What Is Azure Sentinel?

Azure Sentinel is Microsoft’s cloud-native Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platform. Unlike traditional on-premises SIEM products, Azure Sentinel requires no infrastructure provisioning or complex installation — it runs entirely in the cloud and scales automatically to meet data volume demands.

What sets Azure Sentinel apart from conventional SIEM tools is its breadth of data ingestion and its AI-powered analytics engine. It is designed to monitor not only Azure-native resources, but also on-premises workloads, Office 365, AWS, and a wide range of third-party security products. This makes it a genuine hybrid-cloud security solution rather than a siloed, single-vendor tool.

The Four Core Stages of Azure Sentinel

Azure Sentinel organises its capabilities around four sequential stages, each addressing a distinct phase of the threat-management lifecycle:

• Collect — Ingests data at cloud scale from users, devices, applications, and infrastructure across multiple clouds and on-premises environments.
• Detect — Identifies threats in near-real time using built-in analytics rules, reducing mean time to detect (MTTD).
• Investigate — Applies machine learning and AI to surface correlated alerts and map attack timelines across all monitored assets.
• Respond — Automates responses to confirmed threats using Logic App-based Playbooks, eliminating slow manual remediation steps.

Supported Data Sources and Connectors

Once a Log Analytics workspace is connected to Azure Sentinel, you configure Data Connectors to forward logs and telemetry into the platform. At the time of the original preview, available connectors included:

• Azure Active Directory (sign-in logs, audit logs)
• Azure Active Directory Identity Protection (requires Azure AD Premium P1/P2)
• Office 365 (Exchange, SharePoint, Teams activity)
• Microsoft Cloud App Security
• Azure Information Protection
• AWS CloudTrail
• Third-party security appliances: Cisco ASA, Fortinet, Barracuda, F5
• Any syslog source supporting Common Event Format (CEF)

Each connector has its own configuration blade with step-by-step instructions. Some connectors, such as Azure AD Identity Protection, connect with a single click (provided licensing prerequisites are met), while others — particularly CEF-based connectors — require agent installation on a log-forwarding server.

Getting Started: Initial Setup

Setting up Azure Sentinel for the first time involves a small number of steps:

1. Navigate to Azure Sentinel in the Azure Portal — either via the Marketplace, All Services, or the search bar.
2. Click + Add and select an existing Log Analytics workspace, or create a new one in a supported region.
3. Click Add Azure Sentinel to attach the workspace.
4. Open the Data Connectors blade and enable the relevant connectors for your environment.

After connectors are configured, Azure Sentinel begins ingesting data. It can take several minutes before events and alerts start appearing in the dashboards. The longer the platform runs, the richer the historical context it builds for anomaly detection.

Note on regions (as of early 2019 preview): At the time of writing, Azure Sentinel workspaces were supported only in a limited set of regions: Australia Southeast, Canada Central, Central India, East US, East US 2 EUAP, Japan East, Southeast Asia, UK South, West Europe, and West US 2. This list has expanded significantly since general availability. Always check the official Microsoft documentation for the current list of supported regions.

The Azure Sentinel Dashboard

The main Azure Sentinel overview presents a centralised security posture at a glance:

• Events and Alerts counters — rolling totals for ingested events and triggered alerts.
• Events and Alerts over time graph — a time-series view useful for spotting sudden spikes in activity.
• World map — plots the geographic origin of suspicious events, helping analysts prioritise threats by source location.
• Threat Management and Configuration panel — left-hand navigation providing access to Analytics, Playbooks, Hunting, Cases, and Settings.

Writing Custom Alert Rules with Kusto Query Language (KQL)

Azure Sentinel surfaces Log Analytics directly through the Logs blade, allowing analysts to write and test queries using Kusto Query Language (KQL) against the connected Log Analytics workspace. This is particularly powerful for building custom detection logic tailored to your environment.

As a practical example, the following KQL query searches for Windows Security Event ID 4625 — a failed logon attempt — which is a common indicator of brute-force activity:

SecurityEvent
| where EventID == 4625
| summarize FailedLogons = count() by Account, Computer, bin(TimeGenerated, 1h)
| where FailedLogons > 10
| order by FailedLogons desc

Once you have a working query, you can promote it into a scheduled Analytics Rule by:

1. Navigating to Analytics and clicking + Add.
2. Entering a rule name, description, severity level, and status (enabled/disabled).
3. Pasting your KQL query into the Set Alert Query field — the Logic pane renders a preview graph of matching results.
4. Clicking View Query Results to validate the query directly in Log Analytics before saving.
5. Setting an alert threshold and a run frequency (e.g., every 5 minutes, every hour).
6. Clicking Save to activate the rule.

When the query matches results exceeding the defined threshold, Azure Sentinel raises an alert and, optionally, creates a Case for analyst triage.

Automating Responses with Playbooks

Azure Sentinel Playbooks are Azure Logic App workflows triggered by alerts. They enable security teams to automate repetitive response tasks, dramatically reducing mean time to respond (MTTR). Example automated actions include:

• Sending an email or Teams notification when a brute-force threshold is breached.
• Blocking inbound traffic from a known-malicious IP address via a firewall API call.
• Disabling or locking a compromised user account in Azure Active Directory.
• Isolating a virtual machine by removing it from a network security group.
• Shutting down a compromised VM to contain lateral movement.
• Opening a ticket in an ITSM platform such as ServiceNow or Jira.

Because Playbooks are standard Logic Apps, they benefit from the full Logic Apps connector ecosystem — hundreds of pre-built integrations with Microsoft and third-party services.

Azure Sentinel Fusion: AI-Powered Threat Correlation

Azure Sentinel Fusion is an advanced, machine-learning-driven correlation engine that links seemingly unrelated low-fidelity alerts across multiple data sources into high-confidence security incidents. It is designed to reduce alert fatigue by surfacing the small number of incidents that truly warrant analyst attention, rather than flooding dashboards with individual, unconnected alerts.

During the preview period, Fusion was not enabled by default and had to be activated via Azure Cloud Shell. As of general availability, Fusion is integrated into the platform’s analytics rules and enabled automatically for supported detection scenarios. Check the current Microsoft documentation for the latest configuration requirements.

Key Advantages Over Traditional On-Premises SIEM

Prerequisites Summary

• An active Azure subscription with Contributor permissions.
• A Log Analytics workspace created in a supported Azure region.
• Appropriate licensing for premium connectors (e.g., Azure AD Premium P1/P2 for Identity Protection).

Further Reading

For the most current feature set, pricing, and regional availability, refer to the official Microsoft Azure Sentinel (now Microsoft Sentinel) documentation. Note that Microsoft rebranded the product as Microsoft Sentinel in November 2021.

Python Hits a Record High on the Tiobe Index

Python reached its highest-ever rating in the June 2019 edition of the Tiobe Index, the monthly ranking of programming language popularity. The language earned a rating of 8.53 percent, edging past its previous record of 8.376 percent set in December 2018. Python held third place overall, behind Java and C.

Tiobe attributed Python’s surge to its relative ease of use compared to Java and C, noting that the language continues to attract a large number of newcomers. At its current growth trajectory, Tiobe projected that Python could surpass both Java and C to become the index’s most popular language within three to four years.

How the Tiobe Index Works

The Tiobe Index calculates ratings using a formula that aggregates search query data from major search engines including Google, Yahoo, and Bing. Ratings reflect the volume of search activity related to a language’s courses, skilled engineers, and third-party vendors — not lines of code written or GitHub repositories.

A separate index, the Pypl (Popularity of Programming Language) Index, uses a different methodology: it ranks languages solely by how frequently their tutorials are searched on Google. Python already held the top position on the Pypl index at this time, with a commanding lead over Java.

The Tiobe Index Top 10 — June 2019

The following were the ten most popular programming languages in the June 2019 Tiobe Index:

1. Java — 15.004%
2. C — 13.3%
3. Python — 8.53%
4. C++ — 7.384%
5. Visual Basic .NET — 4.624%
6. C# — 4.483%
7. JavaScript — 2.716%
8. PHP — 2.567%
9. SQL — 2.224%
10. Assembly — 1.479%

The Pypl Index Top 10 — June 2019

The following were the ten most popular programming languages in the June 2019 Pypl Index. Python’s lead here was especially pronounced, more than doubling Java’s share:

1. Python — 28.08%
2. Java — 20.51%
3. JavaScript — 8.29%
4. C# — 7.41%
5. PHP — 6.96%
6. C/C++ — 5.76%
7. R — 4.15%
8. Objective-C — 2.82%
9. Swift — 2.36%
10. Matlab — 1.95%

Other Notable Movers in the June 2019 Tiobe Index

Swift and Objective-C

Apple’s Swift climbed to 11th place with a rating of 1.419 percent, up from 15th a year earlier and 18th the previous month — a notable acceleration. Its predecessor, Objective-C, ranked 12th with a rating of 1.391 percent. Tiobe predicted that Objective-C would drop out of the top 20 within two years as Swift continued to absorb its developer base.

Groovy’s Unexpected Climb

Groovy was one of the most dramatic risers in the index, jumping to 14th place with a rating of 1.3 percent. A year earlier it had sat at 60th with a barely measurable 0.19 percent. Tiobe attributed the surge largely to Groovy’s adoption as a scripting language for the Jenkins continuous integration platform — widely used in DevOps pipelines. However, Tiobe also raised questions about whether Groovy could maintain a top-20 position long-term without broader adoption beyond the CI/CD tooling space.

Why Python’s Growth Matters for DevOps and Engineering Teams

Python’s rise is not isolated to a single domain. The language has become a default choice across machine learning, data engineering, infrastructure automation, and scripting — areas that overlap heavily with modern DevOps practice. Tools such as Ansible are Python-native, and the language’s extensive standard library and active package ecosystem (PyPI) lower the barrier for engineers moving between disciplines. The combination of readability, versatility, and ecosystem depth explains why both beginner developers and experienced engineers continue to drive its search volume upward.

Note: The rankings and ratings in this article reflect the Tiobe and Pypl indices as published in June 2019 and should not be taken as current standings. Both indices are updated monthly and rankings change over time.

BreakFree Solutions Launches RapidDeploy for AKS

BreakFree Solutions, a professional services firm specializing in cloud, DevOps, and automation for enterprise IT, announced the launch of BreakFree RapidDeploy for AKS — a structured engagement designed to deploy, operationalize, and onboard development teams onto Microsoft Azure Kubernetes Services (AKS) within a compressed timeframe of approximately 10 weeks.

What Is Azure Kubernetes Services (AKS)?

Azure Kubernetes Services is Microsoft Azure’s fully managed Kubernetes offering. It abstracts much of the control-plane complexity — including cluster provisioning, upgrades, and scaling — so teams can focus on deploying and operating containerized workloads rather than managing Kubernetes infrastructure. For enterprises pursuing cloud-native development and mature DevOps practices, AKS provides a production-ready foundation with built-in security and integration with Azure’s broader ecosystem.

Why Operationalizing AKS Is Non-Trivial

While AKS simplifies Kubernetes cluster management, getting the platform fully operational for enterprise development teams is a different challenge. A production-grade AKS environment typically requires:

• Establishing networking foundations (virtual networks, ingress controllers, private endpoints)
• Configuring identity and access management (Azure Active Directory integration, RBAC)
• Implementing security controls (Pod Security policies, Azure Policy, secrets management)
• Building CI/CD pipelines integrated with the cluster
• Setting up monitoring, logging, and alerting stacks
• Defining standard operating procedures and runbooks for operations teams
• Onboarding and upskilling development teams unfamiliar with container-native patterns

Without the right foundational components in place — or without a DevOps-oriented IT culture — these efforts can stall, delaying the actual business value of the platform.

“The real value of the AKS platform is in the transition of developers and the associated application architectures to build cloud-native workloads. The longer you spend trying to operationalize the AKS development platform, you delay the ability to actually develop effectively on cloud.”

— Bradley Clerkin, CTO, BreakFree Solutions

How RapidDeploy for AKS Works

BreakFree RapidDeploy for AKS is a time-boxed professional services engagement built around several key delivery mechanisms:

• Automation: Infrastructure-as-code and scripted provisioning reduce manual configuration and improve repeatability.
• Reference architectures: Pre-validated AKS architecture patterns provide a proven starting point, avoiding common design pitfalls.
• Standard operating procedures: Documented runbooks ensure operations teams can manage the platform after the engagement ends.
• Structured workshops: Hands-on sessions accelerate knowledge transfer to the customer’s engineering and operations staff.
• Agile scrum delivery: Iterative sprints provide transparency, collaboration, and regular deliverable checkpoints throughout the 10-week engagement.

A key differentiator emphasized by BreakFree is the pairing model — customer engineers work alongside BreakFree’s AKS subject matter experts during deployment, enabling knowledge transfer in context rather than through documentation alone.

“The effort associated with a full AKS deployment may be complicated depending on your IT group’s current state and skills. But the value and the benefits of containers and establishing an AKS development platform will certainly justify these efforts for enterprises looking to modernize software development.”

— Mitch Northcutt, SVP, BreakFree Solutions

Target Audience and Use Cases

RapidDeploy for AKS is positioned for enterprise IT organizations that:

• Have committed to AKS as their container platform but lack internal Kubernetes expertise at scale
• Want to move quickly without compromising security or operational readiness
• Need to onboard multiple development teams onto a shared platform with governance guardrails in place
• Are modernizing legacy application architectures toward cloud-native, containerized workloads

About BreakFree Solutions

BreakFree Solutions is an enterprise IT professional services firm with expertise in cloud-native design, infrastructure automation, continuous deployment, DevOps, and Agile delivery. At the time of this announcement, BreakFree Solutions operated as a division of Fifth Third Bank, serving the commercial sector. Fifth Third Bancorp is headquartered in Cincinnati, Ohio, and trades on the Nasdaq Global Select Market under the ticker symbol FITB.

Source: GlobeNewswire, June 10, 2019.

Why Kubernetes Has Become Central to Multi-Cloud Strategy

Kubernetes has moved well beyond experimental status. IT leaders across industries are deploying it in mainstream production environments, using it to bridge on-premises infrastructure with public cloud platforms. A significant driver of this adoption is that major cloud providers — AWS (Amazon EKS), Microsoft Azure (AKS), and Google Cloud (GKE) — have all embraced Kubernetes and offer it as a managed service. This lowers the barrier to entry, allowing teams to experiment incrementally and scale consumption on a pay-as-you-go basis.

The result is a growing ecosystem of tooling, community knowledge, and integrations that continues to accelerate enterprise digitalization. But with opportunity comes complexity. As Kubernetes use cases expand, three considerations stand out for teams operating in multi-cloud environments.

1. Evolving IT and Developer Roles Demand a New Operating Model

The shift to multi-cloud and multi-cluster Kubernetes environments has fundamentally changed what IT operations teams manage. Applications are increasingly decomposed into microservices and packaged as containers — but not all workloads are ready for containerization at the same pace. This means IT teams often manage hybrid environments where containerized and non-containerized workloads coexist, sometimes across multiple clusters and clouds simultaneously.

For application developers, Kubernetes introduces genuine workload portability. Because it operates as a consistent abstraction layer across public and private cloud infrastructure, developers can target Kubernetes APIs regardless of the underlying provider. Combined with a large open-source community, rich documentation, and widely available tutorials, this portability increases developer velocity and reduces friction when moving workloads between environments.

The practical implication: both operators and developers need to upskill. Platform engineers need to understand multi-cluster networking, policy management, and federated workload scheduling. Developers need enough operational awareness to write applications that take advantage of Kubernetes primitives — health checks, resource limits, rolling deployments, and service discovery — from the start.

2. The Network Security Surface Area Is Expanding

Running containers in production — especially on bare metal — pushes the boundaries of what traditional networking and security tooling was designed to handle. Container workloads are ephemeral, horizontally scaled, and communicate extensively over internal service meshes. Each microservice boundary is a potential attack surface, and in large deployments those surfaces multiply rapidly.

Kubernetes is designed for continuous delivery, zero-downtime deployments, and high availability. These are valuable operational properties, but they also mean the environment is in constant flux — pods spin up and down, IP addresses change, and network policies need to reflect the current state of the cluster at all times. Static, perimeter-based security models are inadequate in this context.

Security teams working with Kubernetes environments should focus on several layers:

• Pod security: Use Pod Security Admission (or equivalent policies) to restrict what containers can do at runtime.
• Network policies: Define explicit ingress and egress rules between namespaces and services using Kubernetes NetworkPolicy resources or a CNI plugin that supports fine-grained policy (e.g., Calico, Cilium).
• Image security: Scan container images for known CVEs before they reach production. Implement admission controllers to block non-compliant images.
• Secrets management: Avoid storing secrets in environment variables or ConfigMaps. Use a dedicated secrets manager and integrate it with the cluster via a CSI driver or sidecar.
• Audit logging: Enable Kubernetes API audit logs and ship them to a central SIEM for anomaly detection.

As microservice architectures grow, mutual TLS (mTLS) between services — often implemented via a service mesh such as Istio or Linkerd — becomes an important defense-in-depth measure, encrypting and authenticating east-west traffic inside the cluster.

3. DevOps Adoption Is Accelerating — and Kubernetes Is a Catalyst

DevOps aims to unify application development and operations across the full software lifecycle: planning, coding, building, testing, releasing, deploying, operating, and monitoring. Kubernetes contributes directly to this goal by providing a common substrate that both developers and operators can target, removing the “it works on my machine” problem at the infrastructure level.

Practically, this means developers can use the same Kubernetes manifests or Helm charts across local development environments, CI pipelines, staging clusters, and production — reducing environment drift and accelerating release cycles. Operators, in turn, gain standardized interfaces for observability, scaling, and rollback rather than managing bespoke deployment scripts per application.

The growing Kubernetes ecosystem reinforces this collaboration. Tools like Argo CD and Flux bring GitOps workflows to cluster management. Prometheus and Grafana provide a consistent monitoring stack. Tekton and other Kubernetes-native CI systems let teams define pipelines as code in the same repositories as their applications. This convergence gives DevOps teams a coherent, auditable, and repeatable delivery pipeline from code commit to production.

Looking Ahead: Kubernetes as Enterprise Infrastructure

Kubernetes is no longer a cutting-edge experiment — for many enterprises it is foundational infrastructure. As adoption matures, the ecosystem of tools, managed services, and community knowledge will continue to expand, making it easier for organizations at every stage of the journey to adopt and operate it effectively.

For IT leaders planning their multi-cloud strategies, the three considerations above are not one-time checkboxes. They require ongoing attention as cluster scale grows, as new workloads are onboarded, and as the threat landscape evolves. Organizations that invest in the operational discipline to manage Kubernetes well — across roles, security, and DevOps culture — will be better positioned to deliver software faster and more reliably across both private and public cloud environments.

Network Congestion Caused the Disruptive Outage

On June 2, 2019, a significant outage struck Google’s (GOOGL) digital infrastructure, disrupting services for consumers and businesses alike. Core Google products — including YouTube and Gmail — were unavailable for several hours across the United States and Europe. The outage was widely reported at the time, including by The Verge.

The disruption was not limited to Google’s own consumer properties. Third-party businesses hosting applications and data on Google Cloud Platform (GCP) also experienced degraded service. Notable affected customers included e-commerce platform Shopify (SHOP) and social media company Snap (SNAP).

Google attributed the incident to network congestion primarily affecting its infrastructure in the eastern United States. Congestion events of this scale are rare for hyperscale cloud providers, but when they occur they can cascade rapidly across interconnected services — demonstrating how a single infrastructure fault can ripple into customer-facing disruptions spanning multiple continents.

Why Outages Are Especially Costly for Cloud Vendors

Cloud providers sell reliability as a core part of their value proposition. Service level agreements (SLAs) typically promise 99.9% or higher uptime for compute and managed services. A multi-hour outage affecting core networking infrastructure can breach those commitments, trigger financial credits, and — more damagingly — erode customer trust at exactly the moment a provider is trying to win enterprise workloads away from competitors.

The June 2019 event was particularly notable because it simultaneously hit both consumer services (YouTube, Gmail) and enterprise cloud customers. This dual impact made the outage highly visible in the press, amplifying reputational damage beyond what a purely enterprise-focused incident might have caused.

Google Cloud’s Competitive Position at the Time

By mid-2019, Google had been steadily narrowing the gap with the market leaders in cloud infrastructure. According to Synergy Research data cited at the time, the global cloud market share breakdown in Q1 2019 was approximately:

• Amazon Web Services (AWS): ~35%
• Microsoft Azure: ~15%
• Google Cloud Platform: ~7%
• IBM Cloud: ~6%
• Alibaba Cloud: ~5%

Google’s upward trajectory — having reportedly overtaken IBM in global share — made the outage particularly poor timing. Enterprises evaluating a move from AWS or Azure to GCP would weigh incidents like this heavily during due diligence. A well-publicised outage, regardless of its root cause, gives procurement teams pause and can extend sales cycles or tip decisions toward more established providers.

The Stakes: A Multi-Hundred-Billion-Dollar Market

The global public cloud market in 2019 was estimated by Gartner to be worth more than $214 billion. Even a fraction of a percentage point of market share represented billions of dollars in annual recurring revenue. For Google, which was aggressively investing in data centre capacity and enterprise sales to close the gap with AWS and Azure, incidents that undermine customer confidence carry an outsized financial cost relative to their technical duration.

Lessons for DevOps Teams Running on GCP — or Any Cloud

Events like the June 2019 Google outage are a useful reminder that cloud providers, regardless of scale, are not immune to infrastructure failures. DevOps and platform engineering teams can draw several practical lessons:

• Design for multi-region failover. Applications that require high availability should distribute traffic across at least two cloud regions, ideally in separate geographic zones. A single-region deployment is a single point of failure.
• Understand your provider’s SLA scope. SLAs typically apply per service, not holistically. Network-level incidents may fall into grey areas; read the terms carefully before an incident occurs.
• Monitor third-party dependencies. If your application relies on GCP-hosted APIs from vendors like Shopify or Snap, their availability depends on their cloud provider’s uptime — not just yours.
• Use cloud status pages and alerting. Subscribe to Google Cloud Status and set up automated alerts so your on-call team learns about provider incidents as quickly as possible.
• Run chaos engineering exercises. Simulating region failures in staging environments surfaces architectural weaknesses before a real outage exposes them in production.

Conclusion

The June 2019 Google outage was a reminder that even the world’s most sophisticated infrastructure operators experience failures. For Google Cloud specifically, the timing was commercially sensitive: the company was actively courting enterprise customers and gaining ground on AWS and Azure. While a single incident is unlikely to reverse long-term market trends, it reinforces the importance of resilient, multi-cloud or multi-region architectures for any workload where downtime has real business consequences.

For DevOps practitioners, the takeaway is straightforward: treat your cloud provider as a dependency that can fail, and architect accordingly.

A People-First Philosophy Drives Award-Winning Firm

On Kenner Kingston’s desk at Salt Lake City-based Architectural Nexus sit schematic sketches that tell the story of the firm’s evolving mission. They are not renderings for a hospital wing or a research laboratory—two market segments where Architectural Nexus has built considerable expertise—but rather plans for a rainwater collection and reuse system for an off-the-grid home in Moab, Utah. The firm is collaborating with a community nonprofit that helps low-income residents construct their own sustainable straw-bale homes in the desert region.

That project encapsulates what Kingston, president of Architectural Nexus, describes as a fundamental shift in the firm’s thinking over the past five years.

“In the past five or so years, we’ve had a real shift in our thinking. It is not about the project or the building, it is about the people. We are people driven in the pursuit of meaningful experiences.”

— Kenner Kingston, President, Architectural Nexus

That philosophy earned Architectural Nexus recognition as ENR’s 2019 Intermountain Design Firm of the Year. The firm was founded through the 2003 merger of Jensen Haslem Architects and Thomas Petersen Hammond Architects, and has since grown to nearly 120 employees with offices in Salt Lake City and Sacramento, California. The firm reported revenue of $19.9 million in 2017 and $22 million in 2018, regularly ranking among the top ten design firms in the Intermountain region.

A Diverse and Multidisciplinary Design Studio

Robb Harrop, director of design at Architectural Nexus, articulates the firm’s client-centered approach succinctly: “We like projects that have a purpose and a vision. Something deeper than just keeping the rain off your head. We want to help clients find what is meaningful to them.”

That approach has produced strong results across multiple sectors. In healthcare, the firm has designed all four completed phases of the Huntsman Cancer Institute (HCI) at the University of Utah in Salt Lake City. The most recent phase—a 230,000-square-foot, $100-million research and office facility—exemplifies the firm’s patient-centered design thinking. Ben Tanner, executive director of the HCI, credits Architectural Nexus with understanding the unique needs of oncology patients.

“Their thoughtful work shows up in our patient satisfaction surveys. Patients always have great things to say about the look and feel of our buildings, and it’s a wonderful place for our clinical and research staff as well.”

— Ben Tanner, Executive Director, Huntsman Cancer Institute

A fifth phase of the HCI project was underway at the time of the award. The firm also completed a $114-million hospital in Nampa, Idaho, for the St. Luke’s Health System, which opened in fall 2017. Sandee Gehrke, vice president of operations at St. Luke’s, praised Architectural Nexus for investing time to understand the organization’s culture and brand identity before putting pen to paper.

Beyond healthcare, the firm’s Salt Lake City portfolio includes the $160-million, 400,000-square-foot Hardware Village apartment and condominium complex and a $7-million, 48,000-square-foot mixed-use residential and retail building in the city’s 9th and 9th district. In higher education, the firm completed the College Activities Center at Gillette College in Gillette, Wyoming, which features a 2,100-seat basketball arena, athlete support facilities, and flexible multi-use space.

Multiple LEED-certified designers work alongside specialists in landscape design, energy analysis, interior design, animation and visualization, and graphic design. Kingston notes that housing all these disciplines under one roof enables the firm to pursue both large, complex commissions and smaller experimental projects that push the boundaries of new technology and construction techniques.

Employee Ownership as a Cultural Cornerstone

Architectural Nexus was in the process of completing an Employee Stock Ownership Plan (ESOP) in 2019. For Kingston, what began as a leadership-transition and financial planning tool evolved into a genuine expression of the firm’s values.

“In 2014 we really had a change in mindset, and the ESOP went from being a financial tool to a culture tool, a way to align with the values of the firm… One of our partners said at one point, ‘What is more Nexus than an ESOP?’ and it just hit me that he was right.”

— Kenner Kingston, President, Architectural Nexus

Julie Berreth, vice president of operations, describes the firm’s internal culture as deliberately flat—characterized by a compressed compensation structure, a commitment to paying employees a living wage, and an extensive mentoring program designed to develop the next generation of architects and designers. The firm also joined the International Living Future Institute’s (ILFI) JUST program, which provides guidelines and external auditing on social justice and diversity performance.

To translate these values into direct community impact, Architectural Nexus launched the Nexus Builds program approximately two years prior to receiving the award. The initiative has partnered with the Seattle-based Block program to design and build tiny off-the-grid homes for people experiencing homelessness on donated urban lots. At the time of the award, employees were constructing a tiny home in a corner of the firm’s Salt Lake City headquarters parking lot, scheduled for delivery to Seattle. Director of communications Brian Cassil described the project as meaningful beyond simple volunteerism: “This is very purpose driven, but at its core it is also about mastery and how to get better at what we do.”

Beyond Conservation: Embracing Regenerative Design

Architectural Nexus has been an early advocate of sustainable design in the Intermountain region. In 2010, its Salt Lake City office became one of the first LEED Platinum certified buildings in the area. But the firm’s ambitions have since moved well beyond conventional green building metrics.

The clearest expression of this trajectory is the firm’s Sacramento office, a renovated office and warehouse building that opened in 2016 in the city’s historic district. The project was designed in accordance with the International Living Future Institute’s Living Building Challenge (LBC)—one of the most rigorous sustainability frameworks in the built environment industry. The completed building features a composting system, onsite agriculture, onsite water filtration and treatment, highly efficient electrical systems, and a solar photovoltaic array. It achieved LEED Platinum certification (level four) and was recognized in 2018 as the first Living Building in California and the 19th in the world certified by ILFI.

Berreth frames the firm’s design philosophy as anticipatory rather than reactive: “We don’t want to look at a building stuck on a site, but how it enhances the wider community and the people around it. Even if we are not putting something like PV panels on a project today, we want to anticipate they might be added one day and design space for them.”

Kingston sees regenerative design—which seeks to restore the built environment to a more natural state, treating buildings as habitats rather than machines—as the logical next phase for the industry and the firm’s greatest strategic opportunity.

“For a long time, most firms in our industry have been climbing this mountain called sustainability. Some of us who were lucky got to the top and looked across to see what was next, and we saw regenerative design, things like the Living Building Challenge… I’m excited for the next five years because I think we are perfectly positioned for oncoming growth in regenerative design.”

— Kenner Kingston, President, Architectural Nexus

From rainwater systems for low-income desert housing to one of California’s most advanced Living Buildings, Architectural Nexus continues to demonstrate that people-centered architecture and environmental responsibility are not competing priorities—they are the same goal expressed at different scales.

Google Announces $2.6 Billion Acquisition of Looker

In June 2019, Google announced its intention to acquire Looker, a cloud-based business intelligence (BI) and data analytics platform, for $2.6 billion in an all-cash transaction. Upon closing, Looker would join the Google Cloud portfolio. The deal positioned Google Cloud more aggressively in the enterprise analytics market, arriving around the same time Salesforce announced its much larger acquisition of Tableau.

For Google Cloud, the acquisition was framed as filling a critical gap: giving customers a more complete path from raw data ingestion to actionable business insights, all integrated with BigQuery, Google’s managed enterprise data warehouse.

Why Looker? Bridging the Gap Between Data and Decisions

Google Cloud CEO Thomas Kurian described the strategic rationale clearly: Looker would help deliver “a more complete analytics solution from ingesting data to visualising results and integrating data and insights into daily workflows.” In practical terms, this meant customers migrating from legacy data stacks to BigQuery would gain access to a mature BI layer without needing to bolt on third-party tooling.

Looker’s core value proposition — its LookML modelling layer and ability to connect to a wide range of data sources — made it a natural complement to BigQuery. Prior to the acquisition, the two companies had already established a meaningful partnership:

• Looker had announced a BigQuery integration enabling data teams to build machine learning models directly within BigQuery through the Looker interface.
• The two companies shared more than 350 mutual customers, including BuzzFeed, Hearst, and Yahoo!

The Multi-Cloud Commitment: A Strategic Differentiator

Perhaps the most significant aspect of the acquisition announcement — especially for enterprise customers already spread across multiple cloud providers — was Google’s explicit pledge to maintain Looker’s multi-cloud functionality.

Looker was already compatible with all major cloud databases and data warehouses at the time of the announcement, including:

• Amazon Redshift
• Microsoft Azure SQL
• Oracle
• Teradata
• Google BigQuery

Looker CEO Frank Bien confirmed that customers should expect continued support across all major cloud databases after the acquisition closed — a reassurance aimed directly at enterprises nervous about vendor lock-in.

Kurian reinforced this position in a public statement: “While we deepen the integration of Looker into Google Cloud Platform, customers will continue to benefit from Looker’s multi-cloud functionality and its ability to bring together data from SaaS applications like Salesforce, Marketo, and Zendesk, as well as traditional data sources. This empowers companies to create a cohesive layer built on any cloud database, as well as on other public clouds and in on-premise data centres.”

Context: Google Cloud’s Broader Open and Multi-Cloud Direction

The Looker acquisition did not happen in isolation. At Google Cloud Next in San Francisco in April 2019, the company’s central themes were hybrid cloud, multi-cloud, and open source. A notable moment from the keynote was the announcement of partnerships with seven open source database vendors, including Confluent, MongoDB, and Redis Labs.

Looker’s broad database compatibility aligned directly with this open, multi-cloud positioning — and gave Google Cloud a commercial BI product that could operate credibly across competitor infrastructure, rather than requiring customers to consolidate entirely onto GCP.

Industry Reaction

Industry analysts viewed the deal positively in the context of the fast-growing data analytics market. Frank Gens, Senior Vice President and Chief Analyst at IDC, commented: “Google Cloud is one of the leaders in the data warehouse market, and the addition of Looker will further strengthen their ability to serve the needs of enterprise customers while also advancing their commitment to multi-cloud.”

Kurian also addressed continuity of support: “Looker customers can rest assured that the high-quality support experience that Looker has provided will be bolstered by the resources, expertise, and global presence of our cloud team. We will also continue to support best of breed analytics and visualisation tools to provide customers the choice to use a variety of technologies with Google Cloud’s analytics offering.”

Key Takeaways for DevOps and Data Engineering Teams

• Multi-cloud BI is a viable strategy. Google’s commitment to keeping Looker database-agnostic acknowledged that most enterprises operate across more than one cloud, and analytics tooling must reflect that reality.
• BigQuery integration deepens. Teams already using BigQuery gained a tighter native path to Looker’s modelling and visualisation capabilities, including ML model creation directly within BigQuery.
• Vendor lock-in concerns are legitimate. Google’s explicit pledge to maintain cross-cloud support was a direct response to enterprise concern — it is worth monitoring how that commitment evolved post-acquisition.
• SaaS data connectivity matters. Looker’s ability to connect to SaaS sources such as Salesforce, Marketo, and Zendesk meant the platform addressed not just cloud warehouse data, but the broader operational data landscape.

The Macro Shift in Enterprise IT Spending

Enterprise IT budgets are under pressure from two converging forces: the accelerating migration from traditional, capital-intensive infrastructure to flexible hybrid multi-cloud architectures, and the deepening integration of artificial intelligence into core business systems. Together, these trends are forcing technology product managers and CIOs to fundamentally rethink how and where they allocate technology investment.

According to Gartner’s 2019 global IT spending forecast, worldwide IT expenditure was projected to reach $3.79 trillion for the year—a modest increase of just 1.1 percent over 2018. Currency volatility driven by a strengthening US dollar, trade tensions, and uncertain macroeconomic conditions contributed to a downward revision of that forecast from earlier in the year.

“Currency headwinds fuelled by the strengthening US dollar have caused us to revise our 2019 IT spending forecast down from the previous quarter. Through the remainder of 2019, the US dollar is expected to trend stronger, while enduring tremendous volatility due to uncertain economic and political environments and trade wars.”

John-David Lovelock, Vice President, Gartner

IT Infrastructure Market Development

Not all segments of IT spending are moving in the same direction. The data centre systems segment was expected to experience the sharpest decline in 2019—a drop of approximately 2.8 percent—driven primarily by falling average selling prices (ASPs) in the server market. Component cost adjustments and improved hardware efficiency were major contributors to that compression.

In contrast, the enterprise software market was forecast to grow to $427 billion in 2019, up roughly 7.1 percent from $399 billion in 2018. The primary driver of this growth is the ongoing migration of enterprise workloads from traditional, on-premises software to cloud-based alternatives. Application software has seen the most significant cloud shift to date.

Looking beyond application software, Gartner anticipated increased near-term growth in the infrastructure software segment, particularly in:

• Integration Platform as a Service (iPaaS): Middleware and integration tooling delivered as cloud services, enabling organizations to connect disparate on-premises and cloud applications without managing underlying infrastructure.
• Application Platform as a Service (aPaaS): Cloud-hosted environments for developing, running, and managing applications, reducing the operational overhead traditionally associated with platform management.

For technology product managers, this environment requires a more deliberate portfolio strategy: balancing investments in high-growth cloud and software categories against the natural decline in hardware-centric markets. According to Gartner’s assessment, product managers who take a long-term view of portfolio decisions made in 2019 will be better positioned for success through 2020 and beyond.

Outlook for AI Applications and Spending Growth

Artificial intelligence is increasingly cited as a disruptive force reshaping IT investment priorities—but Gartner’s analysis makes an important distinction that is often overlooked in market commentary: AI is not a standalone product category. It is a set of techniques and a computer engineering discipline that is being embedded into existing products, services, and development pipelines across virtually every industry.

“The choices CIOs make about technology investments are essential to the success of a digital business. Disruptive emerging technologies, such as artificial intelligence, will reshape business models as well as the economics of public- and private-sector enterprises. AI is having a major effect on IT spending, although its role is often misunderstood.”

John-David Lovelock, Vice President, Gartner

This framing has practical consequences for how IT leaders should evaluate AI-related spending. Rather than tracking a discrete “AI budget line,” organizations should expect AI capabilities to appear as enhancements within their existing software, cloud platform, and infrastructure investments. Examples include:

• Cloud platform AI services: Major cloud providers embed machine learning APIs, natural language processing, and computer vision directly into their platform offerings, making AI accessible without specialized infrastructure.
• Enterprise software augmentation: CRM, ERP, and ITSM vendors are integrating AI-driven features—predictive analytics, anomaly detection, intelligent automation—into their core products.
• DevOps and operations tooling: AIOps platforms use machine learning to correlate events, predict incidents, and reduce alert noise, embedding AI into the operational workflow rather than treating it as a separate initiative.

Strategic Implications for CIOs and Technology Leaders

The combined effect of cloud migration and AI integration is pushing IT investment decisions in a consistent direction: away from owned, depreciating hardware assets and toward recurring service-based expenditure that can scale with business demand. For CIOs and IT strategy teams, several practical considerations follow from this:

• Rebalance CapEx vs. OpEx: Continued cloud adoption shifts capital expenditure to operational expenditure. Budget models, approval processes, and financial governance frameworks need to account for this structural change.
• Evaluate AI as a platform feature, not a project: Rather than funding standalone AI initiatives, assess how cloud vendors and software providers are embedding AI capabilities into platforms already in use. This often delivers value faster and with lower implementation risk.
• Prioritize integration investment: As multi-cloud and hybrid architectures mature, integration complexity grows. iPaaS and aPaaS investments that reduce integration friction can unlock value across the broader portfolio.
• Plan for currency and macroeconomic risk: Global IT procurement is exposed to foreign exchange volatility. Multi-year cloud agreements and vendor negotiations should factor in currency hedging considerations where applicable.
• Take a multi-year view of portfolio decisions: Near-term market softness in hardware segments does not eliminate the need for infrastructure investment. Strategic leaders plan for the 2-to-3-year horizon, not just the current fiscal year.

Summary

The convergence of cloud adoption and AI integration is not a temporary market fluctuation—it represents a structural shift in how IT value is created and delivered. Organizations that recognize AI as a cross-cutting discipline embedded in cloud platforms and enterprise software, rather than an isolated product category, will make more coherent investment decisions. Meanwhile, the continued decline of traditional data centre hardware spending and the growth of enterprise software and cloud services reflect a market that has already begun to price in this new reality. For DevOps teams, platform engineers, and technology strategists, aligning investment priorities with these structural trends is increasingly a competitive necessity.

What Is a Cloud Computing Architect?

A cloud computing architect is responsible for designing, planning, and overseeing an organisation’s cloud adoption strategy. Rather than simply migrating existing systems to a cloud provider, architects evaluate business requirements and translate them into scalable, secure, and cost-effective cloud solutions.

In practice, a cloud architect operates at the intersection of infrastructure engineering, security, networking, and business strategy. They work with teams across an organisation to define standards, select services, and ensure the chosen architecture aligns with compliance, performance, and budget goals.

Why Demand for Cloud Architects Has Grown

Traditionally, companies owned and operated their own physical servers, storage arrays, and networking equipment. This model is capital-intensive, slow to scale, and expensive to maintain. Cloud computing—delivered by providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—allows organisations to provision compute, storage, and networking resources on demand, paying only for what they use.

As adoption has accelerated, the gap between available cloud skills and organisational need has widened. Someone must design these environments thoughtfully. That responsibility falls to the cloud architect.

Core Concepts Every Cloud Architect Must Understand

Before specialising in a specific cloud platform, a strong architectural foundation requires fluency in the following areas:

• Cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) each carry different responsibilities for the customer versus the provider. Architects must know when to use each.
• Deployment models: Public, private, hybrid, and multi-cloud deployments have distinct trade-offs in cost, control, and compliance. Most enterprise environments today are hybrid or multi-cloud.
• Networking fundamentals: Virtual networks, subnets, load balancers, DNS, VPNs, and content delivery networks (CDNs) all come into play when designing resilient cloud environments.
• Security and identity: IAM (Identity and Access Management), encryption at rest and in transit, zero-trust principles, and compliance frameworks such as SOC 2, HIPAA, and GDPR are non-negotiable considerations.
• High availability and disaster recovery: Architects design for failure. This means understanding replication, failover strategies, recovery time objectives (RTO), and recovery point objectives (RPO).
• Cost optimisation: Cloud bills can spiral quickly without governance. Architects implement tagging strategies, reserved instance planning, auto-scaling policies, and rightsizing to manage spend.

Platform Specialisations: AWS, Azure, and Google Cloud

While the underlying concepts are transferable, real-world cloud architecture work requires deep familiarity with at least one major provider’s service catalogue. Each platform has its own terminology, tooling, and certification track.

Amazon Web Services (AWS)

AWS is the longest-established major cloud provider and holds the largest market share. Its breadth of services—from compute (EC2, Lambda) to managed databases (RDS, DynamoDB) to AI/ML tooling (SageMaker)—means architects must be selective and intentional in service selection. The AWS Certified Solutions Architect certification is widely recognised and a common benchmark for hiring teams.

Microsoft Azure

Azure is dominant in enterprises that already have significant Microsoft investments—Active Directory, Windows Server, Office 365—because of its native integration with these products. Azure’s hybrid capabilities, particularly through Azure Arc and Azure Stack, make it a frequent choice for organisations that cannot fully leave on-premises infrastructure. The Microsoft Certified: Azure Solutions Architect Expert designation is the relevant credential.

Google Cloud Platform (GCP)

GCP is particularly strong in data analytics (BigQuery), container orchestration (GCP originated Kubernetes, which it offers through GKE), and machine learning workloads. It is frequently the platform of choice for data engineering and AI-heavy architectures. The Professional Cloud Architect certification is Google’s primary credential for this role.

Building Practical Skills: Beyond Certifications

Certifications validate foundational knowledge, but hands-on experience is what employers actually evaluate. Aspiring cloud architects should invest time in the following practical activities:

• Deploy real projects: Spin up environments in a free-tier account and build something end-to-end—a web application, a data pipeline, or a CI/CD system—rather than following guided labs alone.
• Work with Infrastructure as Code (IaC): Terraform, AWS CloudFormation, and Azure Bicep are standard tools for defining cloud infrastructure in version-controlled, repeatable templates. Architects who cannot write IaC are at a significant disadvantage.
• Learn containerisation and orchestration: Docker and Kubernetes underpin a large proportion of modern cloud-native workloads. Understanding pod scheduling, persistent storage, and service meshes is increasingly expected at the architect level.
• Study reference architectures: AWS, Azure, and GCP all publish well-architected framework documentation and reference architectures for common patterns. Reading and critiquing these builds pattern recognition quickly.
• Contribute to architectural decisions at work: If you are already employed in a technical role, volunteer to document, review, or propose architectural decisions. Exposure to real trade-off discussions is invaluable.

A Suggested Learning Path

If you are starting from a general IT background, a logical progression looks like this:

1. Foundations: Understand networking, Linux, and basic scripting (Python or Bash). These are prerequisites regardless of which cloud you target.
2. Cloud fundamentals: Complete an introductory course on your chosen provider’s core services. AWS Cloud Practitioner, Azure Fundamentals (AZ-900), or Google Cloud Digital Leader are appropriate starting points.
3. Associate-level certification: Progress to an associate-level credential such as AWS Solutions Architect Associate or Azure Administrator (AZ-104), which introduce you to practical service configuration.
4. Hands-on projects: Build and document at least two or three real projects. Include them in a GitHub portfolio or personal blog.
5. Professional or specialist certification: Attempt a professional-level certification that covers architectural decision-making, such as AWS Solutions Architect Professional or GCP Professional Cloud Architect.
6. Broaden your scope: Study security specialisations, DevOps practices, and cost management. Consider exposure to a second cloud provider to become more versatile.

Career Outlook

Cloud architecture is not a role with a hard ceiling. Architects can move into principal or distinguished engineer tracks, cloud centre-of-excellence leadership, or CTO-adjacent strategy roles. Because the discipline spans technical depth and business alignment, strong cloud architects are valuable across virtually every industry vertical—from financial services to healthcare to media.

The continued growth of multi-cloud adoption, edge computing, AI workloads, and cloud-native development patterns means the scope of the role is expanding rather than narrowing. Investment in cloud architecture skills has a long time horizon.

Summary

Becoming a cloud computing architect requires a combination of foundational IT knowledge, cloud-platform expertise, hands-on project experience, and the ability to translate business requirements into infrastructure decisions. Start with the fundamentals, choose a primary platform, earn a recognised certification, and build real systems. The demand is real, the career path is well-defined, and the learning resources—both free and paid—have never been more accessible.

Urban Meyer’s Legacy: Wins, and a Troubled Roster History

Urban Meyer’s record as a college football coach is difficult to dispute. He built undefeated seasons and national championship teams at Utah, Florida, and Ohio State, establishing himself as one of the most successful coaches in the modern era of the sport.

However, by mid-2019, Meyer had stepped away from coaching—citing health reasons—and was preparing to work as a college football analyst at Fox Sports. His departure from the sidelines brought renewed scrutiny to the conduct record of players from his programs.

Most significantly, former Florida defensive back Tony Joiner—a team captain during the Gators’ 2006 national championship season—was charged with murdering his wife. The case drew immediate comparisons to Aaron Hernandez, another former Meyer player at Florida who was convicted of murder and later died by suicide while incarcerated.

Reports at the time indicated that approximately 31 players were arrested during Meyer’s tenure at Florida, with additional arrests—many for lesser offenses—recorded during his time at Utah and Ohio State. These figures prompted ongoing debate about player recruitment standards and coaching accountability in high-profile college football programs.

Horse Racing’s Safety Crisis: Santa Anita and Suffolk Downs

The 2019 Santa Anita Park meet in California became one of the most controversial in horse racing history. By June 2019, 31 horses had died during the meet—a figure that alarmed the public, animal welfare advocates, and sports officials alike.

The Los Angeles Times publicly called for the meet to be shut down before its scheduled conclusion. The controversy was compounded by the fact that the Breeders’ Cup races were scheduled to be held at Santa Anita that fall, raising serious questions about whether the sport could address its safety problems in time.

Meanwhile, Suffolk Downs—a historic track in East Boston with more than 80 years of history—announced that it would hold its final live racing days on June 28 and 29, 2019, after which it would operate as a simulcast-only venue. The closure served as another data point for those arguing that horse racing as a live spectator sport was in long-term decline in the United States.

Terry Bowden Returns—as an Unpaid Intern at Clemson

Terry Bowden, son of Hall of Fame coach Bobby Bowden, had a long head-coaching career of his own that included stints at Auburn and Akron. After being let go from Akron following the 2018 season, Bowden made an unconventional return to football: joining Dabo Swinney’s staff at Clemson as an unpaid intern at the age of 63.

Bowden simultaneously enrolled in a graduate program in athletic leadership at Clemson—an unusual but arguably admirable demonstration that professional development in sports has no strict age ceiling.

UTEP QB Kai Locksley Suspended Following Arrest

Kai Locksley, who was the starting quarterback at the University of Texas at El Paso (UTEP) and the son of Maryland head coach Mike Locksley, was arrested and charged with driving under the influence, marijuana possession, and unlawfully carrying a weapon. Following the arrest, Locksley was suspended from the team pending further investigation and lost his starting position.

Texas A&M Player Arrested After Dispute Over Tacos

In what may be the most unusual entry in any sports incident log, Texas A&M defensive back Derrick Tucker was arrested and charged with assault following a physical dispute with another individual. The cause of the altercation, according to reports: a disagreement over tacos.

Tucker’s case served as the kind of story that columnists and sports commentators reach for when making the point that off-field incidents in college athletics span the full spectrum—from the tragic to the genuinely inexplicable.

Source: collegesportsmaven.io (June 2019). Original reporting attributed to TMG sports commentary. This article summarizes incidents as reported at that time; legal outcomes for individuals mentioned may have changed since publication.

Two Giants Enter the Cloud Gaming Arena

In mid-2019, both Google and Microsoft moved to make cloud game streaming a concrete product offering rather than a distant promise. Google revealed pricing and a launch game list for its Stadia platform, while Microsoft used its E3 showcase to double down on its own cloud streaming ambitions under the Project xCloud banner.

The timing made the competitive dynamic explicit: two of the world’s largest cloud infrastructure operators were now targeting the same workload — delivering interactive, low-latency game sessions to consumer devices over the public internet.

What Separates Stadia from Project xCloud?

The architecturally important distinction between the two services is hardware dependency:

• Google Stadia is a fully cloud-native platform. Games run entirely on Google’s data center hardware. Players need only a supported screen and an internet connection — no dedicated gaming console is required.
• Microsoft’s approach at E3 covered two related but distinct scenarios. The first is console streaming, which lets players stream games from their own Xbox console to a mobile device on the same network. The second — and the direct Stadia competitor — is Project xCloud, which streams games from Microsoft’s Azure data centers and does not require the player to own an Xbox.

Microsoft had originally announced Project xCloud in October 2018, making the E3 2019 demonstration roughly eight months of public development. Early hands-on reports from the event noted that streaming latency was acceptably low under controlled conditions, which is a critical threshold for any cloud gaming service to clear before a broader launch.

Why Latency Is the Central Engineering Problem

Cloud gaming is, at its core, a remote rendering problem with an unforgiving latency budget. The server renders each frame, compresses it, transmits it to the client, and must receive the player’s input in time to render the next frame — all within a window measured in milliseconds.

The latency requirements vary significantly by game genre:

• Turn-based and strategy games are relatively tolerant of higher latency. A delay of 100–150 ms may be imperceptible to players.
• Fast-paced first-person shooters (FPS) are extremely sensitive. Even modest additional latency creates a measurable competitive disadvantage, since the gap between a player’s input and the on-screen result directly affects aim and reaction time.
• Virtual reality (VR) has the strictest requirements of all. Latency above roughly 20 ms between head movement and display update is widely associated with motion sickness, making cloud-rendered VR a particularly difficult engineering target.

5G as the Enabling Infrastructure

Wired broadband connections can already support cloud gaming under good conditions, but truly mobile cloud gaming — playing a latency-sensitive title on a smartphone while away from Wi-Fi — depends heavily on next-generation mobile networks.

5G networks promise substantially lower radio access latency compared to LTE, along with higher and more consistent throughput. For cloud gaming specifically, the relevant gains are:

• Reduced round-trip time (RTT) between device and the nearest edge node, shrinking the input-to-render-to-display loop.
• Higher bandwidth to support higher resolution and frame rate video streams without buffering artifacts.
• Network slicing potential (a 5G feature) that could allow carriers to guarantee quality-of-service parameters for gaming traffic specifically.

The combination of 5G and edge computing — where game rendering infrastructure is placed physically closer to end users — is widely considered the most plausible path to cloud gaming that is competitive with local hardware for fast-paced titles.

The Microsoft–Sony Partnership Complication

One notable strategic wrinkle for Microsoft at the time of E3 2019 was its cloud infrastructure partnership with Sony — the maker of the PlayStation, which is Xbox’s primary competitor in the console market. Microsoft was simultaneously positioning Azure as the cloud backend for a rival’s gaming ecosystem while developing a competing cloud gaming service of its own.

This dual role — cloud provider and direct product competitor — is a dynamic that appears elsewhere in the cloud industry and creates genuine strategic tension. It may partly explain why Microsoft was measured in how much detail it disclosed about Project xCloud at E3.

The Subscription Model Precedent

Both Google Stadia and Microsoft’s cloud gaming efforts were entering a market already conditioned by subscription streaming services in adjacent entertainment categories. Music streaming (Spotify) and video streaming (Netflix) had demonstrated that consumers were willing to pay recurring fees for on-demand access to large content libraries without owning individual titles.

Whether that model translates cleanly to gaming remained an open question in 2019 — and has continued to be debated as the market has matured. Games involve interactivity and session ownership in ways that differ from passive media consumption, and the value proposition of “access without ownership” lands differently depending on the player.

Key Takeaways for DevOps and Cloud Engineers

• Cloud gaming is one of the most demanding real-time workloads a public cloud can be asked to serve, with strict latency, throughput, and encoding performance requirements.
• The distinction between console streaming (client-side hardware required) and cloud streaming (no local hardware required) is architecturally and commercially significant.
• Edge computing deployments that co-locate rendering capacity with 5G radio infrastructure are a logical evolution of this space.
• The competitive dynamics between hyperscalers (Azure, Google Cloud) in the consumer gaming market mirror patterns seen in enterprise cloud competition — infrastructure capabilities, global data center footprint, and network topology all matter.

Original reporting: telecoms.com, E3 2019 coverage, June 2019.

Mitsubishi Heavy Industries Validates Explosion-Proof Robot for Refinery Inspections

Mitsubishi Heavy Industries, Ltd. (MHI) has successfully completed a running test of its prototype EX ROVR, an autonomous plant inspection robot engineered with explosion-protection features. The test was conducted jointly with JXTG Nippon Oil & Energy Corp. at the Mizushima Refinery in Kurashiki, Okayama Prefecture, Japan.

The EX ROVR is purpose-built to operate safely in environments where flammable gases are present. Its explosion-proof design prevents the robot itself from becoming an ignition source — a critical requirement in oil refinery and petrochemical plant settings where even a small electric spark or excess heat could trigger a fire or explosion.

What the Running Test Demonstrated

During the trial at the Mizushima Refinery, the EX ROVR demonstrated the following capabilities:

• Autonomous multi-floor navigation: The robot moved independently through several floors of a confined plant building, including navigating staircases — a significant challenge for wheeled or tracked inspection platforms.
• Multi-sensor data collection: The EX ROVR gathered readings from its onboard sensor suite during the patrol run, simulating the data-collection tasks required in routine plant inspections.
• Automated docking and charging: The robot managed continuous operation by autonomously returning to a docking station for recharging, enabling extended, uninterrupted patrol cycles without human intervention.

The test confirmed that the prototype meets the baseline functional requirements for conducting routine patrol inspections and responding to emergency situations at JXTG Energy facilities. MHI and JXTG Energy are continuing to analyze test results to identify remaining issues and drive further improvements before commercialization.

Explosion-Protection Standards and Safe-Area Classification

A defining feature of the EX ROVR is its compliance with internationally recognized explosion-protection standards:

• Japan’s explosion-proof guidelines — the domestic regulatory framework governing equipment used in hazardous industrial environments in Japan.
• ATEX — the European Union directive covering equipment and protective systems intended for use in potentially explosive atmospheres.
• IECEx — the International Electrotechnical Commission’s scheme for certifying explosion-protected equipment, widely adopted globally.

Together, these certifications allow the EX ROVR to be deployed safely in Zone 1 environments — areas where an explosive gas atmosphere is likely to occur periodically during normal operation. This is a more demanding classification than Zone 2, making Zone 1 compliance especially relevant for active refinery process areas.

Technology Lineage: From Nuclear to Petrochemical

MHI’s development of the EX ROVR draws on its prior experience with robotics in extreme and hazardous environments, including support roles in nuclear plant accident containment. Applying ruggedized, safety-critical robotics expertise to oil and gas inspection reflects a broader industry trend of repurposing heavy-industry automation technologies for plant safety applications.

Path to Commercialization

The Mizushima Refinery test represents an early but meaningful milestone. MHI plans to conduct additional tests and demonstrations across a range of plant environments with the goals of:

• Increasing inspection coverage density — ensuring the robot can reliably check a greater proportion of plant assets per patrol cycle.
• Enhancing overall safety and robustness under varied operating conditions.
• Pursuing full commercialization of the EX ROVR platform for use across the refinery and petrochemical industry.

For plant operators, autonomous explosion-proof inspection robots represent a significant shift: routine hazardous-area patrols currently performed by human technicians could be handed to robots, reducing personnel exposure to dangerous environments and enabling more frequent, consistent data collection.

More information about MHI’s robotics and industrial automation work is available at www.mhi.com.

Source: finance.yahoo.com

Why Hedge Fund Sentiment Matters for Tech Stocks Like Splunk

Tracking what institutional investors — particularly hedge funds — do with their capital is one way analysts gauge conviction in a stock. Hedge fund 13F filings, published quarterly, reveal long equity positions held by funds managing over $100 million in assets. While these filings are backward-looking by up to 45 days, they can highlight meaningful accumulation or distribution patterns in a given stock.

Splunk Inc (NASDAQ: SPLK), the machine data analytics and SIEM platform provider, drew particular attention from institutional investors heading into mid-2019. The company had established itself as a leader in log management, security information and event management (SIEM), and operational intelligence — making it relevant not just to equity investors but to the DevOps and security engineering communities who use its products daily.

The broader market backdrop at the time was shaped by a debate between value and growth investing. As Vilas Fund noted in its Q1 2019 investor letter:

“Since 2006, value stocks (IVE vs IVW) have underperformed 11 of the 13 calendar years and when they beat growth, it wasn’t by much. Cumulatively, through this week, it has been a 122% differential (up 52% for value vs up 174% for growth). This appears to be the longest and most severe drought for value investors since data collection began… We believe the value part of the global equity market is the only place to earn solid risk adjusted returns and we believe those returns will be higher than normal.”

— Vilas Fund, Q1 2019 Investor Letter

Splunk, as a high-growth technology company, sat firmly in the growth camp — making hedge fund positioning in SPLK a useful signal for how sophisticated money managers viewed the software sector’s continued expansion.

Hedge Fund Activity in Splunk Inc (NASDAQ: SPLK) — Q1 2019

According to data tracked by Insider Monkey as of the end of Q1 2019, a total of 29 hedge funds held long positions in Splunk Inc. This represented a 16% decline from the prior quarter, and was also down from the 36 hedge funds that held bullish positions in SPLK a full year earlier.

Despite the overall drop in the number of funds holding the stock, certain managers were actively building or initiating positions — a signal that conviction among remaining holders may have increased even as smaller or more opportunistic funds exited.

Largest Holders at End of Q1 2019

• Two Sigma Advisors held the largest stake, valued at approximately $81.8 million.
• Citadel Investment Group held the second-largest position, worth approximately $48 million.
• Arrowstreet Capital, D E Shaw, and Polar Capital were also noted as significant holders with meaningful portfolio allocations to SPLK.

Notable New Positions Initiated in Q1 2019

Several prominent fund managers initiated brand-new positions in Splunk during the quarter, which can be a stronger buy signal than existing holders simply maintaining their stakes:

• Duquesne Capital (managed by Stanley Druckenmiller) initiated a position worth approximately $14.4 million.
• Moore Global Investments (managed by Louis Bacon) initiated a position worth approximately $11.8 million.
• ZWEIG DIMENNA PARTNERS (Joe DiMenna), Partner Fund Management (Christopher James), and Maplelane Capital (Leon Shaulov) also opened new positions in SPLK during the quarter.

The entry of high-profile managers like Stanley Druckenmiller — widely regarded as one of the most successful macro investors of his generation — into a position is typically watched closely by market observers as an indication of directional conviction.

How Splunk Compared to Peers by Market Cap

For context, analysts at the time also tracked hedge fund sentiment in stocks with comparable market capitalizations to Splunk, including:

• Freeport-McMoRan Inc. (NYSE: FCX) — copper and gold mining
• Cerner Corporation (NASDAQ: CERN) — healthcare IT
• International Paper Company (NYSE: IP) — paper and packaging
• Liberty Global plc (NASDAQ: LBTYA) — international cable and broadband

Comparing institutional sentiment across same-cap peers helps investors evaluate whether interest in a specific stock is sector-driven or reflects company-specific conviction.

What This Means for the DevOps and Security Community

For engineers and architects who evaluate Splunk as a platform rather than an investment, institutional sentiment data still carries indirect relevance. A company with sustained and growing hedge fund backing typically has the financial runway to continue R&D investment, maintain enterprise sales teams, and develop integrations with adjacent platforms — all of which affect the product roadmap.

As of 2019, Splunk’s core product lines included:

• Splunk Enterprise — on-premises machine data indexing and search
• Splunk Cloud — SaaS-delivered version of the platform
• Splunk Enterprise Security (ES) — SIEM solution built on the Splunk platform
• Splunk IT Service Intelligence (ITSI) — AIOps and service monitoring

Strong institutional confidence at the time correlated with Splunk’s ongoing transition from a perpetual license model to a cloud subscription model — a shift that compressed near-term revenue recognition but signaled long-term recurring revenue growth, a pattern institutional investors were familiar with from other SaaS transitions.

Historical Note

This analysis reflects publicly available data from Q1 2019. Splunk’s institutional ownership profile changed substantially in the years that followed. In September 2023, Cisco Systems announced its acquisition of Splunk for approximately $28 billion, a deal that closed in March 2024. Splunk (SPLK) no longer trades as an independent public company.

For current information on Splunk’s product direction and enterprise positioning, refer to Cisco’s official Splunk documentation and press releases.

Adoption of containerization technologies like Docker and Kubernetes has grown rapidly across the industry, and understanding both the benefits and the risks is essential for any DevOps team looking to move workloads into containers successfully.

Why Use Containers?

Containers change the fundamental model of software delivery. A container image bundles application code, runtime, libraries, and configuration into a single, portable artifact. Developers build the image once, test it, validate it, and then deploy it — knowing that what runs in production is byte-for-byte identical to what was tested.

This model provides several concrete advantages over traditional virtual machine (VM)-based deployments:

• Lower resource overhead: Containers share the host operating system kernel rather than running a full guest OS, so they consume significantly less CPU, memory, and storage than VMs. This allows more workloads to run on the same underlying hardware.
• Portability: A container image runs the same way on a developer’s laptop, a CI server, and a cloud provider’s managed Kubernetes cluster — eliminating the “works on my machine” problem.
• Consistent delivery pipeline: The same container image can be used at every stage of the pipeline — build, test, staging, and production — removing environment-specific surprises.
• Faster deployments and scaling: Containers start in seconds, not minutes, and container orchestrators like Kubernetes can spin up or tear down replicas automatically in response to load.
• Support for modernization: Teams can incrementally containerize legacy monolithic applications while simultaneously building new cloud-native microservices alongside them.

These qualities make containers especially valuable in hybrid and multi-cloud environments, where workloads need to move across different infrastructure providers without requiring application changes.

Containers Add Value — But Also Introduce Risk

It is important to be clear about what containers do not solve. They eliminate environment-drift problems, but they do not make software immune to bugs, outages, or security vulnerabilities.

Security Vulnerabilities in Base Images

Container images are built on top of base images — typically minimal Linux distributions — and those base images frequently contain unpatched vulnerabilities. Security research from Snyk has found that widely used public Docker images commonly carry dozens of known vulnerabilities, particularly when they have not been recently updated. Any container built on an outdated base image inherits all of those vulnerabilities.

Practical mitigations include:

• Regularly scanning images with tools such as Trivy, Snyk Container, or AWS ECR image scanning before pushing to a registry.
• Rebuilding and republishing images whenever base image updates are available.
• Using minimal base images (e.g., Alpine or distroless images) to reduce the attack surface.
• Enforcing image provenance policies so that only images from trusted, scanned registries can be deployed.

The Uniformity Risk

Containers solve the mixed-environment problem by ensuring identical software runs everywhere. This is both their greatest strength and a significant risk amplifier. If a vulnerability or misconfiguration is baked into an image, every replica instantiated from that image is equally vulnerable. An exploit that works against one container works against all of them simultaneously.

This makes rigorous image validation before deployment — not after — essential. Vulnerability scanning, image signing, and admission controllers in Kubernetes (such as OPA/Gatekeeper or Kyverno) can help enforce policy gates that prevent vulnerable images from reaching production.

The Cost of Downtime

Application outages carry real economic impact regardless of whether the underlying infrastructure uses containers or VMs. Organizations should not assume that containerization alone reduces their operational risk; it shifts the risk profile rather than eliminating it. Proper testing, observability, and incident response processes remain essential.

A Better Approach to Testing Containerized Applications

Moving to containers requires a corresponding evolution in how QA and testing are done. Simply lifting an existing testing approach and applying it to containerized apps is insufficient.

• Test under production-like conditions: Containers can behave differently depending on CPU limits, memory constraints, network policies, and storage configurations. QA environments should mirror production resource settings as closely as possible.
• Integrate security scanning into CI/CD: Vulnerability scanning should happen automatically at image build time, not as a one-off manual check. Block pipeline progression if critical vulnerabilities are detected.
• Test network behavior: Microservices running in containers communicate over the network. Test for latency, timeouts, partial failures, and retry logic — not just happy-path flows.
• Use chaos engineering: Tools like Chaos Monkey or Litmus can deliberately introduce failures (container crashes, network partitions) to verify that the system degrades gracefully and recovers correctly.
• Validate container orchestration configuration: Kubernetes manifests, Helm charts, resource quotas, and network policies are all part of the deployable artifact. Lint and test them as part of the pipeline.

Key Practices for Successfully Running Containers in Production

Beyond testing, several operational practices distinguish teams that run containers reliably in production from those that struggle:

• Run containers as non-root: Avoid running processes inside containers as the root user. Use a dedicated, low-privilege user in your Dockerfile.
• Set resource limits: Always define CPU and memory requests and limits in Kubernetes pod specs to prevent a single noisy container from starving others on the same node.
• Use health checks: Define liveness and readiness probes so that the orchestrator knows when to restart a failed container or stop routing traffic to one that is not yet ready.
• Store configuration externally: Use environment variables, ConfigMaps, or secrets management tools (such as HashiCorp Vault or Kubernetes Secrets) rather than baking configuration into images.
• Implement centralized logging and monitoring: Container logs are ephemeral. Ship them to a centralized system (e.g., ELK stack, Datadog, or CloudWatch) and instrument applications with metrics and distributed tracing.
• Maintain an image registry hygiene policy: Delete old, unused image tags regularly. Old images are security liabilities and inflate storage costs.

Containers Are Powerful — But Not a Silver Bullet

Containers genuinely improve developer productivity, deployment consistency, and infrastructure efficiency. They are one of the most impactful operational shifts a software team can make. However, they introduce their own failure modes and security considerations that teams must plan for explicitly.

The teams that get the most value from containers are those that treat image security, testing fidelity, and orchestration configuration with the same rigor they apply to application code. Containers used thoughtfully — with proper scanning, testing, resource management, and observability — enable faster, safer software delivery. Containers used carelessly can propagate vulnerabilities and misconfigurations at scale.

Invest the time to understand the risks, build the right tooling into your pipeline, and containers will become one of the most reliable parts of your infrastructure.

This guide walks through the core differences between on-premises and cloud deployment models, the three primary cloud deployment scenarios, the application deployment evaluation cycle, and ten concrete best practices you can apply immediately.

Cloud vs. On-Premises Application Deployment

There are fundamental differences between on-premises and cloud deployment that affect cost, speed, risk, and regulatory compliance. Treating a cloud migration as a straightforward lift-and-shift without understanding these differences can lead to unexpected costs and security exposures.

Direct Data Access

On-premises applications have low-latency, direct access to all data stored on local servers. Cloud applications must connect across a network to reach on-premises data, or must operate entirely on data that originates in the cloud. Moving large volumes of data between on-premises and cloud environments increases costs and can trigger regulatory concerns around data residency and sovereignty.

Risk Profile

Cloud deployments reduce upfront capital risk. If a project fails, you lose developer time but not hardware investment. On-premises deployments typically involve purchasing servers, software licenses, and expanding data center capacity — along with the ongoing costs of power and cooling. Cloud projects carry a different risk: runaway operational spending if resource usage is not governed carefully.

Deployment Speed

Provisioning a virtual machine on a major cloud provider such as AWS, Azure, or Google Cloud typically takes minutes. Ordering, racking, and configuring physical hardware on-premises can take days or weeks. This difference in provisioning speed is a decisive advantage for teams that need to iterate quickly or handle unpredictable demand spikes.

Cost Model

Cloud costs are metered by usage. Applications that run continuously, process large data volumes, or require significant memory can generate substantial monthly bills. On-premises infrastructure, once purchased and operational, does not carry per-use charges. The right choice depends on your workload profile: bursty, short-lived workloads often favour the cloud, while steady, high-utilization workloads may be cheaper on-premises.

Regulatory Constraints

Some industries — finance, healthcare, and government in particular — face regulations that restrict where data can reside or how it can be transmitted. In these cases, moving workloads to the public cloud may not be permitted, and on-premises or private cloud deployment is required.

Cloud Application Deployment: Three Key Models

When deploying to the cloud, you will generally choose between three service models. Each trades off control for managed convenience in a different way.

IaaS (Infrastructure as a Service)

IaaS provides virtualized compute, storage, and networking. The provider manages the physical hardware; you manage everything above the hypervisor — the operating system, middleware, runtime, databases, and the application itself. IaaS gives you the most control and flexibility, but also the most responsibility. Examples include Amazon EC2, Azure Virtual Machines, and Google Compute Engine.

Best suited for: Teams that need fine-grained control over the environment, have existing infrastructure automation (Terraform, Ansible, etc.), or are migrating workloads that cannot easily be refactored.

PaaS (Platform as a Service)

PaaS provides a managed runtime environment. The provider handles the OS, middleware, patching, and scaling infrastructure. You supply the application code and data. PaaS accelerates development by removing infrastructure management overhead, but constrains your choice of language runtimes, frameworks, and databases to those the provider supports. Examples include Heroku, Google App Engine, and Azure App Service.

Best suited for: Development teams that want to focus on application logic rather than infrastructure, and whose technology choices align with what the platform offers.

SaaS (Software as a Service)

SaaS delivers a complete, managed application over the internet. The vendor operates all infrastructure and the application itself; you configure and use it. Customization is possible — for example, Salesforce allows workflow and UI customization — but fundamental architecture changes are not available to the customer.

Best suited for: Commodity business functions (CRM, HR, email) where differentiation comes from configuration and data, not from the application code itself.

Regardless of model, most applications require a database. Major cloud providers offer a broad selection of relational (SQL) and non-relational (NoSQL) managed database services, as well as built-in horizontal and vertical scaling and load balancing to handle traffic spikes.

The Application Deployment Evaluation Cycle

Effective deployment is not a one-time event — it is a lifecycle process. Before deploying any application, IT and DevOps managers should define clear answers to three recurring questions:

• When should the application be installed or initially deployed? Define target environments, prerequisites, and approval gates.
• When and how should the application be updated? Define update frequency, testing requirements, and change windows.
• When should the application be retired or removed? Define end-of-life criteria, data retention obligations, and decommission procedures.

Scheduling these lifecycle events explicitly allows updates and removals to be automated and removes reliance on manual action by end users or individual operators. Tools such as Microsoft Endpoint Configuration Manager (formerly System Center Configuration Manager, or SCCM) implement this pattern directly: they poll for new deployment policies, execute installs or updates on a defined schedule, and report compliance status back to a central console.

Application Deployment Best Practices Checklist

The following ten practices apply across on-premises and cloud environments. Some are universal fundamentals; others become especially important at scale or in regulated industries.

1. Keep It Simple

Minimise the complexity of your installation structure. Deploy only the libraries and dependencies your application actually uses. Avoid scattering files across unexpected filesystem locations. A smaller, cleaner deployment footprint is faster to install, easier to audit, and simpler to troubleshoot.

2. Maintain Consistency Across Environments

Use the same deployment mechanism and configuration structure across development, staging, and production. Environment-specific values (database connection strings, API keys, feature flags) should be injected via environment variables or a secrets manager — not baked into separate deployment scripts. Inconsistency between environments is one of the leading causes of "works on my machine" failures in production.

3. Keep Environments Clean and Organised

Remove obsolete files, deprecated configuration, and old application versions before deploying a new release. Treating each deployment as a clean install — rather than an incremental patch on top of accumulated cruft — reduces the risk of legacy files interfering with new behaviour and makes the deployment state predictable.

4. Embed Security Throughout the Deployment Process

Security should not be a post-deployment review item. Scan container images and dependencies for known vulnerabilities before deploying. Enforce least-privilege access to deployment pipelines and production environments. Regularly test application perimeters with static analysis (SAST), dynamic analysis (DAST), or penetration testing. The cost of finding a vulnerability before deployment is a fraction of the cost of a production breach.

5. Define a Rollback Strategy Before You Deploy

Every deployment should have a documented, tested rollback plan. Whether that means maintaining a previous container image tag, using blue-green deployments, or keeping a database migration revert script ready, you must know how to recover quickly if a release introduces a critical defect. Test your rollback process in a non-production environment — do not discover it is broken during an incident.

6. Adopt Agile and Continuous Delivery Principles

Frequent, small releases are safer than infrequent, large ones. Smaller changesets are easier to test, easier to review, and easier to roll back. Continuous delivery pipelines — where every merged commit is automatically built, tested, and made ready for deployment — enable teams to release on demand rather than on a fixed schedule, reducing the pressure and risk associated with any single release.

7. Use a Deployment Checklist

Checklists are a proven mechanism for reducing errors under time pressure. Create and maintain a deployment checklist that covers pre-deployment checks (dependency versions, database migrations pending, monitoring alerts silenced), deployment steps, and post-deployment verification (smoke tests, health checks, error rate baseline). Experienced teams still use checklists because critical steps are easy to skip when you are focused on executing quickly.

8. Use a Continuous Integration Server

A CI server — Jenkins, GitHub Actions, GitLab CI, CircleCI, and others — automatically builds and tests code every time a developer commits a change. This catches integration problems early, before code reaches a shared environment. CI servers are sometimes called "build servers" because their primary job is to assemble all contributions into a single verified artifact. Without CI, integration problems accumulate silently until they surface as hard-to-diagnose failures in production.

9. Automate as Much as Possible

Manual deployment steps introduce human error and create bottlenecks. Automate environment provisioning (Terraform, Pulumi), configuration management (Ansible, Chef, Puppet), and the deployment pipeline itself. Automation also produces an auditable record of every change: when it was made, by whom, and what the outcome was. In regulated environments, this audit trail is often a compliance requirement.

10. Choose the Right Tools for the Job

There is no single deployment tool that is optimal for every organisation or workload. Evaluate tools against your specific requirements: the target environment (bare metal, VM, container, serverless), team expertise, budget, and integration requirements with your existing pipeline. Common categories include:

• Container orchestration: Kubernetes, Amazon ECS
• Configuration management: Ansible, Chef, Puppet, SaltStack
• Infrastructure as code: Terraform, AWS CloudFormation, Pulumi
• CI/CD platforms: Jenkins, GitHub Actions, GitLab CI/CD, CircleCI, Argo CD
• Endpoint management: Microsoft Endpoint Configuration Manager, Jamf

Invest time in evaluating tools before committing to them at scale — switching deployment tooling after a team has built deep expertise and custom integrations is costly.

Summary

Effective application deployment requires matching your strategy and tooling to your target environment, understanding the trade-offs between on-premises and cloud models, managing the full application lifecycle deliberately, and applying consistent, automated, security-conscious practices at every stage. The ten practices above provide a solid foundation regardless of whether you are deploying to a bare-metal server, a managed Kubernetes cluster, or a SaaS platform.

Benefits of DevOps

Organisations that adopt DevOps practices correctly report improvements across several areas:

• Improved cross-team communication. DevOps breaks down silos between development, QA, security, and operations departments, encouraging shared ownership of product quality.
• Faster, more reliable releases. Continuous integration and continuous delivery (CI/CD) pipelines automate build, test, and deployment steps, reducing the time from code commit to production.
• Higher software quality. Automated testing and continuous feedback loops catch defects earlier in the development cycle, when they are cheapest to fix.
• Stronger security posture. Integrating security practices into the pipeline (often called DevSecOps) makes security a shared responsibility rather than a last-minute gate.
• Faster response to feedback. Monitoring, logging, and alerting tools give teams real-time visibility into how software performs in production, enabling rapid iteration.
• Greater automation. Repetitive manual tasks — provisioning infrastructure, running tests, deploying code — are automated, freeing engineers to focus on higher-value work.

Core Principles Behind DevOps

DevOps is often summarised around three interconnected pillars: people, processes, and tools.

People

DevOps requires a cultural shift before it requires any tooling change. Developers, operations engineers, QA testers, and security specialists must collaborate continuously rather than hand work off in isolated stages. This shared responsibility model means that everyone on the team has a stake in the reliability and quality of what ships to production.

Processes

DevOps streamlines the workflows that move software from idea to production. This includes adopting agile planning practices, defining clear definition-of-done criteria, establishing CI/CD pipelines, and conducting blameless post-mortems when incidents occur. The aim is to identify and eliminate bottlenecks at every stage so that work flows smoothly across teams.

Tools

Tooling supports — but does not replace — the cultural and process changes. A typical DevOps toolchain spans version control (e.g., Git), CI/CD platforms (e.g., Jenkins, GitHub Actions, GitLab CI), infrastructure-as-code tools (e.g., Terraform, Ansible), container orchestration (e.g., Kubernetes), and observability platforms (e.g., Prometheus, Grafana). The right tools are those that fit your team’s existing workflows, not those that are most popular in the industry.

Is DevOps Right for Your Business?

DevOps is not a universal solution, and adopting it without organisational readiness can create as many problems as it solves. Before committing, consider the following questions:

• Is leadership aligned? DevOps transformation requires executive sponsorship. Without it, teams revert to old habits under deadline pressure.
• Are your teams buried in manual work? If engineers are spending the majority of their time on manual deployments, server maintenance, or firefighting incidents, the foundation for DevOps is not yet in place. Address the immediate operational debt first.
• Do you have basic automation in place? Teams that already run automated tests, use source control consistently, and have some deployment automation are far better positioned to benefit from a full DevOps adoption.
• Can you define clear success metrics? Common DevOps metrics include deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate. Without agreed metrics, it is hard to demonstrate value or course-correct.
• Are all stakeholders willing to change? DevOps is a collaborative endeavour. If key departments are resistant, adoption will stall.

If your team has some automation in place, has reduced routine operational bottlenecks, and leadership is committed to cultural change, DevOps adoption is likely to deliver meaningful improvements. If you are still dealing with frequent unplanned downtime, no version control discipline, or siloed teams with no shared goals, resolve those foundational issues first.

How to Implement DevOps

Because every organisation is different, there is no single DevOps implementation blueprint. However, a structured approach helps teams avoid common pitfalls.

1. Build a Shared Understanding

Start with training and alignment. Teams should understand DevOps principles, the CALMS framework (Culture, Automation, Lean, Measurement, Sharing), and the specific outcomes the organisation is targeting. Training programmes such as the DevOps Foundation certification (offered by organisations like the DevOps Institute) provide a structured introduction to these concepts.

2. Embed Security Early (DevSecOps)

Security should be integrated into the CI/CD pipeline from the start rather than bolted on at the end. This means adding static analysis, dependency scanning, and secrets detection to automated pipelines. DevSecOps training extends DevOps principles specifically into the security domain.

3. Modernise Your Testing Practices

Automated testing is the safety net that makes rapid deployment possible. Investing in unit, integration, and end-to-end test coverage — and understanding which tests belong in which stage of the pipeline — is essential to sustainable DevOps. This is the focus of disciplines like DevOps Test Engineering.

4. Start Small, Then Scale

Choose a single team or product to pilot DevOps practices. Establish baseline metrics, implement changes incrementally, and gather feedback before rolling out to other teams. Attempting a company-wide transformation simultaneously is a common failure mode. Lessons learned from the pilot team — including what tooling choices work and which process changes generate resistance — are invaluable when scaling.

5. Choose Tools That Fit, Not Tools That Are Fashionable

DevOps does not mandate specific tools. Project management platforms, CI/CD systems, monitoring stacks, and collaboration tools should be selected based on your team’s existing skills and your product’s requirements. The best tool is the one your team will actually use consistently.

Summary

DevOps is a meaningful investment for organisations that are ready for it. When adopted with genuine cultural commitment, clear metrics, and a phased rollout, it delivers faster releases, higher quality, and more resilient systems. If your teams are still overwhelmed by manual operations or lack basic automation, build those foundations first. DevOps amplifies good engineering practices — it cannot substitute for them.

One of the most effective ways modern teams cut that cycle time is by adopting continuous development practices. This article explains what continuous development is, breaks down its three core components, and offers practical guidance on getting started.

What Is Continuous Development?

“Continuous development” is an umbrella term closely associated with Agile and DevOps methodologies. It describes an iterative approach to building and releasing software in which work is integrated, tested, and shipped in small, frequent increments rather than in large, infrequent releases.

The term encompasses three related but distinct practices: continuous integration (CI), continuous delivery (CD), and continuous deployment (CD). Understanding the difference between them is essential before choosing tooling or designing a pipeline.

The Three Pillars of Continuous Development

1. Continuous Integration (CI)

Continuous integration is the practice of merging developer changes into a shared main branch frequently — ideally multiple times per day. Each merge triggers an automated build and a suite of automated tests.

The core goal is to catch integration problems early. When every developer merges small changes regularly, conflicts and regressions surface quickly and in a limited scope, making them far cheaper to fix than defects discovered weeks later. CI does not automatically release anything to customers; it simply validates that the codebase stays in a releasable state.

• Key inputs: source code commits, feature branches
• Key outputs: build artifacts, test reports, pass/fail status
• Common tools: Jenkins, GitHub Actions, GitLab CI, CircleCI

2. Continuous Delivery (CD)

Continuous delivery extends CI by automating the steps required to prepare a release — packaging, environment configuration, integration testing, and staging deployment — so that the software is always in a state that can be released to production. The actual push to production, however, still requires a deliberate human action (a button click, a merge to a release branch, or an explicit approval).

Because changes are released in small batches, issues are easier to isolate and roll back. Teams can release on a predictable, low-risk schedule rather than treating each release as a major event.

• Key inputs: validated build artifacts from CI
• Key outputs: a tested, staged release ready for one-click production promotion
• Distinguishing trait: a human approves the final production push

3. Continuous Deployment

Continuous deployment takes the automation one step further: every change that passes all automated pipeline stages is deployed to production automatically, with no human intervention required. A change is held back only if a test or quality gate fails.

This eliminates the concept of a fixed “release day.” Teams can ship multiple times per day, gather real user feedback almost immediately, and iterate rapidly. The trade-off is that your automated test coverage must be comprehensive enough to act as the sole quality gate — a significant investment.

• Key inputs: validated build artifacts from CI
• Key outputs: live production releases, triggered automatically
• Distinguishing trait: no human approval step before production

CI vs. CD at a Glance

Why Continuous Development Matters for Small Teams

The efficiency gains from continuous development are proportionally larger for smaller teams. When a two- or three-person team replaces manual integration and deployment steps with an automated pipeline, they recover hours each week that would otherwise be spent on error-prone, repetitive tasks. That time compounds quickly into faster feature delivery, faster bug fixes, and a better product.

Beyond speed, continuous development practices reduce risk. Smaller, more frequent releases mean that if something goes wrong, the blast radius is limited and the change causing the problem is easy to identify.

How to Get Started with Continuous Development

If your team is new to CI/CD, start with these foundational steps:

1. Start with CI before CD. Get every developer merging to a shared branch at least once a day, and make sure an automated build and a basic test suite run on every commit. This alone will surface integration problems far earlier.
2. Write tests before adding automation. A pipeline is only as reliable as its tests. Invest in unit tests, integration tests, and at least smoke-level end-to-end tests before automating deployments.
3. Automate your build and packaging steps. Eliminate manual steps in creating deployable artifacts. Reproducible builds are a prerequisite for reliable CD.
4. Add a staging environment. Deploy automatically to a staging or pre-production environment first. This gives you a production-like target for integration tests without touching live users.
5. Choose continuous delivery before continuous deployment. Most teams benefit from the manual approval gate initially. Shift to fully automated deployment only once your test coverage and monitoring are mature.
6. Use containerization to reduce environment drift. Tools like Docker make it easier to ensure that what passes in the CI environment is exactly what gets deployed to production, eliminating the classic “works on my machine” problem.

Choosing a CI/CD Tool

The market for CI/CD tooling is broad. When evaluating options, consider the following factors:

• Integration with your version control system — native support for GitHub, GitLab, or Bitbucket simplifies setup.
• Pipeline-as-code support — storing pipeline configuration in version control (e.g., a Jenkinsfile or .github/workflows YAML) keeps your process auditable and reproducible.
• Container and orchestration support — if you use Docker or Kubernetes, confirm the tool integrates cleanly with those workflows.
• Scalability and pricing — open-source self-hosted tools (Jenkins, GitLab CI) offer flexibility; hosted SaaS tools (GitHub Actions, CircleCI, Buddy) reduce operational overhead.
• Notification and observability hooks — you need to know immediately when a build or deployment fails.

Continuous Development in Context: Agile and DevOps

Continuous development did not emerge in isolation. It builds directly on the principles of Agile software development, which were codified in the Agile Manifesto in 2001, and on the DevOps movement, which extended Agile principles into operations and delivery.

Where Agile introduced iterative development and frequent feedback loops at the team level, DevOps and continuous development extended those loops all the way to production. The result is that organizations have progressively moved from doing basic CI, to adding continuous delivery, to fully embracing continuous deployment — each step reducing the gap between writing code and delivering value to users.

Summary

Continuous development — encompassing CI, continuous delivery, and continuous deployment — is the foundation of efficient, low-risk software delivery. It is not a single tool or a single step; it is a set of practices that, when layered together, allow teams of any size to ship better software faster. Start with continuous integration, build your test coverage, automate your pipeline incrementally, and your team will spend far less time fighting integration fires and far more time building features your users actually want.

San Francisco conference to offer 135 sessions presented by experts on current digital transformation and software delivery topics

CloudBees, the lead organizing sponsor, the Jenkins community and the Jenkins X community, announced today availability of the agenda preview for DevOps World | Jenkins World 2019 San Francisco. This year’s theme is “Deliver” and the conference will educate attendees on the value that digital transformation has in delivering software more efficiently, more quickly and with higher quality.

The conference is taking place in two locations this year:

• August 12 – 15, Moscone West, San Francisco
• December 2 – 5, Lisbon Congress Center, Lisbon, Portugal
  (Agenda preview available in August)

The conferences will be the largest gathering of IT executives, DevOps practitioners and Jenkins users in the world, with DevOps and continuous delivery experts presenting sessions. There are many opportunities for attendees to learn, network and explore DevOps and many software delivery topics.

This year’s conference in San Francisco includes a total of 135 sessions covering diverse DevOps, software delivery and Jenkins topics. In San Francisco, sessions will be conducted by practitioners from organizations such as: Adobe, Capital One, CloudBees, CVS, Eli Lilly, Fugue, Google, IBM, Microsoft, SAP Labs and Verizon. Sessions will cover topics ranging from cloud native and Kubernetes, to pipeline development and management, CI/CD topics, release management, data and reporting, DevOps and security/compliance.

• Conference sessions
  A sampling of sessions from the DevOps World | Jenkins World San Francisco agenda includes:
  • From Zero to DevOps Superhero: The Container Edition
    Jessica Dean, Microsoft
  • Creating a CI/CD Pipeline for Your Shared Libraries
    Roderick Randolph, Capital One
  • Cloud Native CI/CD with Jenkins X and Tekton
    James Rawlings, CloudBees
  • Using Chaos Engineering for Resilient Cloud Infrastructure Security
    Josh Stella, Fugue
  • Jenkins X Optimized for Google Cloud Platform
    Craig Barber, Google
  • Continuous Delivery Directly to the Car
    Rafal Kowalski, Grape Up
  • 10 Attributes of the DevSecOps Elite
    Derek Weeks, Sonatype and Hasan Yasar, Carnegie Mellon Software Engineering Institute
  • Can Jenkins be the Engine of Mobile DevOps?
    Shashikant Jagtop, XCTEQ Limited

“DevOps World | Jenkins World provides a broad range of software delivery topics. It is the most comprehensive event for IT practitioners around the globe to learn how to accelerate software delivery and decrease time to value,” said Sacha Labourey, CEO and co-founder, CloudBees.

US Sponsors

Sonatype, Delphix, IBM, KPMG, MacStadium, Sauce Labs, GitHub, Go2Group, Harness, LogDNA, Micro Focus, NowSecure, Perfecto, Plutora, SmartBear, SonarSource, WhiteSource Software, Applitools, CheckMarx, CircleCI, Codefresh, Column Technologies, Compuware, Datical, GitLab, iTMethods, LaunchDarkly, Neotys, Perforce, Synopsys, Sysdig, Twistlock, XebiaLabs, XpandIT (XRAY), GitKraken by Axosoft, Gradle, Honeycomb, IncrediBuild, Instana, OverOps, Scalyr, Grape Up and Verifa.

About CloudBees

CloudBees is powering the continuous economy by offering the world’s first end-to-end continuous software delivery management system (SDM). For millions of developers and product teams driving innovation for businesses large or small, SDM builds on continuous integration (CI) and continuous delivery (CD) to enable all functions and teams within and around the software delivery organization to best work together to amplify value creation.

CloudBees is the CI, CD and application release orchestration (ARO) powerhouse, built on the commercial success of its products as well as its open source leadership. CloudBees is the largest contributor to Jenkins and Jenkins X, and a founding member of the Continuous Delivery Foundation (CDF). From startups with full-stack developers practicing NoOps to large Fortune 100 companies, CloudBees enables all software-driven organizations to intelligently deploy the right capabilities at the right time.

Over 3,500 of the world’s best-known brands and over 50% of the Fortune 500, invest in CloudBees because of its ability to work across any cloud, in any development environment and to balance corporate governance and control with developer flexibility and freedom. CloudBees is home to the world’s leading DevOps experts, helping thousands of companies harness the power of “continuous everything” and putting them on the fastest path from great idea, to great software, to great business value.

Backed by Matrix Partners, Lightspeed Venture Partners, Verizon Ventures, Delta-v Capital, Golub Capital and Unusual Ventures, CloudBees was founded in 2010 by former JBoss CTO Sacha Labourey and an elite team of continuous integration, continuous delivery and DevOps professionals.

Getting the challenge of integrating a security mindset into DevOps practices right will become even more important as new markets of IT such as the Internet of Things spin up, writes Andrew Slavkovic of CyberArk

In some companies, security teams make it impossible for DevOps to deliver any new code until they fix the security issues in it

Chief information security officers (CISOs) are under growing pressures to find ways to overcome the consistent barrage of new cyberthreats.

Part of the strain locally is being driven by the introduction of new government policies, such as open banking.  When it’s phased in from July 2019, the way banks handle customer data and how they approach security and privacy will be scrutinised by the public.

The increased expectation from customers across all industries to access secure and convenient services at their fingertips has given rise to an application development boom within organisations. And managing the fast-paced and ideas-driven DevOps community is one area of business that CISOs collectively find challenging, particularly when it comes to setting and enforcing secure systems and safe practices.

A relatively new discipline propelled by speed and ingenuity, DevOps emerged without in-built security. Furthermore, those in DevOps tend to push back on security requests due to the belief that such controls will limit their ability to innovate quickly.

However, without securing the development pipeline, the risks are high. In fact, CISOs need to rethink their approach to securing DevOps.

As Khadir Fayaz, vice president of global security strategy, engineering and architecture, at Pearson puts it, “All it takes is one developer making an error to result in access keys being checked into code repositories.”

DevOps tools can be compromised to take over applications or an organisation’s entire cloud network.

An example of this was when the details of 50,000 drivers of ride-sharing service Uber were stolen after hackers accessed a private GitHub repository used by Uber software engineers, extracting valid credentials. These credentials provided access to an Amazon Web Service (AWS) account containing the data which was stolen.

A recent study from researchers at North Carolina State University revealed that over 100,000 GitHub repositories contain exposed authentication secrets, including 85,311 unique Google API keys, 37,781 unique RSA Private Keys and 47,814 unique Google OAuth IDs. The researchers also found that thousands more repositories are leaking new, unique secrets every day.

To combat the challenge, CISOs must tailor their management approach to the specific needs of the DevOps community. Reshaping how security policies and procedures are enforced, to limit and modify each user’s access to DevOps tools and applications, will demonstrate to developers how restricting access to privileged credentials can help protect an organisation from malicious attacks.

Showing – instead of telling – the DevOps community why managing secrets and privileged credentials is important, and how the practice can be successfully built into application development, helps demystify any misconceptions about security.

“Instead of having developers look at policies, standards and frameworks to figure out what they should do, build consumable security components that are easily adopted. Think in terms of, ‘How do I allow my developers to do the right thing without stifling this agility?’” suggests Fred Gibbins, senior vice president and chief information security oOfficer at American Express.

One example of this is breaking the build, a strategy that has been used by companies like Starbucks. With this approach, security teams make it impossible for DevOps to deliver any new code until they fix the security issues in it. This ensures risk scoring is built into automation tools, so that if the risk exceeds a certain threshold, the build automatically breaks, and developers need to fix it before they can deliver.

CISOs’ that tailor their approach to help DevOps teams understand the need for security by design illustrate their understanding of the DevOps space, how the community works, and what their needs are. CISOs who have made this step have expanded traditional security programs, such as monitoring privileged access across the tool chain, to the DevOps community with little protest.

At the same time, transitioning to an attacking mindset will ensure CISOs are more likely to be able to detect an incident or potential incident before it happens. This proactive approach to security, however, requires a shift from legacy approaches to security.

In the past, CISOs have held a reactive mentality towards security, relying heavily on people being compliant to certain policy documents and having a centralised security management system for all company secrets and credentials.

Introducing security by design and encouraging the cross pollination of security and DevOps teams, should help to remove any feeling that security is authoritarian and instead foster a collaborative partnership between the two.

There a few ways to do this. Setting up a centre of excellence or engineering council with developers and security members who are responsible for selecting and configuring tools, enhancing the developer experience, and promoting good engineering practices is one way.

Another is appointing security champions or community leaders to serve as resources to drive the implementation of practices across the company.

“If the overall culture doesn’t allow security to be partners with DevOps environment, security runs the risk of being a ‘check the box’ compliance exercise. Security awareness also has to be embedded in the company’s culture – especially with DevOps team members,” says Dawn Cappelli, vice president and chief information security officer at Rockwell Automation.

Getting the challenge of integrating a security mindset into DevOps practices right will become even more important as new markets of IT such as the Internet of Things spin up. Using their approach to DevOps security as a standard in the new era, CISOs will not only have to understand new technologies but also modify their method to managing security protocols and safe practices.

CISOs should never be complacent. Continual reviews of policies and governance will keep organisations on high alert about new and potential attacks.

Why Learn Jenkins?

Jenkins sits at the heart of many CI/CD workflows. Its plugin ecosystem — numbering in the hundreds — lets you integrate with source control systems like Git, testing frameworks like Selenium, container platforms like Docker, and cloud providers. Understanding Jenkins helps you automate repetitive software delivery tasks, reduce integration risk, and ship faster and more reliably.

10 Free Resources to Learn Jenkins

1. Getting Started with Jenkins — Official Documentation

The official Jenkins documentation is the most authoritative starting point. It walks you through installing Jenkins on your platform of choice, creating your first Pipeline, defining execution environments using agents and Docker, and deploying artifacts. Because it is maintained by the Jenkins project itself, it stays current with new releases and best practices. Begin here before exploring third-party tutorials.

2. Jenkins Tutorial for Beginners — Online Training Course

Several free online training platforms host beginner-friendly Jenkins courses that cover installation, job creation, pipeline authoring, and integration with external tools such as GitHub and Selenium. A good introductory course will also compare Jenkins with other CI tools (for example, Travis CI), explain how to manage users and permissions using the Role Strategy Plugin, and show you how to build freestyle and Pipeline projects side by side.

3. Jenkins Tutorial by Tutorials Point

Tutorials Point’s Jenkins guide is a concise, text-based reference aimed at software testers and developers who already understand the Software Development Life Cycle. It covers the fundamentals — installation, configuration, job scheduling, and basic pipeline concepts — without assuming prior CI experience. It is a useful companion reference when you encounter unfamiliar terminology in other tutorials.

4. Jenkins Video Tutorial Playlist — YouTube

YouTube hosts several freely available Jenkins playlist series that progress from basic setup to intermediate topics. A typical series of around fifteen to twenty videos covers deploying Jenkins on an application server such as Tomcat, using the Jenkins CLI, integrating with Git repositories, triggering builds via email or webhooks, and monitoring builds. Video walkthroughs are particularly effective for visual learners who want to see each configuration step in real time.

5. Continuous Integration with Jenkins — Written Tutorial

A number of community-authored written guides focus specifically on CI workflows with Jenkins. These typically cover installing Jenkins on a Linux distribution such as Ubuntu, configuring build triggers, setting up the Blue Ocean visual pipeline editor, and structuring multi-stage pipelines. If you are deploying Jenkins for the first time on a Linux server, a step-by-step written guide of this type is often more practical than video content because you can copy commands directly.

6. Jenkins Architecture and Pipeline Fundamentals — Video Course

Courses designed for complete beginners often start with the conceptual layer — explaining what CI is, why it matters, and how Jenkins fits into a broader DevOps toolchain — before moving into hands-on configuration. Look for content that covers Jenkins master/agent architecture, the difference between declarative and scripted Pipelines, and how to write a basic Jenkinsfile. Understanding the architecture early will save significant troubleshooting time later.

7. Jenkins: The Definitive Guide — Free eBook

Jenkins: The Definitive Guide by John Ferguson Smart is available free of charge from the Jenkins project and O’Reilly. While the book predates some modern Jenkins features such as declarative Pipelines, it remains valuable for understanding the foundational concepts: build configuration, notification strategies, distributed builds, security, and integrating Jenkins with Java and JVM-based projects. It also includes chapters on non-Java stacks including Grails, Ruby on Rails, and .NET projects. Supplement it with current official documentation for Pipeline-specific guidance.

8. Jenkins Tutorials — DevOps Learning Portals

Dedicated DevOps learning portals publish structured Jenkins learning paths that combine theory with practical exercises. Topics typically include infrastructure-as-code principles applied to Jenkins configuration, creating and structuring complex multi-stage pipelines, integrating pipelines with Git for source-triggered builds, and managing Jenkins configuration using tools like the Job DSL plugin or Jenkins Configuration as Code (JCasC). These structured paths are useful when you want a guided curriculum rather than ad hoc tutorials.

9. Step-by-Step Jenkins Installation and First Job Tutorial

Practical, task-focused tutorials that walk you through downloading the Jenkins WAR file or installing via a package manager, completing the initial setup wizard, and creating your first build job are particularly helpful for getting hands-on quickly. Once you have a working local Jenkins instance you can experiment freely, which accelerates learning significantly. Look for tutorials that also cover basic security hardening — disabling anonymous access and enabling authentication — as part of the initial setup.

10. Continuous Integration and Continuous Deployment — Microsoft Learn / Azure DevOps

Microsoft has published free DevOps learning content that covers CI/CD practices using Azure DevOps (formerly Visual Studio Team Services) alongside integration with open-source tools including Git and Jenkins. This resource is valuable for teams working in mixed environments — Microsoft Azure infrastructure combined with Jenkins pipelines — because it explains how the two ecosystems connect. It also contextualises Jenkins within broader DevOps practices rather than treating it in isolation.

Choosing the Right Starting Point

If you are entirely new to Jenkins, start with the official documentation to install a local instance, then use a structured video or written tutorial to complete your first pipeline. Once you are comfortable with basic jobs and pipelines, Jenkins: The Definitive Guide provides deeper context on distributed builds and security. For teams adopting Jenkins at scale, explore Jenkins Configuration as Code (JCasC) and shared libraries, topics covered in more advanced DevOps learning portals.

Original article sourced from analyticsindiamag.com and republished with independent editorial expansion.