a hybrid-cloud model and still maintain some data on-premise? This decision must carefully consider the skills, capabilities, and time available to your team to ensure you can get the most out of your investment.

What incentives are being offered?
Nearly all hyperscalers will consider financial incentives to smooth initial costs and accelerate the transition, such as cash offers, asset purchase and service credits. It’s often the case that the more workloads you give to a provider the cheaper the cost will be per workload, which may also impact your decision.

How do I avoid getting locked-in to one partner?
By far one of the most common concerns is being locked-in to working with just one cloud partner. But, while this can happen, most businesses use hybrid solutions to choose from a marketplace of providers. In fact, recent Accenture research shows 67% of enterprises use two or more public cloud providers. With a clear strategy and vision of each business need you can take a targeted approach that gets the best services from multiple providers.

There are also technical measures you can put in place to vendor avoid lock-in, such as using open standards. For example, you could use openSQL or PostgreSQL rather than a proprietary database. Containerisation, such as Kubernetes, also means you can switch between architectures smoothly.

More isn’t always merrier
There can also be disadvantages in trying to spread across too many providers. More partners mean greater complexity when trying to join up data and applications across the business, which may end up setting you back. It also means your team must take significant time and resources gaining the skills necessary to operate across every platform.

For most organisations, the best solution is to select one or two primary hyperscalers for the majority of critical workloads, then work with secondary providers on a case by case basis where the specific needs of the business require specialised capabilities. This approach means that you are still in a strong position to take advantage of many offerings and have greater business continuity, whilst keeping training requirements to a manageable level.

Work in progress
Having your core cloud partnerships in place is not the end of the process. Hyperscale providers are always innovating and developing new services in areas such as IoT, AI and more. Organisations should constantly check in with the provider to see how their new services, tools and capabilities can enable their business to operate differently.

What’s more, if your needs change, having a strong relationship may place you in an advantageous position and save you having to switch. Providers are often willing to invest in developing unique capabilities for loyal customers. This is particularly the case if there’s a good partnership at the C-suite level or if they’re looking to catch up with a competitor that’s dominant in a certain area.

Cloud is a journey, not a destination
With nearly every company now undertaking some form of cloud journey, simply being ‘in the cloud’ is no longer the differentiator it used to be against the competition. But collaborating in partnership with the right hyperscaler for your business needs can take your company to the next level.

While choosing the right provider is vital though, ultimately the ongoing responsibility of managing the IT environment and getting the most out of it comes down to the business and its IT leaders. The final article in this series will explore how IT leaders can get the most value from cloud by using it to reinvent how the entire organisation works.

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.”

The latest report addition addressing stark alteration s in global Cloud Computing Stack Layers market is a must-have business document to aid various investment discretion of leading players and aspiring novice entrants in the industrial space.

The research documentation on global Cloud Computing Stack Layers market offers readers new perspectives to decode market developments highlighting crucial factors such as market size and dimensions, along with trend identification and competition assessment affecting market growth projections across geographies. An assessment guide of global Cloud Computing Stack Layers market with in-depth references of drivers, novel growth opportunities, existing barrier analysis have also been well represented in this versatile report to recoup growth track.

Competition Assessment: Global Cloud Computing Stack Layers Market

Amazon Web Services
Salesforce
Microsoft Azure
IBM
Google Cloud Platform
SAP
Rackspace
H&P Helion
OVH
Avaya
Oracle

The competition assessment of the global Cloud Computing Stack Layers market is a requisite tool to understand various vendor activities and their preferred mode of business delivery with promising business outcome have been aggressively discussed in this section of the report. Further, other relevant details such as SWOT assessment of each of the mentioned players, pricing strategies braced by vendors aligning with end-user affordability as well as consumption and production dynamics along with facility expansion needs are also widely discussed in the report. The competition intensity of the vendor landscape, highlighting players and other relevant market participants are also assessed in the report besides evaluating the vendor potential on pre-set parameters and SWOT assessment.

Further, each of the products and services highlighted in the report have also been classified into specific segments to understand pricing strategies of each segment, revenue generation potential and overall sales performance throughout the assessment span.

Segmentation by Type:

Software as a Service (SaaS)
Platform as a Service (PaaS)
Infrastructure as a Service (IaaS)

Based on application, segmentation is primarily been harnessed to understand the scope of application based on industrial demands and user preferences.

Segmentation by Application:

Commercial Use
Public Services
Others

Regional Segmentation identifies Americas, European nations, APAC countries and MEA as popular growth hubs.

Key Questions Answered in the Report?
• This report is a dependable guide to understand core factors influencing growth and expansion in global Cloud Computing Stack Layers market
• The report helps readers to understand the most crucial factor instrumenting high revenue generation amidst staggering cut-throat competition.
• The report sheds ample light on regional scope and advances in geographical sphere that leverages highest revenue gains. Various factors such as profit margins, sales performance, past and current events that accelerate future scope of actions have also been amply discussed in the report aligning with customer expectations for end-to-end assessment.
• The report also includes versatile understanding of market developments across past and present timelines that eventually enunciate future plausibility of growth and uniform expansion.

COVID-19 Impact Analysis
This intensively researched report presentation has been prepared in real time parlance, rendering substantial attention towards COVID-19 outbreak that has lately wreaked unprecedented damage across industries, stagnating growth.

These crucial market relevant information are estimated to trigger tremendous growth returns and sustain market stability through the forecast tenure.

With unfailing market gauging skills, has been excelling in curating tailored business intelligence data across industry verticals. Constantly thriving to expand our skill development, our strength lies in dedicated intellectuals with dynamic problem solving intent, ever willing to mold boundaries to scale heights in market interpretation.

The four integrations add native support for Microsoft Azure Pipelines, a Jenkins Plugin, GitHub Actions and GitLab pipelines. A continuous integration/continuous delivery (CI/CD) Spoke capability on the ServiceNow platform makes it possible for developers to build automated pipelines using the Flow Designer tool.

Marcus Torres, vice president of product platform management at ServiceNow, said these latest integrations build on a DevOps integration initiative that ServiceNow launched last year. Rather than building its own DevOps platform, the company has opted to focus on integrating ticket-based ITSM processes with DevOps processes as the management of IT continues to evolve and mature.

In some cases, best DevOps practices for rapidly deploying applications is an absolute requirement, he noted. However, there are plenty of IT processes that are still best served by a ticket-based approach based on best practices defined within the widely adopted ITIL framework.

Since the initiative was launched, the number of organizations that are employing the low-code ServiceNow App Engine platform to build custom workflows that integrate DevOps platforms is also steadily increasing, said Torres. ServiceNow will continue to add other integrations with DevOps platforms based on customer demand with each successive update to its platform.

Torres said early tensions between ITIL and DevOps proponents are subsiding in favor of a more practical approach, which seeks to leverage the best of both approaches. The goal is to enable developer agility in a way that internal IT teams can support more reliably. The goal is to reduce to mean time to resolution whenever an incident arises from a recent change to the IT environment in real-time, noted Torres.

Overall, the rate at which IT management platforms have shifted to the cloud has accelerated in the wake of the COVID-19 pandemic. With more IT professionals working from home, remotely accessing management platforms via virtual private networks (VPNs) has shown itself to be cumbersome at best.

Longer-term, that shift to cloud will also enable IT teams to leverage artificial intelligence (AI) and robotic process automation tools to automate most low-level IT processes. The challenge IT organizations face today is on-premises IT management platforms are not able to aggregate enough data to automate IT processes reliably. However, AI capabilities will not replace IT administrators, but rather free them to address a wide range of more complex challenges, said Torres.

Arguably, more IT organizations are re-evaluating their existing IT processes now than at any other point in IT history. The shift to supporting employees who will be working from home much more frequently is seminal moment. Each IT organization will need to determine its own best path forward, but the one thing that is for certain now is, like it or not, there is no going back.

Why Cloud Security Reading Still Matters

Cloud environments are not secure by default. Misconfigured S3 buckets, over-permissive IAM roles, unencrypted data in transit, and weak secrets management are recurring causes of real-world breaches. Whether you run workloads on AWS, Azure, or Google Cloud Platform, understanding the underlying security principles — not just the vendor dashboards — is what separates a reactive team from a proactive one.

Books offer something documentation and blog posts rarely do: a structured, end-to-end treatment of a subject. The titles below range from vendor-neutral fundamentals to practitioner-focused guides on cloud-native security architecture. They are suitable for beginners building a foundation and for experienced engineers looking to formalise or deepen their knowledge.

What to Look for in a Cloud Security Book

Before committing time to a book, consider the following criteria:

• Publication date: Cloud services evolve rapidly. A book more than three to four years old may reference deprecated services or outdated compliance frameworks. Prioritise recent editions or books that focus on durable principles over specific console walkthroughs.
• Author credibility: Look for practitioners with demonstrable experience — security architects, penetration testers, or engineers who have built and defended production cloud environments.
• Coverage of the shared responsibility model: Any credible cloud security book should clearly explain what the cloud provider secures versus what the customer is responsible for. This model underpins everything else.
• Practical exercises: Lab-based or hands-on chapters accelerate retention and give you directly applicable skills.
• Multi-cloud or platform-specific focus: Decide whether you need a vendor-neutral treatment (useful for architects and decision-makers) or a deep dive into a specific platform (useful for engineers operating daily in AWS, Azure, or GCP).

Core Topic Areas Worth Prioritising

A well-rounded cloud security reading list should cover the following domains. Use this as a checklist when evaluating which gaps in your knowledge to fill next:

Identity and Access Management (IAM)

Excessive permissions and poorly scoped roles are among the most exploited attack surfaces in cloud environments. Books and resources in this area cover least-privilege design, role-based access control (RBAC), attribute-based access control (ABAC), federation, single sign-on (SSO), and privileged access management (PAM).

Network Security in the Cloud

Traditional perimeter-based security does not translate directly to cloud-native architectures. Core concepts include virtual private clouds (VPCs), security groups, network access control lists (NACLs), private endpoints, zero-trust networking, and micro-segmentation.

Data Protection and Encryption

Encryption at rest and in transit, key management services (KMS), secrets management tools (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault), and data classification frameworks are essential knowledge for anyone handling sensitive workloads in the cloud.

Compliance and Governance

Regulatory requirements such as GDPR, HIPAA, PCI-DSS, and SOC 2 impose specific technical and organisational controls. Understanding how cloud services map to these frameworks — and where responsibility remains with the customer — is critical for compliance-driven teams.

Incident Response and Threat Detection

Cloud environments generate large volumes of logs and telemetry. Books in this area cover SIEM integration, cloud-native logging services (AWS CloudTrail, Azure Monitor, GCP Cloud Audit Logs), threat detection, and incident response playbooks tailored to cloud scenarios.

DevSecOps and Secure CI/CD

Shifting security left means integrating static analysis, dependency scanning, container image scanning, and infrastructure-as-code (IaC) policy checks into the development pipeline. This is increasingly a required competency for platform and DevOps engineers, not just security specialists.

Building Your Reading List: A Suggested Approach

Rather than reading exhaustively, map books to your current role and the most pressing security risks in your environment:

1. Assess your knowledge gaps. Use a framework such as the CSA Cloud Controls Matrix or the MITRE ATT&CK Cloud matrix to identify where your understanding is weakest.
2. Match books to gaps. Select titles that directly address those areas rather than reading generalist books cover to cover.
3. Combine reading with practice. Set up a personal cloud lab (free-tier accounts on AWS, Azure, or GCP are sufficient for most exercises) to apply concepts as you read.
4. Revisit titles as services evolve. Check whether newer editions exist before starting a book you last read two or more years ago.

Supplementary Resources Beyond Books

Books are one component of a broader learning strategy. Pair your reading with:

• Vendor security documentation: AWS Well-Architected Framework Security Pillar, Microsoft Azure Security Benchmark, and Google Cloud Security Foundations Blueprint are all freely available and regularly updated.
• Cloud Security Alliance (CSA) publications: The CSA publishes guidance documents, research papers, and the Cloud Controls Matrix at no cost.
• Certification study materials: Certifications such as AWS Certified Security – Specialty, CCSP (Certified Cloud Security Professional), and CompTIA Cloud+ come with structured syllabi that complement book-based learning.
• Threat intelligence feeds and breach post-mortems: Reading real-world incident reports (many published by cloud vendors and security firms) reinforces the practical relevance of what you read in books.

Final Thoughts

Cloud security is not a one-time certification or a single book. It is an ongoing discipline that requires continuous learning as attack surfaces shift and cloud providers introduce new services. Building a deliberate reading habit — even one chapter per week — compounds into significant expertise over time. Start with the domain most relevant to your current work, apply what you learn in a lab environment, and expand from there.

If your organisation lacks in-house cloud security expertise, consider evaluating managed security service providers (MSSPs) with proven cloud practices as a complementary measure while your team builds capability.

Why Azure DevOps Skills Matter

Microsoft Azure is one of the most widely adopted cloud platforms in enterprise environments. For DevOps engineers, knowing how to deploy, test, monitor, and automate workloads on Azure is increasingly a baseline expectation rather than a differentiator. Building these skills also maps directly to Microsoft’s AZ-400: Designing and Implementing Microsoft DevOps Solutions certification exam, which validates expertise across the full DevOps lifecycle on Azure.

The following sections break down the three core competency areas that Azure DevOps engineers need to master.

1. Azure Infrastructure Operations

Modern cloud deployments depend on automated, repeatable infrastructure provisioning. Manual server configuration doesn’t scale and introduces human error — both of which undermine the uptime guarantees customers expect. Azure DevOps engineers must understand how to manage infrastructure programmatically rather than through the portal alone.

Key Skills in This Area

• DevOps lifecycle fundamentals: Understanding the relationship between planning, development, delivery, and operations in a continuous loop.
• Infrastructure as Code (IaC): Using tools such as Azure Resource Manager (ARM) templates, Bicep, or Terraform to define and version infrastructure declaratively.
• Cloud security best practices: Applying the principle of least privilege, managing secrets with Azure Key Vault, and enforcing network security groups and policies.
• Repeatable deployments: Building deployment pipelines that can recreate environments consistently across development, staging, and production.

The goal is infrastructure that behaves like code — reviewable, version-controlled, and testable before it ever touches production.

2. Agile Development with Azure

Delivering high-quality software quickly requires marrying Agile project management with automated CI/CD pipelines. In the Azure ecosystem, this typically means combining Azure Boards for work tracking with Azure Pipelines for build and release automation.

Continuous Integration with Python Tooling

A common CI workflow for Python-based projects on Azure involves the following stages in a pipeline:

1. Linting with pylint — catches syntax errors and enforces style standards before a single test runs.
2. Unit and integration testing with pytest — verifies business logic and catches regressions automatically on every commit.
3. Build artifact creation — packages the application for deployment after all checks pass.

Continuous Delivery with Azure Pipelines

Pairing IaC with Azure Pipelines enables end-to-end Continuous Delivery: infrastructure and application code are deployed together through the same automated workflow. This eliminates environment drift and reduces the time between a code commit and a production release.

# Example: minimal azure-pipelines.yml trigger for a Python app
trigger:
  - main

pool:
  vmImage: 'ubuntu-latest'

steps:
  - script: pip install pylint pytest
    displayName: 'Install dependencies'

  - script: pylint src/
    displayName: 'Lint with pylint'

  - script: pytest tests/
    displayName: 'Run tests with pytest'

The snippet above is illustrative. Real-world pipelines add caching, environment-specific variable groups, approval gates, and deployment stages.

3. Ensuring Quality Releases (QA and Monitoring)

Shipping to the cloud is not the end of the DevOps loop — it’s the beginning of the operations phase. Released applications must be continuously evaluated for performance, correctness, and reliability.

Testing Inside and Outside the Pipeline

Quality assurance for cloud applications typically spans two phases:

• In-pipeline testing: Tests that run as pipeline stages before or immediately after deployment — blocking a release if they fail.
• Post-deployment testing: Tests that run against the live environment to validate real-world behaviour, including load and end-to-end UI scenarios.

Common tooling includes:

• Selenium — browser-based UI testing, useful for validating web frontends such as eCommerce applications.
• Postman / Newman — API testing and contract validation against REST endpoints, runnable from the command line inside a pipeline.

Monitoring and Alerting with Azure-Native Tools

Once an application is live, observability is non-negotiable. Azure provides two key services for this:

• Azure Monitor: Collects metrics and logs from Azure resources, and triggers alerts based on thresholds you define — such as error rate spikes or latency increases.
• Azure Log Analytics: A query-driven workspace for analysing custom and platform log data using Kusto Query Language (KQL), enabling root-cause analysis and trend detection.

Building alerting rules and dashboards as part of the deployment process — rather than as an afterthought — is a hallmark of mature DevOps practice.

Mapping to the AZ-400 Certification

The three competency areas above align closely with the domains tested in Microsoft’s AZ-400 exam: DevOps transformation planning, source control, CI/CD pipeline implementation, dependency management, application infrastructure, and continuous feedback. Engineers who build practical experience in Azure infrastructure automation, pipeline development, and QA testing are well-positioned to sit the exam.

Review the official AZ-400 exam skills outline on Microsoft Learn for the current objectives before studying.

Summary

Azure DevOps engineering requires depth across three interconnected domains: infrastructure automation and security, Agile-driven CI/CD pipelines, and systematic quality assurance combined with production monitoring. Developing hands-on proficiency in tools like Azure Pipelines, ARM/Bicep/Terraform, Selenium, Postman, Azure Monitor, and Log Analytics builds the practical foundation needed for both day-to-day engineering work and the AZ-400 certification.

Sponsored For some time now, DevSecOps has looked like the solution to the puzzle of how to get cloud applications out the door without turning throwing security on to the fire. Instead of trying to retrofit complex security policies to work in a hierarchical development pipeline, security becomes a distributed problem that every coder is asked to address.

DevSecOps solutions are by nature designed to be preventative. The idea is to remove complexity by baking robust security methodologies into software development from the earliest stages. Get it right from the outset, and reactive firefighting is greatly reduced.

Conveniently, this model – “shifting security left” to the coder rather than the expert in a fixed hierarchy – also makes sense when developing on cloud platforms that assume rapid deployment and collaboration. There is no development team, security team, or IT deployment team because they are one and the same person. In theory, that’s how security misconfigurations can be caught before they do harm. However, when it comes to cloud development, “shift left” is more talked about than practised. This situation has crept up on organisations that haven’t realised how programming culture has changed rapidly in the cloud era.

“There is a lack of control in this model. With the shift into cloud development and the fact that coders can always get a better answer of Stack Overflow and GitHub, it’s become practically impossible to track the supply chain. It’s a governance problem,” says Guy Eisenkot, the co-founder and vice president of product for a new Israeli-USA start-up, Bridgecrew. Security is now becoming a focal point of continuous development, he says, and this is a work in progress alongside priorities such as performance and features. The problem is that it is not enough to rely on tools alone.

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Eisenkot uses the example of an Elasticsearch cluster that has inadvertently been left in a publicly accessible state. Under today’s hierarchical model, security checking will flag this, and the issue is sent to a ticketing system where it will probably languish for hours until someone with the necessary access spots it. But it would be far more efficient to focus on prevention, with the misconfiguration as part of a CI or CD pipeline that immediately pushes the issue to the team that made the error. Armed with the alert, remediation can happen at compile time rather than runtime, which would mean scrambling back from a potentially serious mistake.

“It’s not simply that the tools are not enough, it’s the fact that too many of them are reporting alerts and sending them to Slack channels, Jira tickets and other queues instead of going to developer’s desktop and CI tools,” says Eisenkot.

This is where Infrastructure as Code (IaC), a methodology that makes it easier for security to be “everyone’s business”, comes in to play. However, “easier” does not necessarily mean “perfect”. Let’s take a closer look.

InterrogatingIAC

The issue of configuration has been brought into sharp focus by the rise of IaC and its implementation, using tools such as Terraform. IaC’s promise is to remove the need for manual configuration and a jumble of individual console tools, turning provisioning of cloud and data centre services into machine-readable code which is bundled into automated workflows or templates.

IaC configuration to a cloud platform such as AWS can be either push (direct to the server using a configuration tool such as Chef or Puppet) or pull in the case of Terraform via a configuration server to the provider’s infrastructure. In most cases, Terraform treats resources as ‘immutable’, that is, it wipes a resource and starts again rather than making a configuration change by fiddling with an existing setup.

In the cloud era where developers use multiple platforms and timescales that have shrunk to hours, DevSecOps still asks a lot of developers to get it right first time, argues Eisenkot.

“It suddenly doesn’t take six months to deploy a change to an application, it takes a few minutes. This speed is a blessing in disguise because it creates challenges when we deploy software very fast. Where do we make the compromises?”

As he explains in a separate article on the company’s approach to Infrastructure as Code (IaC) misconfiguration, just having security tools to spot and remediate problems is not the same as managing development risk. The first problem is that anyone with the job of managing compliance and risk cannot easily see what’s going on in an environment where developers are often handed amazing amounts of freedom.

“There are blind spots,” says Eisenkot. “How do you ensure you know what’s going on in your environment? The idea that we know everything that’s going on in our architecture is an assumption we can’t make any more.”

But at least four in ten IaC templates contain a misconfiguration, according to Bridgecrew’s analysis, which often slip through into runtime vulnerabilities that expose resources. The tools to provision but also misconfigure cloud security have been successfully decentralised, but leaving the expertise to manage this problem centrally managed where they are becoming ineffective. The upshot is that many organisations have adopted IaC while lacking the means to manage it securely.

Bridgecrew’s approach to this problem is to build a new type of platform that oversees the entire development pipeline and its effect on security. In essence the system sanity-checks what developers are doing and issues pull requests before problems arise. This is achieved through overlapping capabilities that integrate with Amazon AWS, Microsoft Azure, and Google Cloud Platform (GCP), as well as violations in CloudFormation, Kubernetes and Terraform.

The first element is the ability to scan for IaC and other cloud misconfiguration and policy violations – ideally during build time so that errors don’t make it to deployed infrastructure. This process can also generate compliance reports to meet standards such as PCI. Importantly, uncovering misconfiguration and unauthorised changes at this stage makes fixing them much quicker and easier.

“Bridgecrew is deployed by running a CloudFormation or Terraform template on the subject cloud account. The template creates a read-only role on the subject IaaS account. This role enables it to perform scheduled scans of configuration states and continuously evaluate their latest states,” says Eisenkot.

For some organisations, this can turn into an almost archaeological task that involves pulling out problems in templates dating back years. Eisenkot uses the example of encryption by default, which wasn’t enabled on some database packages until three years ago. Uncovering those issues are easy wins that can happen on day one.

The basic element of Bridgecrew’s fix strategy is playbooks, automation and remediation scripts that make it easy to address common problems across different cloud platforms that might otherwise require several manual steps. A useful starting point, yes, but they have limitations in real-world development, Eisenkot acknowledges. Today, cloud deployment is highly automated, which means there is a risk that changes made by a playbook can easily be undone by a subsequent change.

The company has also put time into open source tools such as Checkov, an IaC static analysis tool which can be used to spot misconfigurations in Terraform, CloudFormation, and Kubernetes. Other Bridgecrew open source tools include the AWS Least Privilege Terraformer AirIAM, which generates a template designed to limit permissions for identity and access management to those which are strictly necessary; and Terragoat, “a vulnerable-by-design learning tool for experimenting with building secure cloud configuration pipelines“.

If scanning IaC templates sounds straightforward, Eisenkot is candid about the challenges of building a platform that can do this without creating its own headaches.

“It becomes extremely complicated when trying to build a data model that depicts how configuration will eventually manifest based on the prescribed plan. It is imperative to extract its variables and dependencies against the entire code base. Without this step, scans could result in both false positives in the form of configurations that don’t extract all their dependencies or false negatives that are not identified due to misinterpretation of IaC plan,” he says.

Ultimately, handing developers a new platform to manage bad decisions turned out to be only part of the solution. “We learned that it’s not only about giving developers the panic button, but we also had to educate them on making the right decisions when building new infrastructure in the public cloud.” More fundamental still was to educate developers to avoid bad configurations before they happened.

“By making sure those end up at a developer’s pull-request or build report, we were able to provide actionable advice on how to troubleshoot bad configurations and prevent them from getting into production in the first place.”

In a decade, the cloud has gone from powerful resource to invisible problem and, more recently, a risky liability. Having acquired a new set of tools, developers have learned that they must impose some discipline on how they use it. The solution, then, is not more tools to fix security problems but better integration of security as an ethos.

What Is the Cloud Computing Stack?

The cloud computing stack refers to the layered set of services that together form a complete cloud offering. Each layer builds on the one below it, abstracting away progressively more infrastructure complexity. The three canonical layers are:

• Infrastructure as a Service (IaaS) – Raw compute, networking, and storage resources delivered over the internet. Teams retain full control over the operating system and above.
• Platform as a Service (PaaS) – A managed environment for developing, testing, and deploying applications without managing the underlying infrastructure. The provider handles the OS, runtime, middleware, and scaling.
• Software as a Service (SaaS) – Fully managed applications delivered via a browser or API. The provider is responsible for everything from infrastructure to the application layer itself.

These three layers are often visualized as a stack because each higher tier consumes services from the tier below it. A SaaS product, for example, typically runs on PaaS or IaaS underpinnings provided by a hyperscaler.

Layer 1: Infrastructure as a Service (IaaS)

IaaS gives engineers the most control over their environment. Typical IaaS resources include virtual machines, block and object storage, virtual networking, load balancers, and bare-metal servers. The consumer manages everything from the guest operating system upward—patching, runtime configuration, security hardening, and application deployment all remain the team’s responsibility.

Common use cases:

• Lift-and-shift migration of legacy applications
• High-performance computing and batch workloads
• Custom network topologies and security configurations
• Disaster recovery and backup infrastructure

Key IaaS providers: Amazon Web Services (EC2, S3, VPC), Microsoft Azure (Virtual Machines, Blob Storage, VNet), Google Cloud Platform (Compute Engine, Cloud Storage), IBM Cloud, Rackspace, and OVHcloud.

Layer 2: Platform as a Service (PaaS)

PaaS removes the burden of OS and middleware management, letting development teams focus on writing and deploying code. The platform handles auto-scaling, load balancing, and often integrates CI/CD pipelines, managed databases, and monitoring out of the box.

Common use cases:

• Web and mobile application development and hosting
• Microservices deployment on managed Kubernetes clusters
• Serverless function execution
• Managed database and messaging services

Key PaaS providers: AWS Elastic Beanstalk and Lambda, Azure App Service and Azure Functions, Google App Engine and Cloud Run, Heroku (Salesforce), IBM Cloud Foundry, and SAP Business Technology Platform.

Layer 3: Software as a Service (SaaS)

SaaS is the most abstracted layer. End users interact with a fully managed application—there is no infrastructure or runtime to configure. From a DevOps perspective, SaaS is relevant both as a consumer (using tools like GitHub, Datadog, or PagerDuty) and as a target delivery model when building products for customers.

Common use cases:

• CRM and ERP systems
• Collaboration and productivity tools
• Monitoring, observability, and ITSM platforms
• Developer tooling (source control, CI/CD, issue tracking)

Key SaaS providers: Salesforce (CRM), Microsoft 365, Google Workspace, SAP, Oracle Cloud Applications, and Avaya cloud communications.

Major Cloud Vendors Across the Stack

Most hyperscalers operate across all three layers simultaneously. Below is a concise overview of the leading vendors and their primary strengths:

Choosing the Right Layer for Your Workload

Selecting the appropriate cloud layer is a trade-off between control and operational overhead:

• Choose IaaS when you need maximum control over the OS, network configuration, or custom hardware (e.g., GPU instances, specific compliance requirements).
• Choose PaaS when developer velocity matters more than infrastructure control—managed runtimes, auto-scaling, and built-in CI/CD integrations reduce toil significantly.
• Choose SaaS when the capability is not a core differentiator for your business and a third-party product meets your requirements—let the vendor handle operations entirely.

Many production architectures combine all three layers. A typical e-commerce platform might run compute on IaaS virtual machines or containers (IaaS/PaaS), use a managed database service (PaaS), and integrate a SaaS CRM for customer data—all within the same cloud account.

Market Context and Growth Drivers

Adoption of cloud services has accelerated across every industry vertical, driven by several converging factors:

• Operational agility: Cloud provisioning reduces the time from idea to running infrastructure from weeks to minutes.
• Cost model shift: Moving from capital expenditure (CapEx) to operational expenditure (OpEx) improves financial flexibility.
• Global reach: Hyperscaler regions and edge locations allow teams to deploy closer to end users worldwide.
• Managed services breadth: Vendors continually expand their PaaS and SaaS catalogues, reducing the engineering effort required for common capabilities such as databases, message queues, and ML inference.
• Regulatory and compliance tooling: Cloud providers now offer certified environments for regulated industries (HIPAA, FedRAMP, ISO 27001, PCI DSS), lowering the barrier for enterprise adoption.

Key Takeaways for DevOps Teams

• Understand which layer each service in your architecture sits on—it directly determines your shared responsibility boundary with the vendor.
• Evaluate managed PaaS options before defaulting to IaaS; reduced operational overhead often outweighs the loss of low-level control.
• Multi-cloud strategies can avoid vendor lock-in but introduce their own complexity—ensure your team has the tooling (Terraform, Kubernetes, etc.) to manage it consistently.
• Security and compliance ownership shifts depending on the layer: IaaS gives you the most responsibility; SaaS gives you the least. Map this explicitly in your threat model.

Oracle Throws Down the Gauntlet on Cloud Pricing

Oracle made a pointed move against Amazon Web Services (AWS) by launching the Oracle Cloud Workload Cost Estimator, a tool designed to compare the total cost of running workloads on Oracle Cloud Infrastructure (OCI) versus AWS. The implicit — and explicit — message: Oracle believes its cloud is cheaper, and it wants enterprise and DevOps teams to do the math themselves.

The tool was positioned as an “apples-to-apples” comparison, covering the four cost dimensions that most directly affect cloud operating budgets for high-performance workloads:

• Compute – virtual machine and CPU costs
• Storage – block and object storage pricing
• I/O operations per second (IOPs) – storage throughput costs
• Data egress – the cost of transferring data out of the cloud

Data Egress: The “Wild Card” in Cloud Cost Comparisons

Of the four dimensions, Oracle singled out data egress as the factor most likely to catch organisations off guard. Moving data into a cloud provider is typically free or very low-cost. Moving data out is where traditional providers apply significant markups — a practice sometimes called “egress tax” in the industry.

Oracle’s own blog post on the calculator highlighted the contrast directly:

“Traditional cloud companies charge a markup after a given amount of data flows out. So once you hit that magic monthly number — 1 GB for AWS — data egress charges kick in. At Oracle, the meter doesn’t start running until after 10,000 times more data egress — or 10 TB — per month.”

For teams running data-intensive workloads — analytics pipelines, media delivery, machine learning training jobs, or large-scale database replication — egress costs can become a significant and underestimated line item. This is the specific use case Oracle was targeting with the calculator.

How the Oracle Cloud Workload Cost Estimator Works

The estimator was designed to let teams enter their workload specifications and receive a side-by-side cost breakdown comparing OCI and AWS. Rather than forcing users to manually cross-reference pricing pages from two different cloud providers — each of which uses different terminology and pricing structures — the tool attempts to normalise the comparison into equivalent configurations.

Oracle’s stated goal was to demonstrate that for many high-performance enterprise applications, OCI delivers a lower total cost of ownership (TCO). DevOps teams evaluating cloud providers for latency-sensitive or data-heavy workloads may find this type of structured comparison useful, regardless of which vendor they ultimately choose.

Cloud Cost Calculators Across the Major Providers

Oracle is not the only cloud vendor offering a cost estimation tool. All three major hyperscalers provide pricing calculators, though they vary in scope and approach:

• AWS Pricing Calculator – Allows teams to model environments and estimate costs across AWS services, including the impact of Reserved Instances and Savings Plans.
• Microsoft Azure Pricing Calculator – Includes support for comparing pay-as-you-go versus reserved pricing, and factors in Azure Hybrid Benefit for organisations with existing Microsoft licenses.
• Google Cloud Pricing Calculator – Covers Compute Engine, Cloud Storage, Cloud SQL, and other GCP services, with estimates for sustained use discounts automatically applied.

What distinguishes Oracle’s approach is that it was explicitly designed as a cross-vendor comparison tool, rather than simply helping users estimate their own spend within a single provider’s ecosystem. That framing is inherently competitive, and teams should weigh Oracle’s claims accordingly — the assumptions baked into any vendor-produced comparison tool will naturally favour that vendor.

Context: Oracle’s Cloud Ambitions in 2020

At the time of this announcement, Oracle was actively working to expand its cloud presence. The company had announced plans to hire approximately 2,000 cloud-focused employees, a signal of its intent to compete more aggressively with AWS and Microsoft Azure. However, the company had also undergone significant layoffs in the preceding period, and several senior executives had departed — reflecting the internal challenges of pivoting a legacy enterprise software business toward cloud-native infrastructure.

The launch of the cost estimator in May 2020 also coincided with the early months of the COVID-19 pandemic, a period in which many organisations were scrutinising cloud spend more carefully and reconsidering vendor lock-in. Oracle’s timing positioned the tool as a response to that environment, framing OCI as a cost-efficient alternative worth evaluating during a period of economic pressure.

What DevOps Teams Should Take Away

Vendor-produced calculators are a useful starting point, but they should be treated as one input among many. When evaluating cloud costs for a real workload, consider the following:

• Model your actual egress volume. If your workload moves large amounts of data out of the cloud regularly, egress pricing deserves careful scrutiny across all candidate providers.
• Use multiple calculators. Run the same workload specification through each provider’s own tool and compare results. Differences in how you model the workload can significantly affect outcomes.
• Account for operational costs. Raw infrastructure pricing is only part of TCO. Tooling, managed services, support tiers, and staff familiarity with a given platform all affect real-world costs.
• Validate vendor claims independently. Any comparison produced by a vendor will reflect assumptions favourable to that vendor. Seek independent benchmarks or run proof-of-concept workloads in parallel where possible.

Video: Oracle Cloud Infrastructure Overview

Combining IAM with Docker and Kubernetes for Competitive Advantage

As enterprises accelerate digital transformation and shift more workloads to the cloud, the need to deliver secure, scalable identity and access management (IAM) faster has never been more urgent. One effective strategy is adopting a multi-cloud DevOps deployment model that leverages containerization and orchestration technologies — specifically Docker and Kubernetes. This approach enables organizations to speed time to market, scale dynamically for peak demand, and reduce operational overhead.

Why Traditional IAM Struggles to Keep Up

IAM is fundamental to connecting users securely to applications and services across any device or location. However, traditional IAM platforms were not designed for today’s pace of change. Common pain points include:

• Slow deployment cycles: Monolithic IAM architectures are difficult to update, requiring lengthy release windows that slow down new service rollouts.
• Limited scalability: Traditional systems struggle to scale rapidly in response to sudden spikes in user demand or new business requirements.
• High operational cost: Manual configuration and maintenance of on-premises IAM infrastructure consume significant IT resources.
• Lack of cloud-readiness: Many legacy IAM solutions do not natively support multi-cloud or hybrid environments.

Modern IAM must be rearchitected for agility — supporting automated, continuous delivery pipelines and integrating natively with DevOps toolchains across any cloud provider.

Docker and Kubernetes: The Foundation of Modern IAM Deployment

Docker

Docker is a containerization platform that packages an application — along with its dependencies, libraries, and configuration — into a single portable container image. Containers run consistently across development, staging, and production environments, eliminating the classic “works on my machine” problem. For IAM deployments, this means identity services can be built once and deployed reliably on any infrastructure.

Kubernetes

Kubernetes is an open-source container orchestration system originally developed by Google. It automates the deployment, scaling, and management of containerized applications across clusters of machines. Key capabilities relevant to IAM include:

• Automated scaling: Kubernetes scales IAM service pods up or down in response to load, ensuring availability during traffic spikes.
• Self-healing: Failed containers are automatically restarted or rescheduled, maintaining high availability.
• Rolling updates: New IAM versions can be deployed with zero downtime using rolling update strategies.
• Multi-cloud portability: Kubernetes abstracts underlying cloud infrastructure, enabling the same IAM deployment to run on AWS, Google Cloud, or Microsoft Azure.

Together, Docker and Kubernetes give DevOps teams the tooling to deliver IAM services faster and more reliably, whether on-premises, in a hybrid setup, or across multiple cloud providers simultaneously.

The Business Case: DevOps and Identity Together

Large enterprises routinely face identity-related bottlenecks: slow time-to-market for new services, high deployment costs, and a shortage of specialized IAM expertise. A DevOps-native IAM platform directly addresses these challenges by enabling:

• Automated multi-cloud deployments: Infrastructure-as-code and Helm charts (or equivalent manifests) allow repeatable, version-controlled IAM deployments.
• Reduced configuration overhead: Pre-built container images and deployment templates minimize manual setup time.
• Faster iteration: CI/CD pipelines can include IAM configuration changes, allowing identity policies to evolve alongside application code.
• Support for millions of identities: Horizontal scaling via Kubernetes supports enterprise-grade identity volumes for customers, employees, and IoT devices.

“Microservice architectures, DevOps and agile development as new paradigms, and container-based deployments are changing the way IT is done. There are obvious benefits in moving to such architectures, such as increased flexibility and lower cost of operations, but also increased security.”

— KuppingerCole

ForgeRock’s Multi-Cloud DevOps IAM Approach

ForgeRock has designed its identity platform to be natively deployable via Docker and Kubernetes, targeting organizations that need enterprise IAM at cloud speed. Key aspects of the ForgeRock DevOps model include:

• Cloud-agnostic deployment: ForgeRock supports deployment on AWS, Google Cloud Platform, and Microsoft Azure without requiring vendor lock-in.
• Production-grade reference architectures: Pre-validated deployment blueprints reduce the design and testing burden for enterprise teams.
• Benchmarked performance data: Organizations can evaluate cloud deployment options against published benchmark results to optimize for cost, performance, and availability.
• Rapid identity provisioning: The containerized model is designed to support deploying millions of identities in minutes rather than days.

According to ForgeRock, customers adopting this DevOps deployment model have accelerated project timelines by three to six months and reduced implementation costs by approximately 25%. These figures are as reported by ForgeRock and have not been independently verified.

Practical Benefits for DevOps and Platform Engineering Teams

For DevOps and platform engineers evaluating an IAM solution, containerized deployment models offer concrete workflow advantages:

• GitOps compatibility: IAM deployment manifests can be stored in version control and applied through standard GitOps pipelines.
• Environment parity: The same container image runs in dev, test, and production, reducing environment-specific bugs.
• Faster disaster recovery: Stateless IAM pods can be relaunched rapidly from container images, improving RTO (Recovery Time Objective).
• Observability integration: Kubernetes-native monitoring (Prometheus, Grafana) can be applied to IAM service health and performance metrics.

Getting Started

Organizations evaluating a DevOps-oriented IAM strategy should consider the following steps:

1. Audit existing IAM architecture for containerization readiness and multi-cloud compatibility.
2. Evaluate IAM vendors based on native Kubernetes support, published deployment documentation, and available reference architectures.
3. Run proof-of-concept deployments on your target cloud platform(s) using vendor-provided Helm charts or Operator patterns.
4. Integrate IAM deployment into existing CI/CD pipelines to validate continuous delivery workflows.
5. Benchmark performance and cost across cloud providers before committing to a production topology.

ForgeRock provides multi-cloud and DevOps IAM resources, including on-demand webinars covering how to run IAM using Docker and Kubernetes. Refer to the ForgeRock website for current documentation and learning resources.

Containers and Cloud: A Natural Partnership

Containers have become a cornerstone of modern application development and deployment. Their value proposition is well understood: they package an application and its dependencies into a portable unit that can run consistently across development laptops, on-premises servers, and multiple cloud environments. This portability is especially important as enterprises pursue hybrid and multi-cloud strategies, moving workloads between providers or between cloud and on-premises infrastructure without costly re-engineering.

But building and operating a mature containerized environment at scale is not trivial. It requires specialized expertise in orchestration, networking, storage, security, and CI/CD pipelines. That skills gap has created real friction for organizations that want the benefits of containers without investing heavily in building internal platform teams from scratch.

What Is Containers-as-a-Service (CaaS)?

Containers-as-a-Service (CaaS) is a managed cloud service model that abstracts the underlying infrastructure required to run containers. Rather than provisioning and maintaining the orchestration layer — including the control plane, node management, networking, and storage — organizations consume these capabilities as a managed service from a cloud provider or platform vendor.

CaaS sits between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Users retain significant control over how their containers are configured and deployed, but they offload operational responsibility for the underlying container runtime and orchestration platform. In practice, this usually means a managed Kubernetes service, a managed Docker environment, or both.

Common CaaS offerings from major cloud providers include:

• AWS Elastic Container Service (ECS) and Elastic Kubernetes Service (EKS)
• Azure Kubernetes Service (AKS)
• Google Kubernetes Engine (GKE)

CaaS Adoption Is Rising Fast

According to the Flexera 2020 State of the Cloud Report, CaaS is no longer a niche offering. Among the 750 IT executives surveyed, 53 percent reported using CaaS — placing it as the second-ranked PaaS category in use among cloud-driven enterprises, up from sixth place the previous year. Only Database-as-a-Service ranked higher, used by 62 percent of respondents.

In terms of year-over-year growth, CaaS ranked second among the fastest-growing cloud technologies, with 17 percent annual growth — just behind IoT services at 21 percent and level with machine learning and AI services.

Adoption of specific CaaS products reflects this trend:

• AWS ECS/EKS: 54 percent adoption (up from 44 percent the prior year), with another 24 percent planning adoption.
• Azure Container Service: 46 percent adoption (up from 28 percent).
• Google Kubernetes Engine: 24 percent adoption (up from 15 percent).

The survey authors attribute this acceleration to organizations’ growing interest in leveraging containers to speed deployment, scale operations, and increase workload efficiency in the cloud.

Why CaaS Matters: Balancing Speed and Control

One of the key value propositions of CaaS is that it preserves developer and operations control over container configuration while reducing the burden of managing the platform itself. As noted in an overview by Eric Olsson at Scalyr, the evolution toward containers is part of a broader acceleration in software delivery:

“Agile methodologies shortened the development and testing pieces while cloud computing has shortened the deployment piece. Continuous integration (CI) and continuous delivery (CD) further improved the time it takes to get a feature to customers. In addition to these, containers can give you a more powerful and flexible solution. But with all these pieces come additional configuration and complexity.”

Eric Olsson, Scalyr

CaaS helps manage that added complexity by delivering a pre-integrated platform for container orchestration, allowing teams to focus on writing and shipping software rather than maintaining Kubernetes clusters.

Key Challenges Slowing Container Adoption

Despite growing adoption, the Flexera survey identified several significant barriers that organizations still face when working with containers:

• Lack of internal expertise: Cited as a major challenge by 41 percent of respondents, with another 38 percent calling it a moderate challenge. Container orchestration, security, and networking require skills that many teams are still building.
• Migrating traditional applications: 34 percent identified this as a major issue. Containers are optimized for microservices architectures, while many existing enterprise applications are monolithic and were not designed with container portability in mind. Retrofitting these applications requires significant refactoring effort.
• Security concerns: Also cited as a major challenge by 34 percent. Container security encompasses image scanning, runtime protection, secrets management, and network policy — all areas requiring deliberate investment.
• Provider expertise gaps: More than one in four respondents (26 percent) noted that even their service providers lack sufficient expertise in container technology, suggesting that the ecosystem as a whole is still maturing.

The survey authors note that these challenges largely reflect the relative recency of widespread container adoption. As the tooling, training, and managed service options mature, many of these barriers are expected to lower.

Docker and Kubernetes Remain the Foundation

Underlying most containerization efforts are two technologies that have become de facto standards in the industry. According to the Flexera 2020 survey:

• Docker: Used by 65 percent of organizations surveyed, with another 14 percent planning adoption. Docker remains the dominant container runtime and image format.
• Kubernetes: Used by 58 percent of organizations, with 22 percent planning to adopt it. Kubernetes has become the leading container orchestration platform, and most CaaS offerings — including EKS, AKS, and GKE — are built on top of it.

The widespread use of these open-source technologies means that organizations investing in CaaS today are building on a well-supported, vendor-neutral foundation, which reduces lock-in risk compared with proprietary PaaS platforms.

Is CaaS the Right Choice for Your Organization?

CaaS is not a universal solution, but it is a pragmatic one for many organizations. Teams that are beginning their container journey and do not yet have deep Kubernetes expertise can use a managed CaaS offering to get running quickly, then build operational knowledge progressively. Organizations with strong platform engineering teams may prefer more control over their Kubernetes deployment, running self-managed clusters on IaaS infrastructure.

For enterprises running hybrid or multi-cloud workloads, CaaS offerings can also provide a consistent developer experience across environments — particularly when combined with tools like Helm, Terraform, or GitOps workflows that standardize deployments across providers.

The central question is not whether to use containers — at this point, the industry trend is clear — but how much of the operational platform you want to manage yourself versus delegate to a cloud provider. CaaS represents a sensible middle path for most enterprises: enough abstraction to reduce operational burden, enough control to maintain flexibility.

Conclusion

Containers have moved from an advanced practice to a mainstream enterprise requirement, and CaaS is emerging as the practical delivery mechanism for organizations that want container benefits without standing up their own orchestration infrastructure. Adoption data from the Flexera 2020 State of the Cloud Report confirms that CaaS is growing rapidly, driven by real demand across enterprises of all sizes. Challenges around expertise and application migration remain, but the managed service model is helping lower the barrier to entry — and that trend is likely to continue as Docker, Kubernetes, and the broader cloud-native ecosystem continue to mature.

What Is ksqlDB?

ksqlDB (previously marketed as KSQL) is the streaming SQL engine built on top of Apache Kafka. It lets developers write continuous SQL queries against Kafka topics without writing Java or Scala code, making real-time stream processing accessible to a much broader engineering audience. Common use cases include:

• Stream-to-stream joins — correlate events from two or more Kafka topics in real time.
• Aggregations — compute rolling counts, sums, or averages over time windows.
• Transformations and filtering — reshape, enrich, or drop events as they flow through a pipeline.
• Data enrichment — look up reference data stored in ksqlDB tables and join it with live streams.

Queries run on the ksqlDB Server engine, which handles all reads and writes to and from the underlying Kafka cluster. Developers interact with the engine either through a SQL query editor in the Confluent Control Center UI or directly via the ksqlDB REST API.

General Availability on Confluent Cloud

Confluent announced that ksqlDB moved from preview to production-ready status on Confluent Cloud, available to all customers on usage-based billing plans. The managed service removes the operational overhead of deploying and scaling ksqlDB Server clusters yourself. As Derek Nelson, senior product manager at Confluent, explained at the time of the announcement:

Confluent Cloud ksqlDB eliminates the innate complexities of operating infrastructure in production. We take care of running production-grade infrastructure at scale while you focus on solving business problems with ksqlDB. Transformations, stream-to-stream joins, aggregations, and data enrichment can all be accomplished using ksqlDB with just a few lines of SQL.

Derek Nelson, Senior Product Manager, Confluent

Dan Rosanova, senior group product manager at Confluent, also highlighted ksqlDB’s broader role in the platform when speaking to InfoQ:

ksqlDB is really a new kind of database that has been purpose-built for stream processing applications. We see ksqlDB as an integral service rounding out our cloud offering as a complete streaming platform, beyond Kafka, enabling end-to-end stream processing workloads. It complements our Connect service as well, which enables streaming connectivity to and from many services and platforms. These services all compose together to provide users an event streaming SaaS that they can use to build and run sophisticated and complete modern applications.

Dan Rosanova, Senior Group Product Manager, Confluent

Key Capabilities of the Managed ksqlDB Service

Running ksqlDB as a managed service on Confluent Cloud offers several advantages over self-hosting:

• No infrastructure management — Confluent provisions, scales, and monitors the ksqlDB cluster on your behalf.
• Integrated UI — The Confluent Control Center provides a graphical interface to create streams and tables, browse schemas, and submit continuous queries without leaving the browser.
• REST API access — All operations available in the UI are also available programmatically via the ksqlDB REST API, enabling CI/CD integration.
• Schema Registry integration — Streams and tables are schema-aware, enabling safer evolution of event formats over time.
• Kafka Connect complement — Confluent Cloud Connect can source or sink data to external systems, with ksqlDB handling in-flight transformations in between.

Multi-Cloud Support

Confluent Cloud — and by extension the managed ksqlDB service — is available on all three major public cloud providers:

• Amazon Web Services (AWS) — available since 2017.
• Google Cloud Platform (GCP) — available via the GCP Marketplace.
• Microsoft Azure — available via the Azure Marketplace.

This multi-cloud availability means teams can run ksqlDB workloads in the same region and cloud provider as the rest of their data infrastructure, minimising egress costs and latency.

A Practical Example: Streaming Data Pipeline

A common pattern is using Apache Kafka as the backbone of a data pipeline and ksqlDB to transform events before landing them in a downstream system (a data warehouse, search index, or operational database). Instead of writing a custom Kafka Streams application in Java, a developer can express the same logic in SQL:

-- Create a stream from a raw Kafka topic
CREATE STREAM orders_raw (
  order_id VARCHAR,
  customer_id VARCHAR,
  amount DOUBLE,
  currency VARCHAR
) WITH (
  KAFKA_TOPIC='orders',
  VALUE_FORMAT='JSON'
);

-- Filter and enrich: only USD orders above $100
CREATE STREAM high_value_usd_orders AS
  SELECT
    order_id,
    customer_id,
    amount,
    UCASE(currency) AS currency
  FROM orders_raw
  WHERE currency = 'USD' AND amount > 100
EMIT CHANGES;

The resulting high_value_usd_orders stream is itself a Kafka topic, ready to be consumed by downstream services or sinked to an external system via Kafka Connect.

Who Benefits Most?

As Bart Kersteter, a systems engineer at Confluent, noted at the time of the announcement, ksqlDB is particularly valuable for teams that need real-time stream processing but do not have deep JVM expertise:

KSQL is great for writing streaming data apps, especially for non-Java programmers. It is now fully supported and available in Confluent Cloud.

Bart Kersteter, Systems Engineer, Confluent

Data engineers already comfortable with SQL for batch processing can apply the same mental model to streaming workloads, significantly reducing the learning curve compared to the Kafka Streams DSL or Apache Flink.

Getting Started

To try the managed ksqlDB service on Confluent Cloud:

1. Sign up for a Confluent Cloud account at confluent.io/confluent-cloud.
2. Create a Kafka cluster in your preferred cloud region (AWS, GCP, or Azure).
3. Add a ksqlDB application from the Confluent Cloud console.
4. Use the built-in query editor to create your first stream and run a continuous SQL query.
5. Explore the official Confluent Cloud ksqlDB documentation for deeper configuration options.

Note: Pricing details and free-tier offers mentioned at the time of this announcement (April 2020) may have changed. Check the Confluent Cloud pricing page for current information.

Overview

Even against the backdrop of the COVID-19 pandemic in early 2020, cloud computing providers continued to push forward with strategic expansions and leadership changes that directly affected their channel ecosystems. Three announcements stood out: Rackspace’s deepened investment in the Middle East and Africa, IBM’s appointment of a new cloud leader under incoming CEO Arvind Krishna, and Zenoss’s launch of a dedicated EMEA cloud zone. Here’s what each development means for partners and customers.

Rackspace Expands EMEA Presence to Address Cloud Skills Gap in the Middle East

Rackspace — a managed cloud and hosting provider that works across AWS, Google Cloud Platform, and Microsoft Azure — announced a multimillion-dollar investment to deepen its footprint in the Middle East and Africa region. The move was designed to open new opportunities for channel partners, including distributors, master agents, systems integrators, and value-added resellers (VARs) operating in the region.

Lisa McLin, Vice President of Alliances and Channel Chief at Rackspace, told Channel Futures that the expanded investment aimed to bring “additional local resources to help address the dearth of cloud skills in the Middle East.” Rackspace manages private and public cloud deployments on behalf of end users, providing an unbiased, multi-cloud approach that suits the complexity of enterprise environments in the region.

George Pawlyszyn was named General Manager for Rackspace’s Middle East and Africa strategy. According to McLin, Pawlyszyn is focused on “providing trusted and unbiased expertise to migrate customers to the cloud securely, and enhance existing customers’ environments whilst working closely with partners.”

At the time of the announcement, Rackspace already served more than 500 customers in the Middle East. To support growth, the company launched a new hub in Dubai, housing salespeople, professional services consultants, and marketing personnel.

“The cloud market is growing significantly in the region and customers are increasingly requiring unbiased experts to support them on their cloud journey.”

Martin Blackburn, Managing Director, EMEA, Rackspace

For channel partners, this kind of regional investment typically translates to more localized sales support, co-selling opportunities, and access to Rackspace’s multi-cloud managed services portfolio closer to end customers.

IBM Appoints Howard Boville as Senior Vice President of IBM Cloud

In a significant leadership move, newly appointed IBM CEO Arvind Krishna — who himself previously led IBM’s cloud computing division before replacing Virginia Rometty — named Howard Boville as Senior Vice President of IBM Cloud, effective May 2020.

Boville joined IBM from Bank of America, where he served as Chief Technology Officer. Notably, the two organizations had already been collaborating to build cloud platforms tailored for the financial services sector, making Boville a familiar face in the IBM ecosystem.

In his new role, Boville was positioned to lead IBM’s cloud strategy with a specific emphasis on hybrid cloud — a priority that IBM has consistently placed at the center of its competitive positioning against hyperscalers such as AWS, Google Cloud, and Microsoft Azure. He also assumed oversight of IBM’s global cloud infrastructure, which at the time included:

• More than 60 cloud data centers
• Operations spanning 19 countries
• 18 availability zones across six geographic regions

The appointment signaled IBM’s intent to accelerate its hybrid cloud narrative — an approach underpinned by the 2019 acquisition of Red Hat — and to compete more aggressively in enterprise cloud deals where workload portability and data sovereignty are critical concerns.

Zenoss Launches Dedicated EMEA Cloud Zone in Frankfurt

Zenoss, an Austin, Texas-based company that develops monitoring analytics software for cloud, hybrid, and on-premises IT environments, announced the availability of a dedicated EMEA cloud zone for its Zenoss Cloud SaaS platform. The zone is hosted in Frankfurt, Germany.

The Frankfurt location was selected for its strong data protection credentials. Specifically, Zenoss cited compliance with the German Bundesdatenschutzgesetz (BDSG) — Germany’s federal data protection act — as a key factor, alongside alignment with GDPR requirements affecting European customers.

“Zenoss Cloud has taken off domestically, and we’ve been experiencing increased demand for separate instances in other regions. We’re dedicated to serving our customers around the world, and this new zone will provide full-stack monitoring and AIOps with the highest levels of data protection.”

Greg Stock, Chairman and CEO, Zenoss

With this launch, all U.S.-based Zenoss Cloud services became available to EMEA customers from a regionalized, dedicated instance — an important distinction for organizations subject to data residency requirements.

Zenoss Cloud is a SaaS-based platform that provides full-stack monitoring and AIOps capabilities, helping IT teams maintain performance visibility across complex, hybrid environments. The company’s strategic alliance partners at the time included AWS, Accenture, Cisco, and NTT Data. It also offered its software through marketplace partnerships on AWS, CenturyLink, Cisco, Rackspace, and ServiceNow.

Zenoss indicated that additional regional zones were planned following the EMEA launch. According to the company, both partners and customers had already begun using the Frankfurt-based EMEA zone at the time of the announcement.

Key Takeaways for Channel Partners

• Rackspace’s EMEA expansion creates new co-sell and referral opportunities for VARs, MSPs, and systems integrators in the Middle East, backed by a formal hub in Dubai and named regional leadership.
• IBM’s hybrid cloud focus under Boville reinforces the company’s enterprise positioning — partners building IBM-aligned hybrid cloud practices can expect continued investment and messaging support from the vendor.
• Zenoss’s EMEA zone removes a key barrier for European customers concerned about data residency, making the platform easier for EMEA-based partners to position and sell.

All three announcements reflect a broader trend: cloud providers are doubling down on regional infrastructure, local expertise, and partner ecosystems as enterprise adoption of multi-cloud and hybrid environments accelerates globally.

What Is the AWS Migration Acceleration Program for Windows?

Amazon Web Services (AWS) announced the general availability of its Migration Acceleration Program (MAP) for Windows in April 2020. First previewed at AWS re:Invent 2019, MAP for Windows is a structured program designed to help organizations plan, execute, and optimize large-scale migrations and modernizations of Windows workloads running on AWS.

The program bundles prescriptive guidance, expert consulting support, purpose-built tools, training resources, and AWS service credits into a single offering. Its stated goals are to reduce both the risk and the total cost of migrating Windows environments to the cloud, and to help customers lower licensing expenses by adopting cloud-native and open-source technologies where appropriate.

The Three Phases of MAP for Windows

MAP for Windows follows a three-phase framework that takes customers from initial assessment through to active migration and modernization:

1. Migration Readiness Assessment (MRA): An evaluation of the customer’s existing Windows environment, identifying readiness gaps, dependencies, and potential risks before any migration work begins.
2. Mobilization Phase: A remediation stage focused on closing the gaps uncovered during the assessment. This phase establishes the migration foundation—landing zones, governance frameworks, and operational runbooks—needed to migrate safely at scale.
3. Migrate/Modernize Phase: The active migration and, where applicable, modernization of Windows workloads. This phase is carried out with support from Amazon Partner Network (APN) members and the AWS Professional Services (ProServe) team.

Why AWS Launched MAP for Windows

The timing of MAP for Windows reflects a broader competitive dynamic in the enterprise cloud market. At re:Invent 2019, AWS CEO Andy Jassy publicly criticized Microsoft’s licensing practices, arguing that Microsoft was limiting how businesses can deploy Windows and SQL Server in the cloud using their existing on-premises licenses. Jassy’s comments were directed not at Azure specifically, but at Microsoft licensing policies that constrain customers’ cloud portability options.

MAP for Windows positions AWS as a destination for customers who want to escape restrictive licensing arrangements. By including service credits and guidance on adopting open-source alternatives, the program offers a financial incentive alongside the technical support.

AWS Windows Workload Market Position

In the blog post announcing general availability, AWS cited IDC research claiming that AWS hosts nearly twice as many Windows Server instances in the cloud as Microsoft Azure. This statistic was used to reinforce AWS’s argument that it is the de facto cloud for enterprise Windows workloads, not just for Linux or cloud-native applications.

Key Benefits at a Glance

• Prescriptive guidance: Documented migration patterns and best practices tailored to Windows Server and SQL Server workloads.
• Expert support: Access to AWS ProServe consultants and vetted APN partners with Windows migration competencies.
• Tooling: Integration with AWS migration tools such as AWS Application Migration Service (MGN) and AWS Database Migration Service (DMS).
• Training: Role-specific training resources for the teams executing the migration.
• Service credits: Financial incentives to offset migration costs and encourage adoption of AWS-native services.
• Licensing cost reduction: Pathways to reduce Windows and SQL Server licensing spend by moving to cloud-native or open-source equivalents where feasible.

Who Should Consider MAP for Windows?

MAP for Windows is aimed primarily at enterprise customers with significant Windows Server or SQL Server footprints on-premises who are planning a large-scale cloud migration. It is particularly relevant for organizations that:

• Are evaluating or already committed to AWS as their primary cloud provider.
• Want structured, low-risk migration methodology rather than ad-hoc lift-and-shift approaches.
• Are concerned about Microsoft licensing costs in the cloud and want to explore alternatives.
• Need access to vetted migration partners and professional services support.

Further Resources

For more information on MAP for Windows, visit the official AWS MAP for Windows page. Customers interested in enrolling should contact their AWS account team or an APN partner with the Windows migration competency.

Azure’s Rise: A Genuine Competitive Threat to AWS

For most of cloud computing’s history, Amazon Web Services has held a commanding lead in market share. But Microsoft Azure has been steadily closing the gap. According to figures cited around early 2020, Azure grew from roughly 14 percent of the cloud infrastructure market in 2018 to approximately 18 percent by 2019 — a meaningful shift that prompted AWS to reportedly double the size of its sales force to respond to the competitive pressure.

For DevOps engineers and cloud architects, this trend carries a practical message: fluency in Azure is fast becoming as important as AWS expertise, and organizations are increasingly running multi-cloud or Azure-first environments.

Why DevOps Engineers Should Take Azure Seriously

Azure’s growth is not happening in a vacuum. Several factors have driven enterprise adoption:

• Microsoft ecosystem integration: Organizations already running Windows Server, Active Directory, Microsoft 365, or SQL Server often find Azure integration straightforward, reducing migration friction.
• Hybrid cloud strength: Azure Arc and Azure Stack give enterprises mature options for extending cloud services to on-premises infrastructure — an area where Azure has historically led.
• Enterprise agreements: Many large enterprises already have Microsoft licensing agreements that make Azure commercially attractive.
• Developer tooling: Deep integration with GitHub, Azure DevOps, and Visual Studio makes Azure a natural fit for development pipelines already built on Microsoft tooling.

Core Azure Skills Every Cloud Engineer Should Build

Whether you are expanding from AWS or starting fresh, the following capability areas are foundational to productive Azure work:

Compute and App Hosting

Azure Virtual Machines, Azure App Service, Azure Functions (serverless), and Azure Kubernetes Service (AKS) are the primary compute primitives. Understanding when to use each — and how to size and scale them — is essential for any Azure developer or architect role.

Storage and Data Services

Azure offers Blob Storage, Table Storage, Queue Storage, and Azure Files as core storage options, alongside managed database services including Azure SQL Database, Cosmos DB, and Azure Database for PostgreSQL and MySQL. Knowing the right tool for structured, unstructured, and NoSQL workloads is critical.

Identity and Security

Azure Active Directory (AAD) is central to authentication and authorization across Azure services. Engineers should understand role-based access control (RBAC), managed identities, Key Vault for secrets management, and Azure Policy for governance enforcement.

Monitoring and Observability

Azure Monitor, Application Insights, and Log Analytics form the observability stack. Setting up meaningful alerts, dashboards, and diagnostic logging is an operational requirement, not an afterthought.

Networking

Virtual Networks (VNets), subnets, Network Security Groups (NSGs), Azure Load Balancer, Application Gateway, and VPN Gateway are the building blocks of secure, well-architected Azure network topologies.

Azure Developer Certification: AZ-204 (Formerly AZ-203)

The original article references the AZ-203 exam, which was the Microsoft Certified: Azure Developer Associate certification exam available at the time of writing (early 2020). Microsoft has since retired AZ-203 and replaced it with AZ-204: Developing Solutions for Microsoft Azure, which covers similar developer-focused content updated for current Azure capabilities.

The AZ-204 exam targets developers who design, build, test, and maintain cloud applications and services on Azure. Key domains include:

• Developing Azure compute solutions (VMs, containers, App Service, Azure Functions)
• Developing for Azure storage
• Implementing Azure security
• Monitoring, troubleshooting, and optimizing Azure solutions
• Connecting to and consuming Azure services and third-party services

If you are planning to sit an Azure developer exam, verify the current exam code and objectives directly on the Microsoft Learn credentials page, as Microsoft updates exam content on a regular cadence.

How to Structure Your Azure Learning Path

If you are building Azure skills from scratch or transitioning from another cloud platform, a practical learning sequence looks like this:

1. Platform fundamentals: Get comfortable with the Azure Portal, Azure CLI, and Azure Resource Manager (ARM) concepts including resource groups, subscriptions, and management groups.
2. Core services hands-on: Provision and configure VMs, storage accounts, and a basic App Service web app. Use the free tier where available.
3. Identity and access: Set up AAD users, groups, and RBAC assignments. Practice creating service principals and managed identities.
4. Developer services: Deploy a containerized application to AKS or Azure Container Instances. Build a simple Azure Function triggered by a queue message.
5. Observability: Enable Application Insights on a web app and set up a Log Analytics workspace. Write a basic Kusto (KQL) query.
6. Certification preparation: Use Microsoft Learn’s free learning paths and practice assessments aligned to AZ-204 objectives before booking the exam.

Free Resources to Get Started

Microsoft provides substantial free learning material that should be your first stop before purchasing any third-party course:

• Microsoft Learn (learn.microsoft.com): Structured, hands-on learning paths for every Azure role and certification, available at no cost.
• Azure Free Account: Includes a 12-month free tier for select services and $200 in credit for the first 30 days — enough to work through most lab exercises.
• Azure Architecture Center: Reference architectures, best practices, and design guides for common workload patterns.
• Azure documentation: Comprehensive service documentation with quickstarts, tutorials, and how-to guides.

The Multi-Cloud Reality for DevOps Teams

The competitive pressure between AWS and Azure ultimately benefits engineers: skills in both platforms are genuinely valued in the job market, and most large enterprises are running workloads across multiple clouds. Infrastructure-as-code tools like Terraform and Pulumi, and container orchestration via Kubernetes, provide portability across providers. Building a solid foundation in Azure — including its native DevOps toolchain — makes you a stronger cloud practitioner regardless of which provider your employer favors.

Hybrid Cloud in the Modern Enterprise

Hybrid cloud — environments that blend public and private cloud infrastructure — sit at the centre of the modern IT landscape. Organisations of all sizes are working to incorporate legacy systems into forward-looking digital strategies while simultaneously meeting regulatory obligations around data residency, privacy, and compliance. Despite being well-established, hybrid cloud is frequently misunderstood by IT departments, CIOs, and technology partners alike, and its long-term trajectory remains unclear to many practitioners.

Why Organisations Choose Hybrid Cloud

The most common driver for a hybrid cloud adoption is the desire to keep sensitive data under organisational control while still benefiting from the scalability and economics of public cloud. Adam Mansfield, commercial advisory practice leader at UpperEdge, described the model this way:

Using a hybrid cloud, enterprises have the ability to put sensitive data in a highly secure and controlled private cloud environment while having the ability to also place other non-sensitive data in an often more scalable, reliable and cost-effective public cloud environment, like Microsoft Azure, AWS, or Google Cloud Platform (GCP).

Adam Mansfield, UpperEdge

Beyond data sensitivity, organisations adopt hybrid models for several other concrete reasons:

• Agility from existing infrastructure: Hybrid allows companies to continue extracting value from on-premises data centres while gaining public cloud elasticity for new workloads.
• Connectivity constraints: Remote locations — rural factories, farms, oil and gas platforms — often have unreliable or low-bandwidth internet connections, making a fully public-cloud model impractical. Edge or on-premises components bridge that gap.
• Legacy modernisation staging: Andrew Bandera, DMI practice lead and solutions architect, describes years of accumulated IT complexity as enterprise accretion — massively customised systems that need to be untangled. Hybrid provides a staging environment where workloads can be cleaned up and containerised before being fully migrated to the public cloud.
• Regulatory compliance: Regulations such as GDPR, HIPAA, and sector-specific data sovereignty rules may require that certain data remain on-premises or within a specific geographic boundary, making a pure public cloud deployment impossible without a private component.

Key Technologies Improving Hybrid Cloud Performance

Dr. Taras Filatov, CEO and founder of blockchain provider Dappros, observed that the proportion of cloud components relative to on-premises infrastructure continues to grow over time. To get more from a hybrid environment, practitioners have several levers available:

• Kubernetes and containerisation: Containerising applications with Kubernetes enables consistent workload portability across on-premises and public cloud nodes. Filatov noted that Kubernetes adoption was still relatively low at the time of writing, representing a significant opportunity for organisations willing to invest.
• AI-based continuous optimisation: Joel Richman, principal at Opsani, highlighted a trend gaining traction: using AI to automate the continuous optimisation of application resource consumption across public and private cloud environments. This can dynamically right-size workloads without manual intervention.
• Machine learning and blockchain add-ons: Public cloud providers offer managed ML and blockchain services that can extend a hybrid environment’s capabilities without requiring on-premises hardware investments.
• Public cloud compliance tooling: Native compliance frameworks available in major public clouds can help enforce data governance policies, making it easier for hybrid deployments to satisfy requirements like GDPR.

The Role of Microsoft Azure in a Hybrid World

Many of the world’s largest enterprises have longstanding Microsoft relationships, which positions Azure as a natural anchor for hybrid architectures. Bandera assessed Azure as well-equipped to meet hybrid cloud needs, specifically citing:

• VPN connectivity options: Multiple tiers of VPN gateways allow organisations to connect on-premises networks to Azure virtual networks securely.
• Azure ExpressRoute: A dedicated private connection to Azure that bypasses the public internet, improving latency, reliability, and security compared to a standard VPN — an important consideration for latency-sensitive workloads.
• Service-level agreements: Azure publishes a 99.95% SLA for many core services, giving enterprises a contractual baseline for availability planning.

From a commercial standpoint, Mansfield highlighted that one of Azure’s notable advantages for large hybrid deployments is volume-based pricing commitments. Enterprises can make an upfront Azure Monetary Commitment — similar to a pre-paid credit — and draw it down based on actual consumption. As consumption volumes grow, Mansfield argues that Microsoft should provide commitments that Azure service SKU pricing decreases proportionally, rewarding scale with lower unit rates. Buyers negotiating enterprise agreements should evaluate whether these thresholds and discount structures are explicitly locked in contractually.

The Biggest Challenges Facing Hybrid Cloud

Despite its advantages, hybrid cloud introduces costs and complexities that are pushing some organisations to accelerate their transition to a pure public cloud model:

• Dedicated connectivity costs: MPLS-based private circuits — commonly used to connect on-premises environments to cloud providers — are expensive to provision and operate. From a network engineering perspective, latency across an MPLS circuit will always be higher than traffic running natively within a cloud provider’s backbone or subnet, which is a hard physical constraint.
• Operational complexity: Managing two (or more) distinct environments — with separate toolchains, security perimeters, and networking layers — increases the operational burden on infrastructure and security teams.
• Security convergence: As public cloud security controls mature, some organisations find that the security argument for keeping data on-premises weakens. This is accelerating the shift away from private data centres for workloads that were previously considered too sensitive for public cloud.

What Comes Next: The Long-Term Outlook

The expert consensus is that hybrid cloud is not disappearing soon, but its role will change. Bandera summarised the trajectory:

Hybrid scenarios are here to stay for the foreseeable future, particularly for organisations with sensitivities around IP or data, and in low or no bandwidth situations, such as remote industrial mining or oil platforms. Due to cost and complexity, hybrid scenarios will diminish over time, but for now they serve a purpose that cannot otherwise be fulfilled.

Andrew Bandera, DMI

The practical conclusion for DevOps and infrastructure teams is that hybrid cloud will remain a reality for most large organisations for the foreseeable future — but the balance will continue to tip toward public cloud as connectivity improves, compliance tooling matures, and the cost and complexity of maintaining private infrastructure grows harder to justify. Technologies like Kubernetes, AI-based optimisation, and managed compliance services are the tools that will either make hybrid environments work better, or make the case for eliminating the private layer entirely.

Key Takeaways for DevOps Teams

• Hybrid cloud is a practical necessity for organisations with regulatory constraints, remote connectivity challenges, or complex legacy estates — not just a transitional state.
• Kubernetes adoption is a high-leverage investment for improving workload portability across hybrid environments.
• AI-driven continuous optimisation can automate resource management across hybrid layers without manual tuning.
• Azure’s ExpressRoute and volume commitment pricing structures are worth evaluating carefully during enterprise agreement negotiations.
• MPLS connectivity costs and latency are structural constraints that favour public cloud for latency-sensitive workloads wherever connectivity permits.
• The long-term direction is toward greater public cloud adoption, but the timeline varies significantly by industry, geography, and regulatory environment.

Overview

Google Cloud announced a series of new security capabilities aimed at enterprise customers. The centerpiece of the announcement was a significant expansion of Chronicle’s security analytics platform, including a new threat-detection rules language and a unified event timeline data model. Google also moved two additional security services — Web Risk API and reCAPTCHA Enterprise — into general availability.

Chronicle Joins Google Cloud

Chronicle was originally incubated inside Alphabet’s X moonshot division as a standalone cybersecurity company. Google subsequently folded Chronicle into Google Cloud, positioning it as the foundation of the company’s enterprise security analytics story. The integration gave Google Cloud a purpose-built platform for ingesting, indexing, and analyzing large volumes of security telemetry.

YARA-L: A Threat Detection Language Built for Modern Attacks

The most technically significant announcement was YARA-L, a new rules language designed specifically for detecting threats in security logs and telemetry data.

Background: What Is YARA?

YARA is a widely adopted, open-source pattern-matching language originally designed for identifying and classifying malware samples based on file characteristics. Security researchers and analysts use YARA rules extensively to describe malware families and detect malicious binaries.

What YARA-L Adds

The Chronicle team extended the YARA concept to cover log-based detection use cases — hence the “L” suffix. YARA-L allows security analysts to write detection rules that operate on:

• Security log data (authentication logs, firewall logs, etc.)
• Endpoint Detection and Response (EDR) telemetry
• Network traffic metadata

Critically, YARA-L rules are designed to map to the tactics and techniques catalogued in the MITRE ATT&CK framework — an industry-standard knowledge base that organizes the methods adversaries use during an attack lifecycle. This alignment means analysts can write detection logic that directly corresponds to known adversary behaviors rather than only looking for specific file signatures.

Google stated that the new threat detection offering supports massively scalable rule execution in both real-time and retroactive modes — meaning analysts can run new rules against historical data to find threats that may have gone undetected previously.

Unified Event Timeline: Automatic Attack Chain Correlation

Chronicle also introduced a new data structure that combines an updated data model with the ability to automatically link multiple security events into a single, coherent timeline.

To illustrate the value: imagine an employee who receives a phishing email containing a malicious link, then visits a credential-harvesting site, and finally downloads a malware payload. These three actions generate logs in three separate systems — email gateway, proxy or DNS logs, and endpoint telemetry. Traditionally, a security analyst investigating a breach would need to manually correlate these logs to reconstruct the attack chain.

Chronicle’s new data structure automates this correlation, stitching together seemingly unrelated events into a single timeline automatically. This reduces investigation time and lowers the risk that an analyst misses a critical link in an attack sequence.

Palo Alto Networks Cortex XSOAR was announced as the first Google Cloud partner to integrate with this new data structure.

Web Risk API and reCAPTCHA Enterprise Reach General Availability

Two additional Google Cloud security services moved from preview into general availability as part of the same announcement.

Web Risk API

The Web Risk API gives organizations programmatic access to Google’s database of known malicious URLs and domains. Practical use cases include:

• Warning users before they follow a suspicious link on your platform
• Blocking user-submitted content that contains links to known malicious pages
• Scanning URLs at ingestion time to flag high-risk content

reCAPTCHA Enterprise

reCAPTCHA Enterprise is aimed at protecting web applications and user accounts from automated abuse. It targets threats such as:

• Credential stuffing and misuse — automated login attempts using leaked username/password pairs
• Web scraping — automated harvesting of site content at scale
• Fake account creation — bot-driven registration of fraudulent accounts

Unlike the consumer-facing reCAPTCHA product, the Enterprise tier is designed for integration into business-critical workflows with more granular risk scoring and policy controls.

Context: Google Cloud’s Enterprise Security Push

These announcements came as Google Cloud was actively working to close the gap on AWS and Microsoft Azure in the enterprise market. At the time of the announcement, Google Cloud had disclosed an annual revenue run rate of $10 billion. Security capabilities are a key differentiator for enterprise cloud buyers, and absorbing Chronicle gave Google a credible enterprise security analytics story to compete alongside native offerings from its rivals.

In December 2019, Google Cloud had also announced new partnerships with external cybersecurity firms, further broadening its security ecosystem ahead of this February 2020 product wave.

Summary