Blockchain enhances the trust, security, transparency, and traceability of data shared across business networks – and provides cost savings with new capabilities.

Benefits of Blockchain:

1. Enhanced security – Your data is sensitive and important, and blockchain can significantly change the way you view important information. By creating a record that cannot be changed and is end-to-end encrypted, blockchain helps prevent fraud and unauthorized activity. Privacy issues on the blockchain can also be addressed by anonymizing personal data and user permissions to prevent access.
2. Greater transparency – Without blockchain, each organization would have to maintain a separate database. Since the blockchain uses a distributed ledger, transactions and data are recorded identically in many places.
3. Increased efficiency and speed – Traditional paper-heavy processes are time-consuming, prone to human error, and often require third-party mediation. By streamlining these processes with the blockchain, transactions can be completed faster and more efficiently.
4. Automation – Transactions can also be automated with “smart contracts”, which increases your efficiency and speeds up the process even more. Once the pre-specified conditions are met, the next stage of the transaction or process is triggered automatically.
5. Reduced costs – The nature of blockchain can also cut costs for organizations. This creates efficiency in processing transactions. It also reduces manual tasks such as collecting and modifying data as well as simplifies reporting and auditing processes

Features of Blockchain:

Advantges of Blockchain:

• With blockchain, your business process will be better protected with the help of a high level of security
• The hacking threats against your business will also be reduced to a greater extent.
• As blockchain offers a decentralized platform, there is no need to pay for centralized entities or intermediaries’ services.
• Enterprise blockchain technology enables organizations to use different levels of accessibility.
• Organizations can do faster transactions with the help of blockchain.
• Account reconciliation can be automated.
• The transactions done are transparent and hence, easy to track.

Pre-requisites to learn Blockchain:

• Fundamental skill and Knowledge in Technical Field.
• Understand Blockchain Security
• Know the Platforms.
• Distributed ledger expertise.
• Master of Interoperability.
• Cryptography
• Data Structures
• Smart Contracts

How Long Does it Take to Learn Blockchain?

Typically, blockchain training by SimplyLearn will let you master the concepts in about 4 months if you devote 5 to 10 hours per week. The training takes place in such a way that you become proficient in almost all the concepts of blockchain and can easily add certification to your resume.

Is blockchain difficult to learn?

Blockchain is revolutionary and complex, so it seems difficult to understand. However, you shouldn’t be afraid to learn its fundamentals. The basic concept of blockchain is decentralization, mining and consensus mechanism. It is a series of immutable blocks that are cryptographically secure.

How to Learn Blockchain Online?

The first thing you should do before learning Blockchain is to ask yourself how you learn best.

Do you prefer online tutorials, or do you prefer online courses? Do you want to work on projects as part of a team, or are you comfortable learning at your own pace? By considering this question in advance, you will be able to better understand what kind of material you should be looking for.

If you are planning to learn Blockchain, go with DevOpsSchool institute. Will provide you online and classroom training and certification course Program by an expert. To get in-depth knowledge of Blockchain along with its various applications, check out our interactive, live-online training.

Here you can see the Agenda of the Blockchain Course:

This course covers the basics of Blockchain and its main features. You will develop a critical understanding of why Blockchain is useful and how it can be combined with other web development frameworks. You will learn about the basics of Blockchain and its advantages. Then, you’ll cover more advanced topics like integrating Blockchain with other frameworks.

Hopefully, I think this information is helpful for you.

However, if you want to find out more details, be sure to check out our blockchain course to learn more about this technology.

 Today, IT and businesses often find themselves in silos working separately without a shared vision of the customer experience and all the services supporting it. IT must move beyond maintaining up a time to deliver the agility and competitive edge the business needs this means better cohesion between IT and business using predictive tools to prevent service failure meet Splunk IT service intelligence.

 IT service intelligence teams can no longer afford to be reactive especially when it comes to performance and availability. A protective IT strategy is necessary to get ahead of a problem and to protect revenue and your customer experience. Splunk IT service intelligence delivers a predictive IT, running an AIs we have AI scalable data platform that can ingest, search, analyze, and all types of data. ITSI allows you to go from reactive to predictive IT while gaining insights across both IT and business services. Splunk ITSI allows customers to predict service degradation 30 to 40 minutes in advance with the proactive alert for the KPIs most at risk so you can resolve incidents before they start gaining an end-to-end view of both IT and business KPIs on a single screen so you can work on what matters most when it comes to AI ops strategy. ITSI helps you become more proactive and even predictive reduce your incident investigation time business impact outages and event noise ensure your best customer experiences yes and keep the business and IT working better together with Splunk IT service intelligence. Put AI at the heart of your operations and try the free ITSI sandbox from today.

What are the benefits of Splunk IT Service Intelligence?

From the start as we are learning the series of Splunk software tools. In this stack, we discuss that every software has its own performances, capabilities, and security management but how the software works predictive and analytic. This is the reason behind endorsing the system software like how fast service is happening, and how the software is managing by the ITs team. With this effect, there are certain benefits related to Splunk ITSI admin as:-

• Intelligent incident management: Accelerate MTTR using real-time event correlation, automated incident prioritization and integrations with IT service management (ITSM) tools.
• Advance analytics & alerting: It manages a certain function as anomaly detection, adaptive thresholding, and predictive alerts & dashboards.
• Service analyzer: Service monitoring dashboards that display dependent systems and apps. Drill- down into a service’s underlying components to determine what’s impacting its health score.
• End to End visibility: protect business performance by monitoring service health, drill down into underlying infrastructure, all from one dashboard. 

Why we should learn Splunk IT Service Intelligence?

Nowadays, in this trending world. You have met with so many tools in the IT software services. Whenever we talk about Splunk’s advanced services then intelligence plays a prominent role in the IT entities. Now, we are talking about the learning conditions for IT service intelligence. So, what are the reasons to implement a Splunk ITSI? Before we had traditional solutions and those solutions were created as operational challenges like lack of visibility, complex environment, & slow response times. The most important talk is that lack of intelligence service created a significant impact on a business like lost revenue, inefficient productivity, poor customer experiences, & struggle to innovate then it creates technical advance in the field of IT sectors on the basis of Business and IT Service Monitoring, Intelligent Incident Management, Machine Learning, and Predictive Analytics. I wish that these awareness scenarios will give you brain mapping concepts for the Splunk intelligence certified admin.

How our Splunk IT Service Intelligence course/training would help?

This tutorial will help you about understanding the core features of Splunk ITSI like IT event management, service monitoring, and advanced analytics & alerting. It will give you a balancing ideology in the field of KPIs, Data sources, entities, services, incident response, automated remediation, predictive analytics, and event analytics. Through these interactive lectures and hands-on challenges, you will be able to learn to use IT service intelligence, services & glass tables, Notable event & event aggregation policies, and deep dives-investigation with swim lane views. After this, it gives you certification and that makes you qualified for the job opportunities along with the expected high-paid salary and makes your life efficient with future scope.

Related video:  

1. Post Graduate Certification in Big Data Analytics — E&ICT, IIT Guwahati

This programme is designed for graduates who want to build a career in Big Data Analytics, Data Science, or Machine Learning. It spans 11 subject areas including Statistics for Analytics, Data Analytics with Excel and SQL, Python, Java, Linux, Big Data and Hadoop, Apache Spark, MongoDB, Business Intelligence and Data Mining, and Tableau.

• Duration: 9 months
• Price: ₹2,25,000
• Eligibility: Graduates with a minimum of 50% marks in their undergraduate degree
• Certificate: Joint certificate from E&ICT IIT Guwahati and IBM upon completion

2. Machine Learning — Coursera

One of the most recognised introductory machine learning courses available online, this Coursera offering covers logistic regression, artificial neural networks, and a range of ML algorithms. The course balances theoretical foundations with practical implementation exercises, making it suitable for learners who want to apply ML techniques to real problems quickly.

• URL: coursera.org/learn/machine-learning
• Duration: Approximately 64 hours
• Price: ₹4,508 (pricing may vary; audit option may be free)
• Eligibility: Open to all learners
• Certificate: Electronic certificate, shareable on LinkedIn

3. Data Science Architect Master’s Course — Intellipaat

This master’s-level programme covers the full breadth of Data Science tooling: R, Hadoop, Apache Spark, Scala, Deep Learning, Tableau, SAS, SQL, and MongoDB. The curriculum includes 10 core courses, 53 industry projects, and one Capstone project. Learners also receive five IBM-co-created self-paced courses covering Deep Learning with TensorFlow, chatbot development with Watson Assistant, R for Data Science, Spark MLlib, and Python for Data Science.

• URL: intellipaat.com — Data Science Architect
• Duration: 232 hours of online instructor-led training
• Price: ₹62,000
• Eligibility: No prerequisites
• Certificate: IBM certificate upon completion

4. Deep Learning Explained — edX

This edX course provides a practical introduction to deep learning. Topics include multi-class classification using logistic regression, handwritten digit recognition with deep neural networks and convolutional networks, time-series forecasting with recurrent networks, and text processing using LSTM units. It is a good entry point for those with a data science background who want to move into neural network architectures.

• URL: edx.org — Deep Learning Explained
• Duration: 6 weeks
• Price: Free to audit; verified certificate costs $99
• Eligibility: Basic programming skills and working knowledge of data science recommended

5. Cloud and DevOps Architect Master’s Course — Intellipaat

This programme is directly relevant to DevOps engineers and cloud practitioners. It covers AWS, Azure, DevOps toolchains, Python, Java, Splunk, and Linux across 11 courses, 26 industry projects, and a Capstone project. Learners also receive official Microsoft course material on integrating on-premises identity infrastructure with Azure and implementing security in Azure development solutions.

• URL: intellipaat.com — Cloud & DevOps Architect
• Duration: 147 hours of online instructor-led training
• Price: ₹56,900
• Eligibility: No prerequisites
• Certificate: Joint IBM and Microsoft certificate upon completion

6. Mastering Python — Networking and Security — Udemy

Python is the de-facto scripting language for network automation and security tooling. This Udemy course focuses specifically on using Python for network scripting and security tasks. It covers script creation, threading, exception handling, and leveraging Python networking libraries to build practical automation scripts.

• URL: udemy.com — Mastering Python: Networking and Security
• Duration: Approximately 5 hours of on-demand video
• Price: ₹360 (Udemy prices fluctuate; check current listing)
• Eligibility: Basic programming awareness helpful but not strictly required

7. Artificial Intelligence Master’s Course — Intellipaat

This AI-focused master’s programme covers Data Science, Python, R, Statistics, Natural Language Processing, SAS, Excel-based analytics, and Tableau. It is designed for learners who want a broad AI and ML skill set without a strict programming prerequisite, though basic programming knowledge will help you progress faster.

• URL: intellipaat.com — Artificial Intelligence Master’s Course
• Duration: 166 hours of online instructor-led training
• Price: ₹46,000
• Eligibility: No prerequisites; basic programming knowledge is an advantage
• Certificate: IBM certificate upon completion

8. Web Development with Django and AngularJS — Pluralsight

This Pluralsight course teaches full-stack web development by combining Django on the back end with AngularJS on the front end. Students build a complete web application, learn to connect front-end JavaScript to back-end Python using REST and AJAX, and finish with a working, interactive application they can extend independently.

• URL: pluralsight.com — Django and AngularJS Web Development
• Duration: 3 hours 42 minutes
• Price: Included in Pluralsight subscription (approximately $19/month at time of writing; check current pricing)
• Eligibility: Open to all learners

9. Business Intelligence Architect Master’s Course — Intellipaat

For data professionals who work closer to the business layer, this BI-focused programme covers Informatica, Tableau, MSBI, Power BI, MS SQL, data warehousing and Erwin modelling, Azure Data Factory, and SQL DBA administration. The programme includes 9 core courses and 32 industry-based projects.

• URL: intellipaat.com — Business Intelligence Master’s Course
• Duration: 150 hours of online instructor-led training
• Price: ₹56,000
• Eligibility: Open to all learners
• Certificate: Microsoft certificate upon completion

10. Intro to Machine Learning with TensorFlow — Udacity

This introductory programme covers foundational machine learning algorithms, starting from data cleaning and supervised learning models and progressing to deep learning and unsupervised techniques. Practical exercises are integrated at each stage, giving learners hands-on experience with TensorFlow alongside conceptual understanding.

• Duration: Approximately 3 months
• Eligibility: Basic Python and statistics knowledge recommended

Choosing the Right Course for Your Role

The courses above span a wide range of investment levels — from free audits to multi-month master’s programmes. Before enrolling, consider the following:

• DevOps / Cloud engineers will get the most immediate value from the Cloud and DevOps Architect Master’s Course or the Python Networking and Security course.
• Data engineers and analysts should look at the Big Data Analytics certification or the Data Science Architect programme.
• Machine learning practitioners can start with the Coursera ML course or the TensorFlow intro and graduate to the Deep Learning Explained or AI Master’s course.
• Full-stack developers moving into data-driven products will benefit from the Django and AngularJS course alongside a BI or SQL foundation.

Prices and course structures change regularly. Always verify the current syllabus, pricing, and certification status directly on the provider’s website before enrolling.

The Container Management Market Is Expanding Fast

Organizations wrestling with the operational complexity of agile development and cloud-native architectures are turning to container management platforms in large numbers. According to research firm Omdia, the container management platform market was projected to grow at a compound annual growth rate (CAGR) of 30 percent between 2018 and 2023 — a rate that reflects both the urgency of the problem these platforms solve and the scale of enterprise adoption already underway.

The underlying driver is well understood: as organizations move away from monolithic application architectures toward microservices, they need tooling that can manage the dynamic, distributed, and ephemeral nature of containerized workloads. Container management platforms — including orchestrators like Kubernetes and the commercial distributions built around them — fill that gap.

Agile Adoption Is Outpacing Operational Maturity

In an Omdia survey of 700 enterprise end users, 60 percent of respondents reported having adopted agile development methodologies. That is a significant organizational shift, but adoption at the process level does not automatically translate into operational readiness at the infrastructure level.

The same survey found that 32 percent of organizations cited a lack of cloud-native skills as a key factor slowing more rapid deployment of microservices-based applications. In other words, the bottleneck is not desire or intent — it is the shortage of engineers who can reliably run containers and Kubernetes in production at scale.

This skills gap is itself a market catalyst. When internal teams cannot operate complex container infrastructure confidently, organizations look to managed platforms, commercial distributions, and third-party tooling to reduce operational burden. That demand is a core reason the container management platform market is growing as quickly as it is.

Why Microservices Create a Communication Problem

A microservices architecture decomposes an application into small, independently deployable services, each responsible for a specific function. This approach increases deployment flexibility and development velocity, but it introduces a new class of operational challenge: managing service-to-service communication at scale.

Consider a practical example. A user visiting a travel website to book a train ticket triggers a chain of service calls: the request service queries the route database, which in turn queries the schedule service, which queries the pricing database, which checks for applicable promotions, and so on before populating the user’s cart. If the system also generates personalized recommendations, additional services are involved. Each of these is a discrete microservice that must discover, authenticate against, and communicate with the others — reliably, securely, and with acceptable latency.

In a small deployment, this can be managed with relatively simple service discovery and load balancing. As the number of services grows into the dozens or hundreds, the challenge becomes significantly harder. A single high-traffic service — a schedule database that every other service depends on — can become a bottleneck or a single point of failure. Traffic management, retries, circuit breaking, and observability must be handled consistently across every service boundary.

Enter the Service Mesh

A service mesh is a dedicated infrastructure layer designed to handle service-to-service communication concerns consistently across a distributed application — without requiring individual services to implement that logic themselves. Instead of embedding communication logic (retries, timeouts, authentication, tracing) in application code, the service mesh externalizes it into a separate layer, typically implemented using sidecar proxies deployed alongside each service instance.

The open source community has produced several prominent service mesh implementations, including:

• Istio — originally developed at Google, now a CNCF project, widely used in Kubernetes environments
• Linkerd — a CNCF project focused on simplicity and low resource overhead
• HashiCorp Consul — a service mesh with strong service discovery roots, designed to work across Kubernetes and non-Kubernetes environments

As Omdia noted in its research, by 2020 the service mesh market had expanded beyond these foundational projects, with the firm identifying seven leading service mesh capabilities offering differing levels of control and varying requirements for operator expertise.

The Deployment Complexity Challenge

Despite its architectural advantages, service mesh adoption has not been frictionless. Roy Illsley, chief analyst for cloud and data center research at Omdia, identified the core barrier clearly:

“The adoption of service-mesh technology is being inhibited by the uncoordinated manner in which the service mesh is deployed and integrated as part of any cloud-native environment. As the maturity of the ecosystem evolves the complexities of deployment, the process of managing and securing a service mesh must be simplified. Only then will this open the cloud-native technology to a wider audience, and therefore enable more organizations to deploy and manage microservices in production environments.”

— Roy Illsley, Chief Analyst, Cloud and Data Center Research, Omdia

This is a recurring pattern in platform engineering: a technology solves a real problem, but its operational complexity initially limits adoption to teams with deep expertise. Simplification — through better tooling, managed services, or improved abstractions — is what eventually drives broader enterprise uptake.

What a Production-Ready Service Mesh Must Provide

Not all service mesh implementations are equal. Omdia outlined the core capabilities that a service mesh should provide to be considered production-ready. These capabilities represent both the architectural best practices and the functional checklist that engineering teams should evaluate when selecting a service mesh solution:

• Control plane and data plane separation — The control plane manages configuration and policy; the data plane (typically sidecar proxies) enforces that policy at runtime. Keeping these concerns separate improves resilience and flexibility.
• Dynamic routing — Traffic routing rules must be configurable at runtime without service restarts, enabling canary deployments, A/B testing, and traffic shifting.
• Service discovery — Services must be able to locate each other dynamically, accounting for the ephemeral nature of container instances.
• Load balancing — Intelligent, application-aware load balancing distributes traffic effectively and can account for service health and response latency.
• Observability and traceability — Distributed tracing, metrics collection, and logging at the mesh level give operators visibility into request flows across service boundaries — a requirement for diagnosing latency and errors in complex systems.
• Security — Mutual TLS (mTLS) between services, certificate rotation, and policy-based access control enforce a zero-trust security model within the cluster.
• Interoperability — The service mesh should integrate with existing tooling, support heterogeneous environments (mixed Kubernetes versions, non-containerized services), and work with the wider observability and CI/CD ecosystem.

What This Means for DevOps Teams

The growth of the container management platform market is not simply a vendor story — it reflects real pressure on engineering teams. As microservices deployments grow in scale and complexity, infrastructure decisions that worked at 10 services break down at 100. Service meshes are one of the primary architectural responses to that scaling challenge.

For DevOps and platform engineering teams, the practical takeaways are:

• Evaluate service mesh tooling before you need it. Retrofitting a service mesh into a large production deployment is significantly harder than designing for it early.
• Prioritize observability. The value of a service mesh is substantially higher when paired with a robust distributed tracing and metrics stack.
• Account for the skills gap. The Omdia data on cloud-native skills shortages reflects a real hiring and training challenge. Platform teams should budget for enablement, not just tooling.
• Watch the managed service space. Cloud provider-managed service meshes (such as AWS App Mesh, Google Cloud Traffic Director, or Azure Service Fabric Mesh) are specifically designed to reduce operational overhead — a valid option when internal expertise is limited.

The 30% CAGR projected by Omdia reflects a market responding to genuine operational demand. Understanding why that demand exists — and what the platforms in this space are actually solving — is the prerequisite for making informed infrastructure decisions.

Although 99.999% availability may be IT’s ambition, it’s far from reality. In March 2019, Google’s Gmail experienced a 4.5-hour global outage. In the same month, Facebook suffered a 14-hour outage, its most massive outage to date, crippling app-accessibility worldwide.

Downtime and low availability will likely occur to all systems eventually, especially when introducing code changes. When outages occur, it’s truly the detection systems and smart mitigation processes that separate quick rebounds from long recoveries.

LogicMonitor has released its IT Downtime Detection and Mitigation Report, a survey of IT professionals to uncover trends and tactics impacting availability in 2020. The report surveyed over 300 IT design makers at organizations with 2,500 or more employees within the U.S., U.K., Australia and New Zealand.

Below, we examine the LogicMonitor report’s key findings. We’ll discover, on average, what sort of projects are causing the most outages and brownouts. We’ll also see what strategies top IT teams are using to prevent, detect and mitigate disruptions.

Which Transformation Initiatives Hurt Availability the Most?
Ninety-six percent of organizations surveyed had experienced at least one outage in the past three years. But why? One reason LogicMonitor conducted this report was to pinpoint which digital transformation initiatives and IT trends are the leading contributors to high-profile outages and brownouts.

When determining causation, answers revolve around novel cloud IT initiatives. Fifty-nine percent of respondents felt that mobile computing was causing more brownouts/outages. Fifty-seven percent found AI and edge computing were making outages more common. Other high-ranking reasons included digital transformation (57%) and IoT (53%).

According to the report, “LogicMonitor’s research suggests that IT decision-makers hold IT transformation initiatives responsible for increasingly frequent outages and brownouts.”

Though we generally view digital transformation initiatives in a positive light, they do come with caveats. The move from private to cloud infrastructure, for example, can bring unforeseen costs. Also, some authorities note a lack of talent specializing in cloud and hybrid environments. The report also suggested that accelerated cloud technologies may require time to mend before they realize positive business returns (and higher availability).

Critical Strategies for Preventing Outages
Another goal of the study was to discover what sort of tactics IT professionals are actively using to prevent outages.

The study exposed many strategies that IT folks are currently undertaking. The top three tactics were performing preventative maintenance (75%), reviewing system logs (71%) and increasing the capacity of systems (71%). Other top strategies included designing redundancy into systems, and keeping an eye on customer support tickets.

With the rise of a completely remote workforce, some IT executives do anticipate higher expenditures due to AI and cloud architecture, which could bring added pressure. However, it’s worthy to note the cloud hasn’t negatively affected all groups, or even reached all companies. According to another study conducted by Adobe and Fortune, only one in three organizations store their data in a public cloud. Of course, one could argue that in doing so, such companies substitute innovation for less breaking change.

Another necessary aspect to keep a vigilant eye on the digital ecosystem is proactive monitoring. According to the report, 74% of teams rely on proactive monitoring to detect outages. Continuous monitoring has been a philosophy trumpeted time and time again by DevOps professionals to enable a better release frequency and a window to detrimental interruptions.

Careful analysis ahead of time, active monitoring systems to spot problems and continuously reviewing system logs were the most important tactics found being applied by IT leaders to mitigate found outages and brownouts.

Best Practices: Prevent, Detect, Mitigate
In terms of takeaways (other than the ultimate LogicMonitor product sell), the IT Downtime Detection and Mitigation Report does reveal some interesting trends on what IT leaders view to be leading causes of system outages. It will be interesting to see how this evolves and what technologies mature (or remain headaches) into the future.

The report leaves IT professionals with some common, albeit helpful, advice to consider going forward:

Actively monitor IT systems.
Optimize IT systems with forecasting.
Prepare for tomorrow’s IT norms today.
Use intelligent monitoring.
Design redundancy into your systems.

For Chilling Out

Stop, Breathe & Think

This meditation app opens with a simple check-in: you rate your physical and emotional state, and the app responds with tailored activities. Those might include guided breathing exercises, short meditations, or — occasionally — a calming compilation of cat videos. Activities are grouped by emotional tone, covering feelings like compassion, grief, and equanimity. Sessions are short enough to maintain focus but substantial enough to genuinely ease anxiety. The interface uses cool blues and approachable cartoon instructors to set the tone from the moment you open it.

For Gaming

Playing With Friends: Heads Up! on Houseparty

Heads Up! is a reverse-charades style game backed by Ellen DeGeneres. Houseparty is a group video-calling app that places multiple friends on screen simultaneously. When you can’t meet in person, combining the two is straightforward:

1. Download Houseparty.
2. Tap the dice icon in the upper-right corner.
3. Select Heads Up! from the drop-down menu.
4. Invite your friends and start playing.

Playing Solo: The Sims Mobile

The Sims franchise remains a reliable escape. In The Sims Mobile you control a virtual person who can shop, throw parties, go on dates, and interact with ghosts — essentially everything that indoor life temporarily limits. You can play it as wish fulfillment, or lean into the absurdity and trap your Sim inside the house by deleting all the doors. Either approach is oddly therapeutic.

For Learning

edX

edX provides access to thousands of university-level courses through its mobile app. Courses span subjects from computer science and data analysis to humanities and business, offered by more than 100 partner universities. Notable during the pandemic was Yale University making its popular “Happiness” course freely available to non-students. You won’t earn a formal degree through the free tier, but audit access to structured course content is a legitimate way to develop new skills or deepen existing knowledge.

Duolingo

Duolingo turns language learning into a habit through game-like mechanics: image-heavy lessons, streaks, XP points, and short daily sessions. The app offers more than 35 languages, including constructed languages like Klingon (from Star Trek) and High Valyrian (from Game of Thrones). Its gamified structure makes it easier to stay consistent compared to traditional study methods, though serious learners will eventually want to supplement it with other resources.

For Cooking

Project Foodie: Guided Cooking

Project Foodie is aimed at home cooks who want to go beyond their usual repertoire. It walks users through recipes via step-by-step video instruction led by professional chefs. Unlike broadcast cooking shows, the format doesn’t skip prep steps — every stage is demonstrated in full. This makes it genuinely useful for developing foundational culinary techniques like blanching, julienning, and proper knife work.

For Socializing

Nextdoor

Nextdoor connects you with people in your immediate neighborhood — many of whom you may never have met in person. The platform is organized by local area, making it useful for coordinating mutual aid, sharing local information, and checking in on neighbors who may need support. As with any social media platform, misinformation can circulate on Nextdoor, so approach unverified posts with appropriate skepticism and look to official public health sources for health guidance.

For Working Out

Aaptiv

Aaptiv delivers audio-guided workouts, meaning you follow spoken instructions rather than watching a screen. This makes it especially suitable for running, strength training, or yoga where you want to keep your phone in your pocket or on a surface without constantly glancing at it. The app recommends workouts based on your fitness goals and current level, making it a reasonable starting point if you’re building a routine from scratch.

Peloton

Peloton’s app extends well beyond its branded stationary bike. The video-guided content library includes strength training, yoga, boot camps, stretching, and cooldown classes. At the time of the original article’s publication (April 2020), Peloton was offering a 30-day free trial. Check the current Peloton website for up-to-date pricing and trial offers, as these change over time.

Runkeeper

Runkeeper tracks outdoor runs using GPS, logs distance and pace over time, and lets you monitor progress across sessions. The app includes training plans for different goals — from beginner 5K prep to longer distances — and previously offered rewards toward fitness gear for consistent use. It’s a straightforward tool for anyone using outdoor running as a daily mental reset.

For Staying Healthy and Informed

Apple’s COVID-19 App

During the pandemic, Apple released a dedicated COVID-19 app developed in collaboration with the CDC, FEMA, and the White House Coronavirus Task Force. It provided a symptom screening tool, state-specific guidance, and regularly updated public health information. Note: This app was specific to the 2020–2021 pandemic response period. Its current availability and relevance will have changed significantly since the original article was published. Check the Apple App Store and official public health sources for current tools.

For Escapist Entertainment

TikTok

TikTok’s short-form video format proved well-suited to periods of fragmented attention. The algorithm quickly surfaces content tailored to your interests — which means the experience varies dramatically from user to user. Beyond the dancing and lip-syncing that the platform is most associated with, TikTok hosts educational creators, comedians, journalists, activists, and community organizations. It’s worth exploring beyond the default feed to find content that is genuinely useful or entertaining to you. Be aware that TikTok’s data privacy practices have been subject to ongoing regulatory scrutiny in multiple countries; review current guidance if that is a concern.

A Note on This List

This article is based on a CNN list published in April 2020. App availability, features, pricing, and platform policies evolve continuously. Some apps mentioned — particularly those tied to pandemic-era initiatives — may have changed significantly. Always check the relevant app store listing or official website for the most current information before downloading.

Containers: A Fundamental Shift in How Software Is Delivered

Container and Docker technologies represent one of the most consequential shifts in modern IT infrastructure — a move away from heavyweight, monolithic machine-centric deployments toward lightweight, application-centric systems. Where traditional virtualization abstracts hardware, containers abstract the operating environment itself, letting developers ship code with all its dependencies in a single, portable unit.

This shift is being accelerated by several converging forces:

• Growing demand for automated, scalable, distributed infrastructure
• Faster application development cycles driven by CI/CD pipelines
• The rise of open-source ecosystems that have moved into commercial adoption
• Programming languages and frameworks that reduce the effort required to build and ship software
• An industry-wide push toward performance, reliability, and developer experience over raw hardware compatibility

At its core, a container solves a deceptively hard problem: how do you get software to run reliably and consistently when moved from one computing environment to another? Containers answer that by packaging the application code, runtime, system libraries, and configuration together into a single isolated unit.

How Containers Work

Containers share the host operating system kernel but isolate the application’s process space using Linux kernel features such as namespaces and control groups (cgroups). This makes them dramatically lighter than virtual machines: a container starts in seconds rather than minutes, and many containers can run on the same host with minimal overhead.

A container image bundles your application code together with its runtime dependencies — libraries, environment variables, configuration files, and anything else the app needs to run. That image can be version-controlled, stored in a registry, and deployed identically across a developer’s laptop, a staging server, or a production Kubernetes cluster. This reproducibility is the foundation of modern CI/CD workflows.

When combined with a service-oriented or microservices architecture, containers create clear boundaries between components. Each service can be developed, tested, scaled, and replaced independently, reducing coupling and accelerating iteration.

Why Containers Support Agile Engineering

Effortless Host Operations

Unlike traditional virtualization, where each virtual machine (VM) runs its own full OS, containers execute application processes in isolation from the host OS without requiring that OS to have application-specific software installed. This keeps the host lean and makes it straightforward to apply security patches and OS updates without worrying about breaking application dependencies.

Platform Independence

A container image carries everything the application needs to run: code, runtime, libraries, and configuration. This portability means the same container image can run on a developer’s local machine, a bare-metal server, a private cloud VM, or a public cloud instance — without modification. The phrase “works on my machine” effectively becomes irrelevant when the machine itself is packaged with the code.

Developer and Operations Productivity

Containers create a clean separation of concerns. Developers focus on application logic and declare dependencies in a Dockerfile or equivalent specification. Operations engineers take that image and handle deployment, scaling, and management — without needing to understand every nuance of the application’s internal dependencies. This separation reduces friction between development and operations teams, which is a central goal of DevOps practice.

Fault Isolation and Security Boundaries

Because containers run in isolated process spaces, a crash or compromise in one container does not automatically propagate to others running on the same host. If a container serving one microservice fails, the remaining containers continue operating unaffected. Similarly, a security breach contained within one container’s namespace limits the blast radius of an attack — though this is not a substitute for proper network policies, image scanning, and least-privilege configurations.

Code Reuse Through Images and Layers

Container images are built in layers, and base images can be shared across many applications. A team maintaining a hardened, patched base image can distribute it to all their application teams, who extend it with application-specific layers. Public registries such as Docker Hub provide community and vendor-maintained images that developers can pull as a starting point, reducing boilerplate and accelerating setup.

Linux Distributions Purpose-Built for Container Hosting

General-purpose Linux distributions include many packages and services that are unnecessary — and represent unnecessary attack surface — on a system whose sole job is running containers. Several distributions have been created specifically as minimal container hosts:

• Fedora CoreOS / Red Hat CoreOS: The successor to Container Linux and Project Atomic Host. Red Hat CoreOS is the worker node OS for OpenShift. Fedora CoreOS is the community upstream. Both use transactional updates for atomic OS upgrades, minimizing the risk of a broken update leaving nodes in an inconsistent state.
• Container Linux (formerly CoreOS Linux): One of the first minimal OSes designed from the ground up for running containers. CoreOS the company was acquired by Red Hat, and development has continued in the CoreOS lineage under Red Hat and Fedora projects.
• Ubuntu Core: A minimal, snap-based Ubuntu variant designed for IoT devices and large-scale cloud container deployments. It uses snaps for all system components, enabling transactional, rollback-capable updates.
• RancherOS: A minimalist Linux distribution where the entire OS runs as Docker containers — the system Docker runs infrastructure services, and a user Docker runs application containers. Note that RancherOS has been deprecated in favor of more modern Rancher offerings such as k3OS and Elemental.
• VMware Photon OS: A minimal Linux container host optimized for VMware platforms, including vSphere and VMware Cloud Foundation. It is maintained by VMware and is well-suited for teams operating in VMware-centric infrastructure environments.

Orchestration: The Missing Piece

Running a single container on a single host is straightforward. Running hundreds of containers across dozens of hosts — with health checks, rolling updates, service discovery, load balancing, and secrets management — is an entirely different challenge. That is the problem container orchestration platforms solve.

Kubernetes has become the dominant container orchestration system. It handles scheduling containers onto nodes, restarting failed containers, scaling workloads up or down, exposing services to internal and external traffic, and managing configuration and secrets. Docker Swarm is a simpler alternative suitable for smaller deployments. Managed Kubernetes offerings from major cloud providers — EKS, GKE, AKS — reduce the operational burden of running the control plane.

Adopting containers without an orchestration strategy works at small scale but becomes operationally unsustainable as the number of services and hosts grows. Planning for orchestration early is a prerequisite for a production-ready container strategy.

Key Takeaways

• Containers package code and all its dependencies into a portable, reproducible unit, solving the “works on my machine” problem.
• They are lighter and faster than VMs because they share the host kernel rather than emulating full hardware stacks.
• Containers improve developer productivity, enable clear DevOps role separation, and provide fault isolation between services.
• Minimal Linux distributions such as Fedora CoreOS, Ubuntu Core, and Photon OS are designed to reduce attack surface and operational overhead on container hosts.
• Container orchestration — most commonly Kubernetes — is essential for managing containerized workloads at production scale.
• A microservices architecture amplifies the benefits of containers by allowing independent development, deployment, and scaling of each service.

Container technology continues to evolve rapidly. The ecosystem now includes not just runtime and orchestration tooling but also service meshes, eBPF-based networking and observability, WebAssembly as a complementary runtime, and confidential computing extensions that bring hardware-level isolation to containers. Teams that invest in understanding the fundamentals — image construction, networking, storage, and orchestration — will be well-positioned to adopt these advances as they mature.

The Shadow IT Problem Is Bigger Than You Think

Do you know how much cloud and IT infrastructure your employees are actually consuming? For most organizations, the honest answer is: not entirely. Shadow IT — resources provisioned outside of officially sanctioned IT channels — represents a massive and often invisible drain on budgets and security posture.

According to research cited by the Everest Group, shadow IT spending accounts for 50% or more of total IT budgets at many organizations. The financial exposure is significant on its own, but the security risk compounds the problem further. Gartner has projected that a substantial share of security attacks would target shadow IT resources — systems and services that IT teams may not even know exist, let alone monitor or patch.

The takeaway is clear: shadow IT is not merely an accounting headache. It is an operational and security liability that, left unchecked, can expose organizations to breaches, compliance failures, and runaway spend.

Why Developers Go Rogue — and Why You Can’t Blame Them

DevOps and application teams are among the heaviest consumers of IT resources in any modern organization. They operate on tight release cycles, respond to shifting product priorities, and measure productivity in hours, not weeks. When they need a compute instance, a test database, or a staging environment, they need it now — not after a five-day ticket queue.

IT teams are not the villain here either. They are typically managing dozens of competing priorities, responding to incidents, maintaining compliance, and supporting the broader business. They are genuinely doing their best. But from a developer’s perspective, waiting days or even hours for a resource can feel like an eternity when a sprint deadline is looming.

The result is predictable: developers bypass IT, spin up resources on personal or departmental credit cards, choose whichever cloud service is fastest to provision, and — inadvertently — introduce security and cost risks the organization has no visibility into.

The Right Answer: Self-Service Provisioning With Guardrails

The goal is not to lock developers down further — that approach has already failed. The goal is to give developers a fast, sanctioned path to the resources they need, while preserving IT’s ability to enforce cost controls, security policies, and compliance requirements.

Self-service provisioning, backed by a curated service catalog, is the practical answer. Rather than forcing developers to navigate unfamiliar infrastructure systems (a process that introduces both delay and misconfiguration risk), a well-designed catalog surfaces only the resources relevant to a given team. Developers pick what they need, and provisioning happens in minutes rather than days.

Orchestration blueprints are a key enabler here. A blueprint is a reusable, pre-approved template that automates the provisioning of a defined set of resources — compute, networking, storage, permissions — in a consistent, policy-compliant way. Because the blueprint encodes IT’s security and configuration requirements, developers get speed without improvisation, and IT gets repeatability without manual review for every request.

Maintaining Control: Cost, Compliance, and Access Policies

Self-service does not mean ungoverned. IT teams can layer automated controls on top of the self-service model to prevent the cost and compliance problems that made shadow IT dangerous in the first place.

Cost Controls

• Automated quotas and spending limits: Cap how much compute, memory, or cloud spend any individual or team can consume in a given period.
• Power scheduling: Automatically shut down non-production VMs outside of working hours, preventing resources from running over weekends when no one is using them.
• Expiration dates on environments: Demo environments, staging clusters, and proof-of-concept deployments can be configured to terminate automatically after a set period, eliminating forgotten resources that silently accumulate cost.

This matters more than it might seem. Research attributed to ParkMyCloud suggests that roughly 44% of IaaS compute spend comes from non-production resources that are simply left running accidentally. Automated lifecycle policies directly address this waste.

Access and Approval Policies

• Role-based entitlements: Define who can request which types of resources — servers, workloads, XaaS offerings — and at what scale.
• Approval workflows: For higher-cost or higher-risk resource types, route requests through an approval chain before provisioning occurs.
• Real-time resource attribution: Maintain a live inventory of who is using which resources, so there are no surprises at month-end and no orphaned assets without an owner.

What Good Looks Like: IT as an Enabler, Not a Bottleneck

When self-service provisioning is implemented well, the dynamic between IT and DevOps shifts. Developers access pre-approved, correctly configured resources in seconds or minutes through a catalog designed for their needs. IT is no longer fielding repetitive provisioning tickets and can redirect that capacity toward higher-value work: architecture, security posture, platform improvements, and incident response.

Shadow IT shrinks not because developers are blocked from going around IT, but because the official channel is genuinely faster and easier than the workaround. When the sanctioned path removes friction instead of adding it, developers have no reason to go rogue.

Key Takeaways

• Shadow IT is a spending and security problem that grows when official IT channels are too slow or too complex for development teams.
• Self-service provisioning with a curated catalog is the most effective way to address the root cause, not just the symptoms.
• Orchestration blueprints enable repeatable, policy-compliant provisioning without requiring developers to understand underlying infrastructure details.
• Automated cost controls — quotas, power scheduling, expiration policies — prevent the runaway spend and forgotten resources that inflate cloud bills.
• Role-based access and approval workflows keep compliance intact without reintroducing the bottlenecks that drove shadow IT in the first place.

Taming shadow IT is ultimately about designing systems that make the right choice the easy choice. When IT becomes an enabler rather than a gatekeeper, both sides of the equation — developer velocity and organizational control — improve together.

Proliferation of hybrid and multi-cloud architectures has resulted in rapid and remarkable technological advances. But it has also introduced monumental, multi-faceted complexities into IT infrastructure management, including:

Managing multiple on-premises, private and public cloud resources
Complicated and lengthy provisioning processes for resource deployment
Continually evolving challenges maintaining visibility into multi-cloud ecosystems
Ongoing concerns around governance, cost control and security
In the face of these challenges, enterprise IT must enable innovation and digital transformation. Whether through self-service and intelligent automation, or through building a base of continuous insights driven by AI, machine learning, and deep learning, adopting these new paradigms has never been more critical. Simultaneously, IT needs to stay operationally vigilant regarding visibility, governance and cost control as organizations accelerate their hybrid cloud growth.

Unfortunately, many IT leaders are struggling to successfully adapt to these challenges. According to ESG’s 2019 Technology Spending Intention Survey, most executives view IT as a drag on — not a driver of — innovation. Specifically, a mere 6 percent consider IT a competitive differentiator, and a full quarter of executives actually see IT as a business inhibitor. In a rapidly changing world of cloud, there has to be a better way.

By adopting these five core principles of IT infrastructure management, IT leaders can become strategic business drivers and better meet end users where they are:

1. Self-service

To prevent bottlenecks, IT must institute a self-service delivery model enabling developers to access resources when they need them. When end users are able to quickly and easily access the resources they need – storage, compute, networking and more – without having to go through IT, organizations can reach a whole new level of agility and productivity.

2. Intelligent automation

Self-service depends on automation – intelligent automation. It must ensure the orchestrated delivery of IT resources — whether that’s infrastructure or advanced services such as IoT, blockchain and developer tools — fit the end user’s role and responsibilities as well as the needs of the business. With intelligent automation, IT teams can build security and cost guardrails into provisioning those resources, covering everything from access privileges to how long provisioned resources remain available in the cloud. The result? Better governance and cost control.

3. Comprehensive visibility

Business and IT leaders don’t need visibility into some of their infrastructure; they need visibility into all of their infrastructure — whether that’s virtual machines on-prem, to private cloud networks, or multi-tier, load balanced applications in AWS, Azure, GCP and others. And they need this visibility consolidated into a single view, the proverbial “single pane of glass.” Unified visibility enables modern IT leaders to always know where and how resources have been deployed, monitoring all usage from a single platform.

4. Modular extensibility

To swiftly adopt new technologies and leverage existing IT investments, modern IT leaders embrace modular, extensible frameworks. You need such frameworks — for example, modular plug-in in the form of scripts, web hooks and email notifications – to support a rapidly evolving array of development tools, paradigms and priorities. For example, the world of hybrid cloud can mean using a mashup of existing tools, whether that’s Terraform for provisioning, Ansible for configuration, Veeam for cloud migration and many more. IT teams need to go beyond the API and leverage extensibility that makes plug-and-play painless. The result? Less vendor-locking and the ability to build your hybrid cloud, your way.

5. Continuous insight

Unified visibility into IT resource deployment and usage depends on the capture of data across workloads, clouds and teams. This allows IT teams to quickly identify areas of risk, sprawl and inefficiency. More impactfully, it also enables IT to pursue data-driven resource planning. With increased accuracy in rate estimation, up-to-date views of inventory and predictive provisioning based on recognized patterns of usage, IT can proactively anticipate the needs of the business and application development teams, becoming the innovation enabler it should be.

Real-world examples: The Home Depot

Situation

The Home Depot had its sights set on continuous integration and continuous delivery (CI/CD), but getting a machine provisioned took anywhere from several days to a week, causing major bottlenecks in business productivity. Also, the company lacked an effective way to track and manage resources, particularly in its hybrid cloud environment.

Solution

The company developed a self-service model, providing developers with front-facing infrastructure services. The IT team now allows thousands of users to provision cloud-based resources without submitting a ticket. What once took one to two weeks now takes 20 minutes.

Furthermore, by implementing a more flexible framework built on modular extensibility for orchestration, Home Depot made integration with existing infrastructure easy. This extensibility supports Home Depot’s evolving IT strategy and facilitates the adoption and integration of Kubernetes, Docker, Terraform and other emerging technologies.

InterContinental Hotels Group (IHG)

Situation

IHG owns over 5,000 hotels and utilizes four different public clouds. They wanted the flexibility of multiple public clouds in order to use the platform that provides the optimal environment for each workload, as well as to protect the organization from price increases and instabilities from any one public cloud. As a result, administrators needed to understand and use four different interfaces to locate, manage and provision compute resources.

Solution

IHG moved all managed resources from VMware vRA to a cloud management platform that provided a single user interface and API, through which they could deploy complex apps to VMware and any of the four public clouds they used. This enabled them to manage these apps and their servers over the course of their lifecycle, allowing for business policy automation as well as adoption of best practices around server deployment and management.

IHG now also uses comprehensive visibility and intelligent automation to control VM sprawl and protect against unexpectedly large bills from vendors. Through the use of expiration dates for servers, configurable expiration behavior, visibility into all VMs across all technologies, quotas, cost tracking and easy de-provisioning of servers and services, IHG ensures that organizations only pay for what they need and use.

The 5 principles and your organization

To tame enterprise IT infrastructure complexity, IT leaders must adopt a flexible and extensible model for IT infrastructure management that relieves pressure on IT while freeing both the enterprise and developer teams to take advantage of emerging technologies. By adopting the five core principles of IT infrastructure management, your organization can improve time-to-value for your hybrid cloud and multi-cloud management strategy.

The Digital Transformation Pressure on Healthcare IT

Healthcare organisations are under relentless pressure to modernise. Mergers, acquisitions, expanding patient populations, and an accelerating reliance on digital tools are forcing IT teams to deliver more with tighter budgets and leaner staff. Unlike most industries, healthcare carries an additional layer of complexity: downtime is not merely costly — it can directly affect patient outcomes.

Martin Bradburn, CEO of Peasoup Hosting, frames the opportunity clearly: “For the healthcare sector, cloud infrastructure with its security and scalability can, and in some instances is already accelerating the development of clinical applications. Digital transformation is also changing the way healthcare operates, connecting remote clinical specialist resources directly to the patients for improved diagnosis on a worldwide scale.”

Cloud infrastructure’s ability to process and deliver data collaboratively — and to charge organisations only for what they consume — directly addresses two chronic problems in healthcare IT: over-provisioning and budget rigidity.

What Is Hyperconverged Infrastructure (HCI) and Why Does It Matter for Healthcare?

Hyperconverged infrastructure (HCI) combines compute, storage, networking, and virtualisation into a single software-defined system — typically deployed as a cluster of commodity nodes. Instead of managing separate silos of servers, storage arrays, and networking hardware from different vendors, an HCI platform presents a unified management interface.

David Trossell, CEO and CTO of Bridgeworks, explains the value for smaller healthcare providers specifically: “By consolidating down multiple technologies from multiple providers for all the separate equipment that forms a modern data centre, along with all the differing support contracts and possible interoperability issues that can occur, hyperconverged brings this all down to one or two providers.”

Key Benefits of HCI in Healthcare Settings

• Reduced physical footprint: Fewer physical appliances means lower power consumption, reduced cooling requirements, and smaller data centre floor space.
• Simplified management: A single management plane reduces the skill sets needed to operate the environment and cuts the number of support contracts from many to one or two.
• Higher resilience: Fewer interconnecting components means fewer potential points of failure and fewer bottlenecks under load.
• Easier cloud extension: HCI platforms are designed to extend workloads elastically into the cloud, removing the traditional constraint of fixed on-premises capacity.
• Predictable scaling: Organisations can add nodes incrementally as demand grows rather than making large, speculative hardware purchases.

David Lehr, CIO of Luminis Health (a regional healthcare system in Maryland), articulates the governance dimension well: “We need to be good stewards of the investments our communities make in us. Driving up unnecessarily high costs on complicated IT infrastructure that takes an army of people to manage isn’t a great way to live up to that expectation.”

Cybersecurity Challenges: HCI Is Not a Silver Bullet

Hyperconvergence solves many operational problems inside the data centre, but it does not eliminate external threats — and those threats are intensifying. Trossell is direct on this point: “The biggest threats these days emanate from outside of the data centre in the form of cyber-attacks, and these attacks are getting more and more sophisticated in their approach.”

Critically, attackers are no longer satisfied with encrypting live data. Modern ransomware campaigns actively target backup software and backup repositories, eliminating the traditional recovery option of simply restoring from a clean copy. This forces healthcare organisations to adopt multi-layered, multi-location data protection strategies rather than relying on a single backup destination.

“Until the last byte of backup data has been received by the cloud, there is no backup.”

— David Trossell, CEO and CTO, Bridgeworks

The implication is architectural: backups must be replicated to multiple geographically separated locations, and the pipeline between on-premises systems and those locations must be reliable enough to complete backup windows within agreed time frames.

The Hidden Bottleneck: WAN Performance

Moving data reliably to the cloud — whether for backup, disaster recovery, or active workloads — depends on the performance of the Wide Area Network (WAN) link connecting on-premises infrastructure to cloud endpoints. Trossell identifies three governing factors:

1. Latency — the round-trip delay between sender and receiver, which worsens over longer distances.
2. Packet loss — even small percentages of dropped packets force TCP retransmissions, which dramatically reduce effective throughput.
3. Bandwidth — the raw capacity of the WAN link.

The common assumption is that sluggish data transfers are a bandwidth problem, solved by purchasing more bandwidth. In practice, Trossell notes, the existing bandwidth is often adequate. Latency and packet loss — not raw capacity — are the limiting factors, particularly when transferring large medical imaging files such as MRI or CT scans across geographic distances.

Why WAN Optimisation and SD-WAN Fall Short

Two technologies are commonly deployed to address WAN performance problems:

• WAN optimisation — historically relied on data deduplication and compression to reduce the volume of data traversing the link. When data is encrypted end-to-end (as it must be in healthcare), these techniques cannot inspect payloads and therefore have little effect.
• SD-WAN — improves traffic routing and link aggregation across multiple WAN paths, but does not fundamentally address the physics of latency and packet loss on any individual path.

WAN Data Acceleration: A Different Approach

WAN data acceleration takes a fundamentally different approach. Rather than compressing or deduplicating data, it uses parallelisation techniques — managed by artificial intelligence — to control the flow of data across the WAN in a way that actively compensates for latency and packet loss effects. Trossell explains: “WAN acceleration approaches the transport of data in a new way. Rather than trying to squash the data down, it uses parallelisation techniques controlled by artificial intelligence to manage the flow of data across the WAN, whilst mitigating the effects of latency and packet loss.”

Importantly, the approach is data-agnostic: it does not alter, inspect, or decompress the data itself, making it compatible with encrypted payloads. It can also be deployed alongside existing backup tools and SD-WAN implementations rather than replacing them. According to Trossell, properly implemented WAN acceleration can push WAN bandwidth utilisation toward 95%, meaning organisations may find that their current bandwidth is already sufficient once latency and packet loss are mitigated.

Team Collaboration and Real-Time Data in Healthcare

Clinical workflows are increasingly distributed. Remote diagnosis, telemedicine, multi-site specialist consultations, and real-time surgical video all depend on low-latency, high-throughput connectivity. Bradburn notes: “The cloud environment is perfect for big data sets — archiving, pulling out and data manipulation is fast and effortless. This is vital for all services when the response is urgent.”

A cloud environment can also serve as an air-gapped backup location when configured correctly, and when bundled with cloud-native disaster recovery services, it provides a meaningful layer of resilience against ransomware and other destructive attacks.

Data Sovereignty and Compliance

Healthcare data is among the most sensitive personal data that exists. Regulatory frameworks — including HIPAA in the United States and GDPR in the European Union — place strict requirements on where patient data can be stored, how it must be protected, and how breaches must be disclosed. When selecting cloud providers or configuring multi-site replication, healthcare organisations must verify:

• Which countries host the data centres used for primary storage and replication.
• Whether the provider’s terms of service permit cross-border data transfers.
• What certifications (ISO 27001, SOC 2, HIPAA BAA, etc.) the provider holds.
• What the full cost model looks like — some providers offer low per-unit storage costs but charge materially for data ingress and egress, which can significantly increase total cost.

5 Practical Tips for Accelerating Your Healthcare Cloud

Drawing on the expert commentary in this article, here are five actionable recommendations for healthcare IT teams evaluating or modernising their cloud and hyperconverged strategy:

1. Scrutinise cloud supplier pricing carefully. Smaller cloud providers often offer simpler, more predictable pricing with unlimited bandwidth. Larger hyperscalers may charge separately for data ingress and egress — costs that can double the effective price of a solution. Always review the full contract, including annexes.
2. Consider green and efficient infrastructure. Newer hardware generations deliver better performance at lower power draw. Some cloud providers operate ecological data centres with liquid cooling, which supports higher-density, higher-throughput workloads — relevant for imaging-heavy environments.
3. Verify data sovereignty before committing. Confirm that patient data will be stored and replicated within jurisdictions that satisfy your regulatory requirements (HIPAA, GDPR, or applicable national law). Do not assume compliance — obtain written confirmation and check audit reports.
4. Address latency and packet loss explicitly. Do not assume that purchasing more bandwidth will solve data transfer problems. Evaluate WAN data acceleration to mitigate latency and packet loss, particularly for backup pipelines and large medical imaging workloads. SD-WAN and WAN acceleration are complementary, not mutually exclusive.
5. Evaluate hyperconverged infrastructure on its total cost of ownership. Beyond the capital cost, weigh the reductions in management complexity, support contract overhead, power and cooling spend, and the operational risk reduction that comes from fewer interdependent components.

Looking Ahead: Standards, Interoperability, and Global Care

Bradburn identifies the next frontier: building the standards and interoperability frameworks that would allow a truly global healthcare network to function. This means agreeing on data formats from clinical systems and personal health IoT devices so that information is universally interpretable regardless of where it originates.

The vision is ambitious — clinicians sharing imaging, diagnostic data, and treatment protocols across borders in real time, enabling remote specialist consultation at scale. Realising it requires healthcare organisations to invest now in the network infrastructure (WAN acceleration, resilient cloud connectivity), the data governance frameworks (sovereignty, compliance, security), and the architectural patterns (hyperconvergence, hybrid cloud) that will underpin those future workflows.

As Bradburn concludes: the true power of the cloud in healthcare services will only be unlocked when the underlying network, storage, and security infrastructure is built to match the ambition of the clinical use cases it must support.

Expert commentary in this article is attributed to Martin Bradburn, CEO of Peasoup Hosting; David Trossell, CEO and CTO of Bridgeworks; and David Lehr, CIO of Luminis Health. The original article was published by cloudcomputing-news.net.

Why AIOps Needs a Structured Deployment Plan

Applying artificial intelligence and machine learning to IT operations — commonly called AIOps — promises faster incident resolution, smarter alert management, and reduced operational noise. The category has produced a wave of specialist vendors and significant enterprise interest. Yet many IT leaders are rightly cautious: an AIOps tool that misfires early can erode both team confidence and executive sponsorship in ways that are hard to recover from.

The antidote is process. Before you license, configure, or run a single model, you need a clear workflow that takes the project from problem definition through to production and continuous refinement. The following seven steps provide that structure.

Step 1: Plan

Resist the temptation to boil the ocean on day one. Choose one or two specific use cases that have measurable pain points and a team that is genuinely open to change. Common starting points include alert noise reduction, automated ticket routing, and anomaly detection on a bounded service or application tier.

During planning, address the following areas:

• Skills inventory: Identify where you have gaps in data science, automation engineering, DevOps, and continuous integration. IT Ops staff need enough working knowledge of machine learning to audit model behaviour once automated control is active — they do not need to become data scientists, but blind trust in a model is risky.
• Workflow impact: Map the operational changes required for your chosen use case. For example, if the AI system is correlating alerts across an e-commerce platform, define what happens when correlated alerts span multiple teams and who owns the escalation path.
• Data readiness: Understand what data the tool requires and whether that data is available natively or needs to be sourced from a CMDB or another system of record. Missing context at ingestion time will degrade model accuracy.
• Goal setting: Define measurable targets — for example, a percentage reduction in alert volume, a reduction in mean time to resolve (MTTR), or a decline in ticket count. Establish how results will be measured and communicated to stakeholders.
• Model training expectations: Document what trainers and operators should expect during the early weeks, when the ML model is still immature and generating lower-confidence outputs. Setting realistic expectations early prevents early abandonment.

Step 2: Socialize

Technical readiness means little if the people affected by the system are not brought along. AIOps triggers legitimate concerns about job displacement, changed responsibilities, and loss of control over operational decisions. Address these concerns directly and early.

Explain how the system works at a conceptual level, what problems it is solving, and how it will change day-to-day workflows. Identify internal evangelists — operators or engineers who understand the technology and are willing to advocate for it — and empower them to answer peer questions and support informal training. Broad organizational buy-in reduces resistance when the system enters production and makes feedback loops faster and more honest.

Step 3: Understand the System’s Capabilities

Before configuration begins, invest time in learning how the chosen AIOps platform actually works, not just what the vendor claims it does. Focus on:

• Data dependencies: Some use cases require additional data to function correctly. Alert correlation, for instance, may need topology or dependency mapping data to validate that a relationship between alerts is genuine rather than coincidental.
• Supported use cases: Confirm which problems the platform addresses out of the box. Common capabilities include anomaly detection, event correlation, alert suppression, and automated ticket routing. Prioritize the use cases most likely to produce quick, visible wins with the lowest operational risk.
• Model transparency: Understand how the platform surfaces its reasoning. Operators who cannot see why a conclusion was reached will struggle to audit or trust the system.

Step 4: Set Up and Observe

Configure the system for your selected use cases and put it into observe-only mode — the system watches live data, applies its algorithms, and generates suggestions, but takes no autonomous action yet. This phase serves two purposes: it validates that the configuration is correct, and it builds operator familiarity with how the system reasons.

A practical signal of solution quality emerges here: if the platform requires weeks of complex configuration before the ML layer can even begin producing outputs, that is worth flagging as a risk. Modern AIOps tooling should reach an observable state relatively quickly. Operators should be able to inspect how algorithms interact with incoming data and trace how specific conclusions were reached — transparency is not a nice-to-have, it is a prerequisite for trustworthy automation.

Step 5: Recommend — Controlled Automation in a Safe Environment

AIOps delivers compounding value when it handles routine, predictable events autonomously. In this step, IT Ops defines a category of well-understood, low-risk events — such as VM utilization threshold breaches or known patch update sequences — and configures the system to apply a standard remediation automatically.

Run this automated handling in a sandbox or against a non-critical workload first. Monitor results closely. The goals at this stage are to verify that automated responses are accurate, that standard procedures are being followed consistently, and that automated handling is not inadvertently masking conditions that would otherwise escalate. Success here is the foundation for expanding automation to higher-stakes workloads.

Step 6: Deploy to Production

Once pilot results are acceptable and stakeholders are comfortable with the recommendations the system is producing, move to a production deployment. Do not treat this as a hard cutover. Run the system in a shadow or test mode alongside production operations for at least two weeks, confirming that outputs remain accurate under real load and that operators find the guidance actionable.

Collect structured feedback from users during this period. Issues that did not surface in the controlled pilot environment will appear under the variety of real-world conditions, and catching them before full autonomy is granted limits operational impact.

Step 7: Review and Refine

After a few weeks of production operation, measure results against the goals established in Step 1. If the target was alert noise reduction, quantify the change. If it was MTTR improvement, compare pre- and post-deployment baselines. Share results transparently with stakeholders.

Supplement quantitative metrics with qualitative feedback. Survey operators about their experience: what is working, what is creating friction, and what they wish the system did differently. Use this input to retrain the model, adjust configurations, or — once the initial use cases are performing well — expand to additional use cases.

AIOps is not a deploy-and-forget technology. The operational environment changes, new alert patterns emerge, and model drift is a real phenomenon. Building review cycles into your operating rhythm ensures the system continues to deliver value rather than quietly degrading.

Summary: The 7-Step AIOps Deployment Framework

AIOps implementations do not need to be high-risk undertakings. Breaking the project into discrete, verifiable stages — each with clear criteria for moving forward — keeps the team in control, builds trust incrementally, and produces a system that operators genuinely rely on rather than work around.

The following 13 bad practices are among the most common sources of preventable IT bottlenecks. Each one is paired with practical guidance on how to address it.

1. Lack of Communication from Company Leadership

When business-line owners fail to share long-term strategy with IT teams, developers are forced to make architectural decisions without understanding future requirements. The result is brittle solutions that need expensive rework as the business evolves.

Fix it: Establish regular strategic briefings between business stakeholders and engineering leads. Product roadmaps should be visible to the development teams building against them, not just to management.

2. Starting Quality Assurance Too Late

Treating QA as a final gate before release creates a predictable crunch. Defects found late are exponentially more expensive to fix, and a QA backlog at the end of a sprint can stall entire releases.

Fix it: Adopt a shift-left testing approach. Involve QA specialists from the requirements phase onward. Pair continuous integration (CI) pipelines with continuous testing so defects surface within minutes of a commit, not days before release.

3. No Plan for Managing Work Peaks

IT workloads are inherently uneven. Organizations that staff and plan only for average load will be overwhelmed when multiple high-priority projects converge, a product launch hits, or an incident response demands full attention simultaneously.

Fix it: Model your team’s capacity with peak scenarios in mind. Establish pre-vetted relationships with third-party partners or staffing agencies who can absorb overflow work. Document escalation plans before the crunch arrives, not during it.

4. Insufficient Automation

Manual processes are a hidden tax on engineering capacity. Tasks such as employee onboarding and offboarding, compliance checks, software license audits, and environment provisioning are time-consuming when done by hand — and error-prone.

Fix it: Audit your team’s recurring manual tasks and prioritize automating the highest-frequency, lowest-complexity ones first. Evaluate your SaaS stack for integration gaps — missing integrations between tools are often the root cause of manual workarounds. Apply the principle that if a machine can reliably do it, a person shouldn’t have to.

5. Unclear Lines of Responsibility

Ambiguous ownership is a reliable bottleneck generator. When two teams share overlapping functions — such as business analysis and technical analysis — tasks fall through gaps or get duplicated, both of which slow delivery.

Fix it: Use a RACI matrix (Responsible, Accountable, Consulted, Informed) to define ownership for each process and deliverable. Review it whenever team structures change. Make sure every work item in your backlog has a single accountable owner.

6. Waiting for Approvals to Move Forward

Approval chains that require synchronous sign-off from multiple stakeholders are one of the most common velocity killers in IT delivery. The larger and more distributed the organization, the worse this gets.

Fix it: Identify which approvals are truly mandatory versus habitual. For the mandatory ones, define SLAs so approvers respond within a set timeframe. In parallel, find work items that are not blocked and can be progressed independently. Asynchronous, documented approval workflows (rather than meeting-dependent ones) dramatically reduce wait time.

7. Patching Systems at Peak Times

Applying patches or making infrastructure changes during peak business hours is a high-risk practice that can bring systems down when the impact is greatest. A maintenance window that seems convenient for the engineer can be catastrophic for operations.

Fix it: Establish a change management policy that mandates low-traffic maintenance windows — typically nights or weekends — for any change that carries downtime risk. Require a rollback plan before any patch is applied. Use canary deployments or staged rollouts where possible to reduce blast radius.

8. Over-Reliance on Point-to-Point Data Integration

Traditional data integration projects — building pipelines between systems — tend to be expensive, time-consuming, and temporary. Every time a new system is introduced, the web of integrations needs to grow, compounding maintenance burden and creating fragile dependencies.

Fix it: Evaluate architectural approaches such as event-driven architectures, data mesh, or data fabric concepts that reduce tight point-to-point coupling between systems. Before starting a new integration project, ask whether a more general-purpose platform capability could solve the problem more durably.

9. Bypassing the CISO and IT Security Team

Shadow IT — where business units procure tools or vendors without involving the security or IT team — is often motivated by a desire to avoid a perceived bottleneck. In practice, it creates far larger risks: unvetted vendors, data exposure, and compliance gaps that are expensive to remediate.

Fix it: Make the security review process fast enough that teams aren’t tempted to circumvent it. Publish a vendor approval checklist so requestors know what to prepare. A lightweight, time-boxed review process is better than one that takes months and drives people to go around it.

10. Being Reactive Instead of Proactive

IT teams that spend most of their time in reactive mode — responding to incidents, handling ad-hoc requests, and cleaning up problems after they escalate — have little bandwidth left for prevention or improvement. The reactive cycle is self-reinforcing.

Fix it: Reserve a defined percentage of sprint capacity for proactive work: monitoring improvements, technical debt reduction, runbook creation, and process automation. Use metrics such as mean time to detect (MTTD) and mean time to resolve (MTTR) to track whether your reactive burden is decreasing over time.

11. Poor Management of Test Environments

Automated testing delivers value only when the environments it runs against are stable and representative. Flaky or poorly maintained test environments produce unreliable results, erode trust in the test suite, and introduce delays when engineers can’t determine whether a failure is a genuine bug or an environment issue.

Fix it: Treat test environment provisioning as a first-class engineering problem. Use infrastructure-as-code (IaC) to make environments reproducible and version-controlled. Define environment ownership and establish SLAs for environment availability. Consider ephemeral environments spun up per pull request to eliminate contention.

12. Overlooking Workflow Mapping

Bottlenecks often hide inside the end-to-end flow of information across teams. Without explicitly mapping how work moves from inception to delivery, it is easy to optimize local steps while leaving the actual constraint untouched.

Fix it: Conduct value stream mapping exercises to visualize where work waits, where handoffs occur, and where rework loops exist. Tools such as Kanban boards can surface flow problems in real time. Engage each business unit to understand how they receive and send information to IT — the bottleneck often lives at the interface between teams, not within them.

13. Requiring Universal Buy-In for Every Decision

Highly hierarchical organizations can grind to a halt when every decision requires sign-off from a large group of stakeholders who are distributed across time zones, have competing priorities, or simply have difficulty reaching consensus. The cost of consensus is speed.

Fix it: Adopt a decision-making framework that distinguishes reversible from irreversible decisions. Empower smaller groups — or individual decision-makers — to act on time-sensitive, reversible choices without requiring full committee approval. Reserve broad sign-off for strategic or high-risk decisions that genuinely warrant it. Document the rationale for decisions asynchronously so stakeholders stay informed without blocking delivery.

Key Takeaways

• Most IT bottlenecks are process and culture problems, not technology problems.
• Automation, clear ownership, and proactive planning remove the most common sources of delay.
• Shift-left practices in testing and security review prevent bottlenecks from forming at the end of delivery pipelines.
• Decision-making structures that empower teams — rather than require universal consensus — are a significant competitive advantage in fast-moving environments.
• Mapping your workflow end-to-end is the fastest way to find the real constraint, as opposed to optimizing steps that aren’t the limiting factor.

Addressing even two or three of these practices systematically will have a measurable impact on delivery throughput, team morale, and system reliability. Start by identifying which bottleneck is costing your organization the most right now, fix it, then move to the next.

In this special guest feature, Jay Budzik, CTO at ZestFinance, discusses the set of developer tools that Google launched that allow data scientists to create explainable ML models, and also the reality of these new tools and how they are likely to impact the financial services market. ZestFinance is a software company that helps banks and lenders build, run, and monitor fully explainable machine learning underwriting models. As CTO, Jay oversees Zest’s product and engineering teams. His passion for inventing new technologies—particularly in data mining and AI—has played a central role throughout his career. Jay has a Ph.D. in computer science from Northwestern University.

Algorithmic models get a bad rap for being black boxes prone to unfair bias. They’re not—if you have the right tools to explain their decisions.

That’s why Google’s new suite of cloud-based explainable AI (XAI) tools is a step in the right direction for companies looking to adopt AI. These tools decipher, and defend, how specific data factors contribute to the output of machine learning (ML) models built atop complex neural networks.

While Google XAI should draw welcome attention and help bust a few ML myths, it likely won’t spur adoption for all companies—especially financial-services firms that need fully interpretable underwriting models to decide who can borrow and who can’t.

First, Google’s tools (now in beta) require customers to build and implement their models within the Google Cloud. But most companies need explainability tools that can be used in any cloud environment or on local servers.

Second, Google XAI isn’t flexible enough to accommodate more robust models. Like other explainability tools, Google’s are rooted in complex mathematical principles, including a version of the Aumann-Shapley method that uses a series of foils—called “counterfactuals”—to perfect an algorithm’s output. Andrew Moore, head of Google Cloud’s AI unit, described the process to the BBC in London last month: “The neural network asks itself, for example, ‘Suppose I hadn’t been able to look at the shirt color of the person walking into the store. Would that have changed my estimate of how quickly they were walking?’ By doing many counterfactuals, it gradually builds up a picture of what it is and isn’t paying attention to when it’s making a prediction.”

As compelling as that approach sounds, Google XAI won’t work with so-called ensembled models that weave together multiple models using diverse or competing techniques. “Ensembling” boosts the predictive power of AI credit scoring—assuming the collective system can be examined to assess how decisions are being made—and will become standard as lenders embrace ML over the next few years. For now, Google provides limited support for tree-based models and ensembles of trees and neural networks.

Third, while the Google AI “What-If” tool is pretty clever and allows modelers to test different scenarios at a glance, Google’s user interface could be tricky to use. Developers will have to learn a specific coding language and configuration convention to access explainability functions.

Last, Google’s explainability package is aimed primarily at data scientists—not credit analysts working for heavily regulated financial-services firms. In theory, data scientists at banks could build models using Google’s tools. But those same folks would need to build additional tools to test their models for accuracy and fairness and to generate all the compliance reporting required by regulators.

So while Google now offers a solid explainability tool for cloud-based neural networks, the vast majority of end-users will continue to demand more complete solutions that address their regulatory risk. Those solutions should include managed services (including expert help from a dedicated team), risk-management documentation (MRM) and model monitoring tailored for that industry, underwriting expertise and experience working with the largest lenders and, not least, a track record of deploying models that surpass regulatory scrutiny.

Google is taking important first steps to bring more transparency to the world of ML. That’s encouraging because the sooner our industry cracks open those scary black boxes, the better—for lenders, consumers, and the tech community.

The Promise and the Problem

What if interacting with your devices felt as natural as chatting with a colleague? Voice is our primary communication channel as humans, and it’s increasingly the interface we expect from the machines around us — smart speakers, cars, wearables, home automation systems, and enterprise software alike.

Advances in speech recognition, neural networks, and raw compute power have made voice computing commercially viable. Virtual assistants now ship in everything from microwave ovens to eyeglasses. Yet despite this momentum, conversational AI still struggles with what toddlers do effortlessly: hold a simple, contextual conversation.

Understanding why that gap exists — and how engineers are closing it — is essential knowledge for anyone building or integrating AI-driven products today.

The Current Landscape

Modern AI can drive cars, optimize global supply chains, beat grandmasters at chess, and surface cancer-treatment candidates from medical literature. Yet most AI systems still falter at casual, open-ended conversation — the kind you’d have with a neighbor over a fence.

Projects like Google Duplex demonstrated that narrow, task-specific conversational AI (e.g., booking a restaurant reservation) can sound remarkably human. But generalized, contextually aware conversation remains an unsolved problem at scale.

Demand for conversational AI is accelerating regardless. According to eMarketer (figures cited in the original Forbes piece, dated early 2020), nearly 40% of internet users were already using voice assistants such as Google Assistant, Alexa, Siri, Bixby, and Cortana. The research firm MarketsandMarkets projected the global conversational AI market to grow at a compound annual growth rate of just over 30% between 2019 and 2024.

Gartner, also cited in the original article, predicted that by 2021 roughly one in six customer service interactions globally would be handled by AI — but also warned that approximately 40% of chatbot and virtual assistant applications launched in 2018 would be abandoned by 2020. That second figure is a useful reminder: deploying conversational AI is easy; making it good enough that users keep using it is hard.

How Conversational AI Actually Works: A Six-Stage Breakdown

To understand where conversational AI succeeds and fails, it helps to decompose a single human conversational exchange into its constituent stages. Each stage maps to a distinct technical problem.

Stage 1 — Speech Recognition (Speech-to-Text)

The system must capture audio, filter background noise, compensate for accents and speech impediments, and convert the acoustic signal into a sequence of words. This is the Automatic Speech Recognition (ASR) layer. Modern ASR engines — powered by deep learning architectures like recurrent neural networks (RNNs) and, more recently, transformers — have become remarkably accurate under controlled conditions. Real-world noise, multiple simultaneous speakers, and heavy accents still degrade performance significantly.

Stage 2 — Semantic Understanding

Once words are transcribed, the system must resolve ambiguity. The word bank means something different depending on whether the surrounding conversation is about fishing, finance, or billiards. Homophones (there/their/they’re), homonyms, and domain-specific jargon all require the system to lean on surrounding context rather than dictionary definitions. This is where many lightweight chatbots begin to fail.

Stage 3 — Natural Language Understanding (NLU)

NLU goes further than semantics. It requires the system to interpret intent by layering multiple types of context:

• Conversational context: What has already been said in this session?
• Personal context: Who is the speaker, and what is their history with the system?
• Situational context: What is happening in the user’s environment right now?
• World context: Are there external events (weather, news, calendar) that influence the meaning of the utterance?

A question like “Does it look like rain?” asked on a cloudy afternoon is rarely a meteorological inquiry in isolation — the user probably wants to know whether to pack an umbrella or reschedule outdoor plans. Capturing that underlying intent is what NLU aims to do, and it remains one of the hardest open problems in the field.

Stage 4 — Response Generation

Once intent is understood, the system must determine what kind of response actually satisfies the user’s need. Consider the query: “Do you accept American Express?” A literally correct answer — “No” — is not particularly helpful. A more useful response would be: “No, but we accept Visa and Mastercard.” Generating contextually appropriate, intent-satisfying responses requires the system to model the user’s goal, not just parse their words. Because NLU is imperfect, response generation inherits those imperfections.

Stage 5 — Natural Language Generation (NLG)

NLG is the process of expressing the intended response in fluent, natural-sounding language. Rule-based and template-driven systems produce output that sounds robotic precisely because they treat NLG as a fill-in-the-blanks problem. Neural approaches — including large language models — generate more natural text but can introduce hallucinations, inconsistencies, or off-topic content. Striking the right balance between fluency, accuracy, and safety is an active area of research and engineering.

Stage 6 — Speech Synthesis (Text-to-Speech)

The final stage converts the generated text back into audio. Modern Text-to-Speech (TTS) engines — including WaveNet-based and neural TTS systems — produce voices that are increasingly difficult to distinguish from human speech. Like Stage 1, TTS has matured considerably. The hard problems live in Stages 2 through 5.

Why This Pipeline Is Hard to Get Right

Humans execute all six stages simultaneously and unconsciously — often while performing other complex tasks — in a fraction of a second. We also do it adaptively, correcting misheard words mid-stream, adjusting our vocabulary for the listener, and reading emotional subtext.

Machines must do this explicitly, stage by stage, with each layer accumulating error from the previous one. A small ASR mistake at Stage 1 can cascade into a completely wrong response by Stage 4. And in noisy real-world environments — exactly where voice interfaces are often expected to work — ASR accuracy drops, magnifying downstream errors.

Research efforts like Google Brain’s Meena (introduced around early 2020) demonstrated that end-to-end neural conversational models — trained on massive dialogue datasets — could produce more coherent multi-turn conversations than earlier rule-based or retrieval-based chatbots. End-to-end training allows the model to learn conversational flow holistically rather than optimizing each stage in isolation. However, even these models struggled with factual accuracy and consistency across longer conversations.

Practical Applications Already Deployed

Despite these challenges, conversational AI is already delivering real value across a range of enterprise and consumer scenarios:

• Customer service automation: Chatbots and voice bots handle tier-1 support queries, reducing agent load and improving response times.
• Personalized customer experiences: Voice assistants adapt responses based on user history and preferences.
• Sentiment analysis: ML models score customer interactions to flag dissatisfaction or escalation risk in real time.
• Multilingual support: Neural machine translation integrated into conversational pipelines enables assistants to serve global audiences without separate per-language development efforts.
• Smart home and IoT control: Voice interfaces manage thermostats, lighting, security systems, calendars, and online shopping.
• In-vehicle assistants: Hands-free voice control for navigation, calls, and media in connected vehicles.

Most of these capabilities are now available as cloud APIs — from providers such as Google, Amazon, Microsoft, and IBM — meaning teams can integrate sophisticated conversational AI into their products without building NLU or NLG models from scratch.

What Engineers and DevOps Teams Should Watch

If you’re evaluating or deploying conversational AI components, keep the following in mind:

• Failure modes cascade: Monitor each pipeline stage independently. An uptick in ASR word error rate will show up as degraded NLU accuracy before users start complaining explicitly.
• Abandonment is the real metric: A chatbot that works in a demo but frustrates users in production will be abandoned quickly. Instrument completion rates and fallback rates from day one.
• Context window management: Multi-turn conversation quality depends heavily on how well the system maintains and uses conversational history. Session context that is too short produces repetitive, disjointed responses.
• Template-driven NLG does not scale: Rule-based response templates become unmaintainable as use cases grow. Plan for a migration path to model-driven generation if you expect to expand the assistant’s capabilities over time.
• Latency matters for voice: Users tolerate much shorter response delays in voice interfaces than in text chat. End-to-end pipeline latency must be optimized, often requiring edge inference for the ASR stage.

The Road Ahead

Conversational AI has made extraordinary progress, but the gap between today’s systems and genuinely human-like conversation remains real and significant. The most useful near-term advances are likely to come from better contextual modeling, improved factual grounding, and tighter integration between the discrete stages of the pipeline.

For practitioners, the opportunity lies in building systems that are honest about their limitations — gracefully handing off to a human agent when confidence is low, rather than confidently generating a wrong answer. That engineering discipline, more than any single model breakthrough, is what will determine whether the next generation of conversational AI sticks or gets abandoned.

The Mainframe Is Not Going Away — But Its Tooling Needs to Evolve

For decades, mainframe developers relied on tools like ISPF green screens and the Eclipse desktop IDE augmented with proprietary plugins. These tools served their purpose well, but the wider software development world has moved on — and mainframe tooling must follow.

Broadcom highlighted this challenge in the Arcati Mainframe Yearbook, pointing to two converging pressures: a generational shift in the developer workforce and a dramatic acceleration in software delivery expectations.

A Workforce Shift and the Rise of Modern IDEs

Experienced mainframe engineers are retiring at a steady pace. The developers stepping in to replace them have grown up with modern toolchains and expect to keep using them. According to data cited from the Stack Overflow Developer Survey, Visual Studio Code’s popularity among developers grew from 34.9% to 50.7%, while Eclipse declined from 18.9% to 14.4% over the same period. Python emerged as the most sought-after scripting language skill.

These preferences are not just cosmetic. Developers who are comfortable in VS Code or browser-based environments will be less productive — and less happy — when forced into legacy toolchains. For enterprises that depend on mainframe talent pipelines, this is a real recruitment and retention risk.

DevOps Velocity Demands Are Reaching the Mainframe

Software delivery expectations have changed fundamentally across the industry. Data cited from 451 Research indicated that 24% of companies release application software daily or hourly. DORA’s State of DevOps report placed 20% of organizations deploying multiple times per day. Continuous deployment is no longer aspirational — it is the baseline expectation.

For the mainframe to remain a viable platform for application development — rather than just a runtime for legacy batch jobs — its development workflows need to match these cadences. That requires modern CI/CD-compatible tooling, scripting via CLIs, and team collaboration capabilities that distributed development teams already take for granted.

Eclipse Che and Che4z: A Browser-Based IDE for Mainframe Developers

Eclipse Che is an open-source, Java-based developer workspace server and browser-based IDE that is container-native. It was architected to deliver a VS Code-like experience from the browser, built on Eclipse Theia. Theia implements the same tooling protocols that underpin VS Code — the Language Server Protocol (LSP), Debug Adapter Protocol, and compatibility with VS Code extensions — making the transition familiar for developers already working in that ecosystem.

The analogy often used is Git: its usability and collaborative design made it the de facto standard for version control across teams of all sizes. Che’s workspace server aims to deliver comparable collaborative benefits, particularly for hybrid IT teams working across mainframe and distributed platforms.

The Che4z subproject, contributed by Broadcom, extends Che specifically for mainframe development. It provides:

• Access to the z/OS file system
• Remote editing and debugging of COBOL programs
• Syntax highlighting, code assist, and real-time syntax validation
• Language Server Protocol support for COBOL (with High-level Assembler planned)

Because Che4z uses LSP, developers can use COBOL with Che, VS Code, or any other LSP-compatible editor such as IntelliJ, VIM, or Emacs — significantly broadening the reach of mainframe language support without requiring a proprietary plugin ecosystem.

Zowe: The Open Source Foundation That Makes It Possible

The Che4z extensions are powered by Zowe, the first open source project based on z/OS. Zowe was founded by Broadcom, IBM, and Rocket Software and enables developers to manage, control, script, and develop on the mainframe using familiar patterns from cloud development — all while maintaining mainframe-native security standards.

While open source is not new to mainframe environments — zLinux has existed for years — Zowe was a significant step in opening access to off-platform toolchains. The Zowe CLI is a key component of the Che4z stack, enabling the kind of scripting and automation that underpins DevOps adoption. CLIs allow developers to automate repetitive tasks, integrate with pipelines, and script complex workflows — capabilities that were difficult or impossible with traditional mainframe tooling.

Code4z: Bringing Mainframe Development Into VS Code

For developers who prefer to stay in their desktop environment rather than a browser-based IDE, Code4z is a free Visual Studio Code plugin that provides access to mainframe datasets and elements directly from VS Code. It allows developers to write and manage mainframe code using the most popular desktop IDE available today, without needing to context-switch into a separate toolchain.

Why Containerized Workspaces Matter for Mainframe Teams

Because Che workspaces are containerized, they carry meaningful operational advantages over traditional desktop IDE setups:

• No local installation: Developers open a container and begin work immediately, with no environment configuration on their local machine.
• Fast onboarding: New team members can be productive in minutes rather than days — a critical advantage when mainframe talent is scarce.
• Reduced maintenance overhead: The heavyweight plugin management required by Eclipse-based setups is eliminated.
• Pair programming support: Che facilitates real-time collaboration between mainframe and distributed developers working on cross-platform applications.

For mainframe leaders under pressure to reduce costs and onboard the next generation quickly, migrating teams from Eclipse-with-customizations to a containerized Che environment represents a concrete opportunity to reduce operating costs and improve developer productivity.

Bridging Mainframe and Cloud: The Hybrid IT Case

For many large enterprises, the mainframe hosts critical systems of record — transaction engines and authoritative data stores — while cloud and mobile applications serve as systems of experience. Cross-platform applications increasingly need to combine the transactional power and data assets of the mainframe with modern user interfaces built on cloud and mobile stacks.

This integration work falls on cloud and mobile developers who are unfamiliar with mainframe tooling. Allowing them to use the editors they already know — VS Code, Che, LSP-compatible IDEs — removes a significant barrier and accelerates delivery. The open source, standards-based approach taken by Che and Zowe is central to making that practical.

Commercial Support for Open Source Mainframe Tools

Enterprises that depend on mainframe infrastructure are understandably cautious about adopting open source tools without a clear support model. Vendors including Broadcom address this by offering commercially supported versions of these open technologies alongside the open source releases.

Broadcom’s CA Brightside product, for example, provides a streamlined installation path, technical and legal support for Zowe core capabilities, and certified integrations with proprietary solutions such as CA Endevor SCM, CA File Master Plus, and CA OPS/MVS. This model allows enterprises to adopt open source tooling at their own pace while maintaining enterprise-grade support coverage.

The Broader Revolution in Mainframe Application Development

The convergence of Che, Che4z, Zowe, and Code4z represents more than a tooling refresh. It signals a fundamental shift in how mainframe development is positioned — away from a siloed, vendor-controlled ecosystem and toward participation in the same open source communities, protocols, and workflows that drive development on every other platform.

Expanded tool choice, greater alignment with off-platform development peers, and DevOps-compatible software delivery cadences are the practical outcomes. For organizations managing hybrid IT estates — where mainframe reliability and cloud agility both matter — this open source foundation is what makes sustainable modernization possible.

The Che4z extensions are open sourced and free to use. Community contributions are welcomed by both the Che and Che4z projects.

What Is Application Release Orchestration?

ARO platforms go beyond simple deployment scripts. They model a release as a first-class object — with approvals, environment gates, rollback strategies, audit trails, and cross-team coordination built in. Typical capabilities include:

• Pipeline orchestration — sequencing build, test, and deploy stages across heterogeneous toolchains.
• Environment modelling — tracking which version of which component is running where.
• Approval gates and compliance controls — enforcing change-management policies before production promotion.
• Rollback and remediation — reverting a release or triggering runbooks when health checks fail.
• Audit and reporting — producing release records for regulatory or internal review.

Leading Vendors in the ARO Space

The ARO market includes established enterprise vendors, open-source-led platforms, and cloud-native newcomers. The following companies are widely referenced in this space:

• XebiaLabs (now Digital.ai) — enterprise release orchestration and DevOps analytics.
• Electric Cloud (now CloudBees) — pipeline automation with a strong heritage in large-scale release management.
• GitLab — integrated CI/CD with release management built into the same platform as source control.
• IBM — enterprise-grade release tooling, often integrated with existing IBM middleware estates.
• Red Hat — OpenShift Pipelines and Ansible-based automation for hybrid cloud deployments.
• Octopus Deploy — deployment automation with strong support for .NET, multi-tenant scenarios, and runbook automation.
• CA Technologies (now Broadcom) — release automation within a broad IT management portfolio.
• Microsoft — Azure DevOps Pipelines and GitHub Actions for end-to-end release workflows.
• Puppet — infrastructure-as-code and continuous delivery automation, particularly on Puppet Enterprise.
• Micro Focus — release control integrated with ITSM and ALM tooling.
• VMware (Tanzu) — application deployment on Kubernetes and multi-cloud environments.
• ARCAD Software — specialised release orchestration for IBM i and mainframe environments.
• Inedo (ProGet, BuildMaster) — package management and deployment automation for Windows-centric shops.
• Clarive Software — release and DevOps pipeline automation with a strong ITSM integration story.

Vendor positioning shifts over time through acquisitions and product pivots (for example, Electric Cloud becoming part of CloudBees, XebiaLabs rebranding under Digital.ai). Always verify current product names and ownership before evaluating a platform.

Deployment Models

ARO tools are generally available in two deployment models, each with distinct trade-offs:

On-Premises

Deployed inside the organisation’s own data centre or private cloud. Preferred by organisations with strict data-residency requirements, air-gapped environments, or existing infrastructure investments. On-premises deployments give full control over upgrade timing, networking, and integration with internal systems — but place the operational burden on the internal team.

Cloud-Based (SaaS)

Hosted and managed by the vendor. Reduces infrastructure overhead, accelerates time-to-value, and keeps teams on current versions automatically. The trade-off is dependency on internet connectivity and the vendor’s security and compliance posture. Most modern ARO vendors now offer a SaaS-first or SaaS-only option.

Primary Adoption Segments

Small and Medium-Sized Enterprises (SMEs)

SMEs typically start with lightweight, SaaS-delivered ARO tools that integrate tightly with existing CI platforms (GitHub Actions, GitLab CI, CircleCI). The priority is reducing manual handoffs between developers and operations without requiring a dedicated release engineering team. Open-source options and lower-tier SaaS plans make ARO accessible even with constrained budgets.

Large Enterprises

Larger organisations face additional complexity: multiple teams, regulated industries, hybrid infrastructure, mainframe or legacy systems, and mandatory change-approval processes. Enterprise ARO platforms address these needs with role-based access control, ITSM integrations (ServiceNow, Jira Service Management), detailed audit logs, and the ability to orchestrate releases across dozens of environments simultaneously.

Key Market Drivers

Several forces are pushing organisations toward dedicated ARO tooling:

• Accelerating release cadences — competitive pressure to ship features faster while maintaining quality.
• Microservices and distributed architectures — deploying tens or hundreds of interdependent services manually is error-prone and slow.
• Regulatory compliance — industries such as finance, healthcare, and government require demonstrable controls over what gets deployed and when.
• Cloud-native adoption — Kubernetes and container platforms introduce new deployment primitives that benefit from orchestration tooling.
• DevSecOps integration — embedding security scanning and policy enforcement within the release pipeline rather than as a post-deployment afterthought.

Key Market Restraints and Challenges

• Tool sprawl and integration complexity — organisations already running multiple CI/CD tools may struggle to add another orchestration layer cleanly.
• Cultural resistance — teams accustomed to manual release processes may resist automated gates and approvals.
• Licensing costs — enterprise ARO platforms can carry significant per-user or per-pipeline pricing, making ROI justification necessary.
• Skill gaps — configuring and maintaining ARO pipelines requires expertise that many teams are still building.

Evaluating an ARO Platform: Practical Checklist

When assessing ARO tools for your organisation, consider the following dimensions:

1. Integration breadth — does it connect natively to your existing source control, CI, ticketing, and monitoring tools?
2. Environment modelling — can it accurately represent your actual infrastructure topology?
3. Approval workflow flexibility — can gates be configured to match your change-management process without becoming a bottleneck?
4. Rollback capability — how automated and reliable is rollback, and does it extend to database migrations?
5. Audit trail quality — will the records satisfy an internal or external audit?
6. Scalability — can it handle your peak release volume without degrading?
7. Vendor stability — given the consolidation in this market, assess vendor roadmap and financial position.

Summary

Application Release Orchestration software addresses a real operational pain point: coordinating complex, multi-environment deployments reliably and at speed. The vendor landscape spans purpose-built ARO platforms, CI/CD suites with built-in release management, and enterprise IT toolchains with release control modules. Choosing the right fit depends on your organisation’s size, regulatory requirements, existing toolchain, and target deployment model. Evaluating tools against concrete operational criteria — rather than feature marketing — will produce better long-term outcomes than buying on analyst rankings alone.

Why IT Infrastructure Monitoring Matters

Modern IT environments span on-premises data centers, hybrid clouds, and fully managed SaaS platforms. Without continuous monitoring, teams lack the observability needed to detect outages, capacity bottlenecks, security anomalies, and performance degradation before they affect end users. Infrastructure monitoring tools address this need by collecting metrics, logs, and events from every layer of the stack and surfacing actionable alerts.

Key Players in the Market

Research covering the global IT infrastructure monitoring tool market profiles a broad set of vendors, ranging from large software companies to open-source projects and specialized SaaS providers. Frequently cited participants include:

• SolarWinds – Known for its comprehensive on-premises and SaaS monitoring portfolio, including network performance management and application monitoring.
• ManageEngine – Offers an integrated suite covering network, server, application, and cloud monitoring, commonly adopted by mid-market IT teams.
• Zabbix – A widely used open-source monitoring platform with strong community support and enterprise add-ons.
• Paessler (PRTG) – Provides agentless network and infrastructure monitoring with a sensor-based licensing model.
• Datadog – A cloud-native observability platform covering infrastructure, APM, logs, and security in a unified SaaS interface.
• Nagios – One of the original open-source monitoring frameworks, still widely deployed for host and service checks.
• VMware – Delivers infrastructure monitoring tightly integrated with its virtualization and cloud management stack.
• PagerDuty – Focuses on real-time alerting, on-call scheduling, and incident response automation.
• Catchpoint – Specializes in digital experience monitoring and synthetic testing from distributed vantage points.
• LogicMonitor – A SaaS-based monitoring platform with automated device discovery and a large library of pre-built integrations.
• ScienceLogic – Targets large enterprises and service providers with AI-assisted topology mapping and event correlation.
• Kaseya – Provides remote monitoring and management tools oriented toward managed service providers (MSPs).
• Micro Focus – Offers enterprise-grade operations bridge and AIOps capabilities.
• NetApp – Includes storage and cloud infrastructure monitoring as part of its data management portfolio.
• TeamViewer, Xmatters, Ipswitch, Virtual Instruments – Additional vendors covering remote access, notification workflows, network management, and infrastructure analytics respectively.

Market Segmentation

The IT infrastructure monitoring tool market is typically segmented along two primary dimensions:

By Deployment and Delivery Model

• On-premises software – Installed and managed within the customer’s own data center. Preferred by organizations with strict data sovereignty or compliance requirements.
• SaaS / cloud-hosted – Subscription-based platforms managed by the vendor. Faster to deploy and easier to scale, driving strong adoption growth.
• Open-source – Community-maintained tools such as Zabbix, Nagios, and Prometheus, often chosen to minimize licensing costs while accepting higher operational overhead.

By End-User Industry

• Banking, Financial Services, and Insurance (BFSI)
• Healthcare and Life Sciences
• Retail and E-commerce
• Telecommunications
• Government and Public Sector
• Manufacturing and Logistics
• IT and Managed Service Providers

Geographic Coverage

Market research typically breaks the global IT infrastructure monitoring tool market into the following regions:

• North America – United States, Canada, Mexico. The largest revenue region, driven by high cloud adoption rates and a mature enterprise IT base.
• Europe – UK, Germany, France, Italy, Spain, Russia. Strong growth linked to digital transformation initiatives and GDPR compliance requirements driving monitoring investment.
• Asia-Pacific – China, India, Japan, South Korea, Indonesia, Singapore, Australia. The fastest-growing region as cloud infrastructure build-out accelerates.
• South & Central America – Brazil, Argentina, Chile. Emerging adoption with expanding internet infrastructure.
• Middle East & Africa – Saudi Arabia, UAE, Turkey, Egypt, South Africa. Growing investment in smart city and government digitalization projects.

Competitive Dynamics and Market Trends

Competition in this market is intensifying as vendors pursue several strategic directions simultaneously:

• Platform consolidation – Vendors are expanding from point solutions into unified observability platforms that combine infrastructure metrics, application performance monitoring (APM), log management, and real user monitoring (RUM).
• AIOps integration – Machine learning is being embedded to reduce alert noise, perform anomaly detection, and automate root-cause analysis.
• Cloud-native support – Native integrations with AWS, Azure, and GCP services, as well as Kubernetes and container monitoring, have become table-stakes capabilities.
• Mergers and acquisitions – Established players are acquiring specialized vendors to fill capability gaps quickly rather than building features organically.
• Open-source and open standards – The rise of the OpenTelemetry standard is influencing both commercial and open-source tools, promoting vendor-neutral instrumentation.

Choosing the Right Monitoring Tool

When evaluating IT infrastructure monitoring tools, engineering teams should consider the following criteria:

1. Coverage scope – Does the tool monitor all relevant layers: network devices, servers, containers, serverless functions, and cloud-managed services?
2. Scalability – Can it handle your current environment size and projected growth without significant re-architecture?
3. Integration ecosystem – Does it connect to your existing ticketing, CI/CD, logging, and communication tools?
4. Alert quality – Does the platform support intelligent alert grouping and suppression to prevent on-call fatigue?
5. Total cost of ownership – Factor in licensing, infrastructure, and operational overhead, especially for self-hosted open-source deployments.
6. Compliance and data residency – If regulatory requirements apply, confirm where monitoring data is stored and retained.

Summary

The IT infrastructure monitoring tool market is large, diverse, and growing. It spans open-source frameworks, on-premises enterprise suites, and modern SaaS observability platforms. Key players such as SolarWinds, Datadog, ManageEngine, Zabbix, and Nagios each address different segments of the market, from SMBs and MSPs to global enterprises running hybrid and multi-cloud environments. DevOps and platform engineering teams evaluating tools in this space should assess not just current feature parity, but long-term vendor roadmap alignment with cloud-native and AIOps trends.

Yet despite the momentum, AI projects carry a troubling failure rate. A 2019 study by Pactera reported that 85% of AI projects fail to deliver on their intended promises—a figure that stands in stark contrast to traditional enterprise software success rates. Closing that gap requires more than budget and tooling. It requires a fundamentally different approach to how organizations plan, execute, and iterate on AI initiatives.

The trajectory is clear: what used to be said about software—”software is eating the world”—increasingly applies to AI itself. As AI capabilities become embedded deeper into cloud platforms and enterprise software, the boundary between the two will blur. Organizations that build strong AI fundamentals now will be far better positioned as that convergence accelerates.

Three factors consistently separate successful AI adoption from expensive disappointment.

1. Increase Organizational AI Literacy

The single biggest obstacle to successful AI adoption is not technology—it is people. Human factors, including unrealistic expectations, fear of the unknown, and a lack of foundational knowledge, undermine AI projects before the first model is trained.

Organizations must invest in structured AI education at every level, from executive leadership to line-of-business users. This does not mean every employee needs to understand backpropagation. It does mean that stakeholders should be able to distinguish between the core disciplines of AI—natural language processing, computer vision, supervised and unsupervised machine learning—and understand, at a high level, what each can and cannot do.

Treating AI as a black box where magic happens is a recipe for misaligned expectations and abandoned projects. When business users understand the difficulty and scope of AI work, they are better equipped to set realistic success criteria, allocate appropriate resources, and sustain commitment through the inevitable early setbacks. Short-term education investment pays dividends in long-term adoption success.

2. Commit to Continuous Iteration, Not a One-Time Deployment

Many enterprise software projects follow a familiar pattern: requirements gathering, design, build, test, deploy, monitor. AI projects do not fit that mold. Applying a traditional “measure twice, cut once” mentality to AI is one of the most common reasons projects stall or fail after initial deployment.

AI is a continuous journey. Predictive models degrade as the real-world conditions they were trained on evolve. Data distributions shift. Business contexts change. A model that performs well at launch may deliver meaningfully worse results six months later without ongoing attention.

Successful AI programs move beyond proof-of-concept phases and establish pipelines for continuous delivery of intelligent solutions. This means building MLOps practices—monitoring model performance in production, retraining on fresh data, and establishing feedback loops between model outputs and business outcomes. Business leaders need to treat AI as an operating capability requiring ongoing investment, not a project with a defined end date.

3. Ensure Every Insight Is Actionable

AI that generates insights without translating them into clear, contextual actions for the people who can act on them delivers little business value. This is the convergence point between AI and analytics—and it is increasingly where the real competitive advantage is won or lost.

For roughly two decades, analytics platforms delivered reports and dashboards that required business users to interpret data themselves and determine what to do next. AI changes that model fundamentally. Machine learning systems can now function as prescriptive engines—not just surfacing trends, but recommending or even automating the next best action within the user’s existing workflow.

The critical design question for any AI initiative is: who can act on this insight, and does the system deliver it to them in a way they can use immediately? An insight buried in a data science dashboard that a frontline user never sees is not a success. Success is measured by whether the right person receives an actionable recommendation within the context of their daily work—and whether that recommendation improves the outcome.

A New Skill Set for an AI-Driven World

For IT and software professionals, AI demands an expanded skill set. Data is the driving force of AI projects, not a side effect or a necessary evil. That shift elevates disciplines like statistics, probability, and applied mathematics alongside traditional software engineering skills.

Teams that bridge software engineering, data engineering, and quantitative analysis are better equipped to build AI systems that actually work in production—and to iterate on them effectively over time. The organizations investing in that cross-functional capability today are laying the groundwork for dramatically higher AI success rates as the technology matures and embeds itself more deeply into every layer of the enterprise stack.

Originally sourced from devops.com.

What Is Data Virtualization?

Data virtualization software sits between data consumers (BI tools, applications, APIs) and data sources (databases, data lakes, SaaS platforms, cloud storage). It provides a real-time or near-real-time federated query capability, translating a single logical request into optimized queries against multiple physical systems and returning a unified result set. Key benefits include:

• Reduced data duplication: Data stays at the source; only query results are moved.
• Faster time-to-insight: New data sources can be exposed to consumers in hours rather than weeks.
• Simplified governance: Security policies and masking rules are applied at the virtualization layer, covering all consumers consistently.
• Hybrid and multi-cloud readiness: A single logical model can span on-premises databases, private cloud, and multiple public cloud providers.

Major Vendors in the Data Virtualization Software Market

The market includes both dedicated data virtualization platforms and broader data integration suites that include virtualization as a feature. Prominent vendors frequently cited in analyst coverage include:

• SAP – Offers data federation and virtualization capabilities within the SAP Data Intelligence and SAP HANA portfolios.
• Informatica – Provides data virtualization as part of its Intelligent Data Management Cloud (IDMC) platform.
• Oracle – Integrates virtualization through Oracle Data Integrator and its Autonomous Database services.
• AWS – Amazon Athena and AWS Glue together support federated query patterns across S3, RDS, and other sources.
• Denodo – A dedicated data virtualization platform with strong multi-source federation, caching, and data catalog features.
• VMware – Relevant through its data management and infrastructure virtualization stack.
• OpenLink Virtuoso – An open-source and commercial platform focused on RDF, SPARQL, and relational federation.
• AtScale – Specializes in semantic layer and OLAP-style virtualization over cloud data warehouses.
• Data Virtuality – A logical data warehouse platform combining virtualization with optional materialization.
• IBM – Data virtualization is available through IBM Cloud Pak for Data and IBM Db2 Big SQL.
• Red Hat – Offers JBoss Data Virtualization (now part of the Red Hat Integration portfolio).
• TIBCO – Provides data virtualization through its TIBCO Data Virtualization (formerly Cisco Information Server / Composite Software) product.
• Actifio – Focused on copy data management and virtual data pipelines, particularly for DevOps and backup use cases.
• Stone Bond – Offers the Enterprise Enabler integration and virtualization platform targeting complex legacy environments.

Deployment Models

Data virtualization software is typically available in two primary deployment models:

Cloud-Based

Cloud-native or cloud-hosted deployments are growing rapidly as organizations shift workloads to AWS, Azure, and Google Cloud. Cloud-based data virtualization platforms offer elastic scaling, reduced infrastructure management overhead, and native integrations with cloud-native data stores and SaaS applications. Many vendors now offer SaaS-delivered virtualization that requires no on-premises footprint.

Web-Based (On-Premises / Self-Managed)

Traditional on-premises deployments, managed through web-based administration consoles, remain common in regulated industries such as financial services, healthcare, and government, where data sovereignty or security policies restrict public cloud usage. Many enterprises run hybrid configurations, with the virtualization layer connecting both on-premises and cloud sources simultaneously.

Primary Use Cases and Application Segments

Large Enterprises

Large organizations with complex, heterogeneous data estates are the primary adopters of data virtualization. Common use cases include enterprise-wide self-service analytics, regulatory reporting, master data management, and logical data warehouse architectures that complement or partially replace physical warehousing. The ability to federate dozens of source systems without replication is particularly valuable at enterprise scale.

Small and Medium Enterprises (SMEs)

Cloud-based and SaaS delivery models have lowered the entry barrier for SMEs. Smaller organizations benefit from data virtualization when they need to surface data from multiple SaaS tools (CRM, ERP, marketing platforms) for unified reporting without the cost or expertise required to build and maintain a full data warehouse and ETL infrastructure.

Regional Market Considerations

Adoption patterns for data virtualization software vary significantly by region, driven by cloud maturity, regulatory requirements, and existing data infrastructure investments:

• North America: The most mature market, with strong enterprise adoption driven by cloud-first strategies and a competitive analytics ecosystem.
• Europe: GDPR and other data privacy regulations create demand for virtualization as a privacy-by-design pattern, since data can be accessed in place rather than copied across borders.
• Asia Pacific: Rapid digital transformation in markets such as China, India, and Southeast Asia is accelerating adoption, particularly cloud-based deployments.
• Latin America: Growing interest, primarily among large financial institutions and telcos, though cloud infrastructure maturity varies by country.
• Middle East and Africa: Emerging adoption, with government-led digital transformation initiatives and major enterprise projects driving early demand.

Key Technology and Competitive Trends

Several trends are reshaping the data virtualization software landscape:

• Semantic and knowledge graph integration: Vendors are adding ontology and knowledge graph layers on top of virtualized data to improve discoverability and context.
• AI-assisted query optimization: Machine learning is being applied to optimize federated query plans and recommend caching strategies automatically.
• Data mesh alignment: Virtualization platforms are evolving to support domain-oriented data product architectures, acting as the infrastructure layer beneath a data mesh.
• Built-in data catalogs: Leading platforms now integrate metadata management and data lineage tracking directly into the virtualization layer rather than treating it as a separate tool.
• Consolidation through M&A: Larger integration platform vendors continue to acquire specialized data virtualization providers to expand their portfolio coverage.

Evaluating a Data Virtualization Platform

When assessing data virtualization solutions for your organization, consider the following criteria:

1. Source connector breadth: Does the platform natively support your existing databases, cloud warehouses, SaaS APIs, and file stores?
2. Query performance and caching: What optimization strategies does it offer, and can frequently accessed result sets be selectively cached?
3. Security and governance: Does it support column-level and row-level security, dynamic data masking, and integration with your identity provider?
4. Deployment flexibility: Can it run on-premises, in your preferred cloud, or as a managed SaaS service?
5. Lineage and cataloging: Does it track where data originates and how it is transformed, making it auditable for compliance purposes?
6. Total cost of ownership: Consider licensing, infrastructure, and the operational cost of maintaining the platform alongside your existing data stack.

Data virtualization is not a replacement for all data warehousing or integration patterns, but it is a powerful complement—particularly when data access speed, governance, and heterogeneous source coverage are priorities. As cloud adoption and multi-source data estates continue to grow, the strategic importance of a well-chosen data virtualization platform is only likely to increase.

This article breaks down five technology trends that are expected to drive enterprise investment decisions in the near and medium term, and explains what each means in practice for engineering and DevOps teams.

1. Digital Transformation: Moving the Entire Business Online

Digital transformation remains the overarching strategic priority for most enterprises. The core idea is straightforward: migrate processes, customer touchpoints, and decision-making from analogue or legacy systems to digital platforms that are faster, more data-driven, and easier to scale.

Industry surveys have consistently found that a large majority of senior executives view digital transformation as essential for survival, yet an equally significant portion acknowledge they are behind where they need to be. The gap between intention and execution is the defining challenge. Companies that close it gain measurable advantages in speed-to-market, customer experience, and operational cost. Those that do not risk becoming structurally uncompetitive.

For engineering organisations, digital transformation translates into concrete decisions: modernising monolithic applications, adopting cloud-native architectures, implementing continuous integration and continuous delivery (CI/CD) pipelines, and retiring technical debt systematically rather than deferring it indefinitely.

2. IoT: Managing Thousands of New Attack Surfaces and Data Sources

The Internet of Things extends computing and connectivity to physical devices — sensors on factory floors, smart meters in buildings, medical monitoring equipment, and logistics trackers. The volume of data these devices generate creates powerful opportunities for real-time operational intelligence.

However, enterprise adoption at scale still faces three concrete barriers:

• Cost: Deploying, managing, and maintaining large device fleets requires significant capital and operational expenditure, particularly when retrofitting existing infrastructure.
• Security: Each connected device is a potential entry point. Many IoT devices ship with weak default credentials, infrequent firmware updates, and limited compute resources for on-device security controls. This dramatically expands the attack surface organisations must monitor.
• Integration complexity: Connecting device data to existing enterprise systems — ERP, CRM, analytics platforms — requires robust data pipelines and governance frameworks that many organisations are still building.

For DevOps teams, IoT adoption means owning firmware CI/CD pipelines, device fleet management, and integration with cloud IoT platforms. Security must be designed in from the start, not bolted on after deployment.

3. AI and Machine Learning: Moving from Hype to Operational Reality

Artificial intelligence and machine learning have passed the peak of inflated expectations and are now delivering measurable value in specific, well-scoped use cases. The most common enterprise applications include:

• Business intelligence and forecasting: ML models trained on historical data can surface demand patterns, churn signals, and anomalies that rule-based systems miss.
• Process automation: Repetitive, rules-adjacent tasks — document classification, ticket routing, invoice processing — are strong candidates for ML-driven automation.
• AI-assisted decision making: Organisations are embedding model outputs into operational workflows so that human decision-makers have data-driven recommendations at the point of action.
• Digital identity verification: Computer vision and natural language processing are increasingly used to verify identities for online financial services and onboarding workflows.

Deep learning, the subset of ML that powers most modern AI breakthroughs, requires significant compute and large labelled datasets. This makes it better suited to organisations with mature data infrastructure. For teams earlier in the ML journey, classical ML methods often deliver better ROI faster.

From a DevOps perspective, AI/ML introduces a distinct operational discipline — MLOps — covering model versioning, data pipeline reliability, model monitoring in production, and retraining cadences. These are not software engineering problems that existing toolchains solve out of the box.

4. Quantum Computing: A Long-Term Research Bet

Quantum computing leverages the principles of quantum mechanics — superposition and entanglement — to perform certain categories of computation exponentially faster than classical hardware. Problems that involve searching vast solution spaces, simulating molecular interactions, or optimising complex logistics networks are candidates for quantum speedup.

However, quantum computing remains predominantly in the research and development phase. Current quantum hardware is noisy, error-prone, and requires extreme environmental conditions to operate. Practical, fault-tolerant quantum computers capable of running production workloads do not yet exist at commercial scale.

Major technology companies and national research agencies are investing heavily in quantum R&D, and cloud providers have begun offering quantum computing access as a service, allowing enterprises to experiment without building dedicated hardware. For most organisations, the near-term opportunity is in developing quantum literacy — understanding the problem classes where quantum advantage is theoretically achievable — rather than deploying production quantum workloads.

One area where quantum computing has immediate strategic relevance is cryptography. Sufficiently powerful quantum computers could break widely used public-key encryption schemes. Security teams should be tracking post-quantum cryptography standards development, notably the work underway at NIST, and planning migration timelines accordingly.

5. DevOps: Evolving Toward Customer-Centric Delivery

DevOps adoption has matured significantly, but many organisations are hitting a ceiling. CI/CD pipelines are in place, but release velocity is not translating directly into improved customer outcomes. The next evolution of DevOps practice is shifting focus from speed for its own sake to outcomes — specifically, customer value delivered per release cycle.

Key areas of development in DevOps practice include:

• Platform engineering: Building internal developer platforms (IDPs) that abstract infrastructure complexity and let product engineers focus on feature delivery rather than operational toil.
• Continuous testing evolution: Moving beyond functional regression testing toward contract testing, chaos engineering, and continuous performance validation in production-like environments.
• Developer experience: Reducing cognitive load and friction in development workflows to maintain sustainable pace without burning out engineering teams.
• Security integration (DevSecOps): Embedding security scanning, policy enforcement, and compliance checks directly into pipelines rather than treating them as release gates.

Teams experiencing stress from deployment pressure should treat that signal seriously. Sustainable delivery velocity requires good observability, a culture of blameless incident review, and a genuine commitment to reducing toil through automation — not simply demanding more from engineers.

Where to Focus Investment

For most enterprises, the practical investment priority follows a logical sequence. Digital transformation and DevOps maturity are foundational — without them, investments in AI and IoT will underdeliver because the organisational and technical infrastructure to operationalise those technologies is not in place. AI and IoT deliver compounding returns on top of a solid digital and delivery foundation. Quantum computing is a longer-horizon bet best approached through education and selective experimentation today.

Across all five areas, cybersecurity is a cross-cutting concern rather than a separate line item. Every new technology surface — IoT devices, ML model APIs, quantum-vulnerable encryption — requires a security posture that scales with adoption.

Original analysis sourced from reporting by TechRepublic.