A step-by-step blueprint of tools, techniques, processes, and practices (from a “20 years in the trenches” lens)

1) First, get the terms right (because teams lose months here)

In my experience, most “CD” confusion starts with language:

• Continuous Delivery means every change can be deployed safely at any time (pipeline keeps the system deployable).
• Continuous Deployment means every change that passes the pipeline automatically goes to production, often many times per day.

That distinction matters because continuous deployment is not a switch you flip—it’s the outcome of disciplined engineering, guardrails, and measurement.

2) What “continuous deployment-ready” actually looks like

When a team is truly doing continuous deployment, a few things are always true:

1. Main is always releasable (small changes, frequent merges).
2. Deployments are routine, boring, and reversible (rollbacks are standard, not heroic).
3. Risk is managed by automation (tests + progressive rollout + observability).
4. Performance is measured (you can prove if you’re improving or getting worse).
   DORA’s four key metrics are the simplest common language I’ve seen work across organizations: deployment frequency, lead time for changes, change failure rate, MTTR. ()

3) The DevOps skill map for continuous deployment

Here’s the skills stack I expect a DevOps engineer (or platform/SRE in many orgs) to be strong in.

A. Version control & integration discipline

• Trunk-based development (or close to it): short-lived branches, frequent merges, reduce integration hell. ()
• Pull request hygiene: review standards, small PRs, ownership
• Release-friendly practices: feature flags, backward-compatible schema changes

B. Pipeline engineering (CI as a product)

• Pipeline as code, repeatable builds, caching strategy
• Artifact immutability: “build once, promote many”
• Secret handling: ephemeral credentials, least privilege, no secrets in logs

C. Automated testing strategy (gating without slowing teams to death)

• Test pyramid thinking (unit → integration → e2e), contract testing where relevant
• Ephemeral preview environments for PR validation
• Deterministic builds + deterministic tests (flake reduction is a real skill)

D. Deployment & release engineering

• Rolling updates vs canary vs blue/green
• Health checks and readiness/liveness correctness (Kubernetes teams: this is life)
• Automated rollback strategies
• Progressive delivery + safe experimentation

Kubernetes natively supports rolling update patterns in Deployments, and the mechanics matter if you want safe automation. ()

E. GitOps & environment state management (modern default for K8s)

• GitOps basics: desired state in Git + reconciler applies it
• Tools like Argo CD / Flux for cluster delivery and drift control ()
• Understanding push vs pull deployment models, RBAC implications

F. Observability & release validation

If you can’t “see” what changed in production, continuous deployment becomes reckless.

• Metrics, logs, traces as first-class signals (not an afterthought)
• Standardized instrumentation approach (OpenTelemetry is the most broadly adopted open framework here). ()
• SLOs / error budgets: release decisions tied to reliability signals (even if lightweight at first)

G. Security & supply chain integrity (DevSecOps is not optional anymore)

• Pipeline hardening and access control (CI/CD is a juicy target). ()
• Software supply chain security: provenance, trusted builds, signing, SBOM
• Align to recognized guidance like NIST SP 800-204D for securing DevSecOps CI/CD pipelines (especially cloud-native). ()
• Understand frameworks like SLSA security levels to mature integrity guarantees over time. ()

4) Step-by-step approach to build continuous deployment (the blueprint I use)

Step 1 — Define “release policy” and a realistic deployment target

Before tools, decide:

• What is “production” (single region? multi-region? active-active?)
• Allowed risk profile (B2B internal tool ≠ consumer payments system)
• “Definition of Done” for a change to be auto-deployed
• Rollback expectations (time to rollback, blast radius limits)

Then commit to measuring with DORA metrics; otherwise “CD progress” becomes a debate, not a fact. ()

Step 2 — Make the main branch releasable (this is the real foundation)

Continuous deployment collapses if your integration model is “merge at the end”.

Practices

• Trunk-based development (short-lived branches, frequent merges) ()
• Enforce fast PR checks: lint + unit tests + minimal integration tests
• Feature flags for incomplete work (ship code dark, enable later)

Tools

• GitHub / GitLab / Bitbucket
• Feature flags: LaunchDarkly / Unleash / Split / Cloud provider equivalents (choose based on scale & governance)

Step 3 — Build once, produce immutable artifacts, and store them properly

If you rebuild the same commit for dev/stage/prod, you’ll eventually deploy something you never tested.

Practices

• One artifact per commit (immutable)
• Semantic versioning or commit-based tags
• Artifact repository becomes part of your supply chain

Tools

• Container registry: ECR / GCR / ACR / Harbor / Artifactory
• Artifact repos: Nexus / Artifactory
• SBOM generation (if you’re serious about security posture): CycloneDX tooling, Syft (ecosystem choice)

Step 4 — Engineer the CI pipeline as a product (speed + trust)

This is where many teams create slow, flaky pipelines that become bypassed.

Practices

• Pipeline as code
• Parallelize test stages
• Cache dependencies
• Fail fast; don’t run expensive tests if build already failed
• Treat flaky tests as incidents (because they destroy trust)

Tools (common choices)

• GitHub Actions / GitLab CI / Jenkins / CircleCI / Azure DevOps
• For Kubernetes-native CI: Tekton (when you want pipelines running inside clusters)

Step 5 — Standardize environments using IaC and configuration discipline

Continuous deployment dies when environments drift or are hand-patched.

Practices

• Infrastructure as Code (everything repeatable)
• Separate config from code, but keep config versioned
• Use the same deployment mechanism across environments

Tools

• Terraform / OpenTofu / Pulumi / CloudFormation / Bicep
• Config delivery: Helm / Kustomize
• Secrets: Vault / cloud secrets managers + workload identity patterns

Step 6 — Choose a deployment control plane: Push CD or GitOps CD

This is a key architecture decision:

Option A: Push-based CD
CI system pushes deployments to the target environment. Works, but can lead to access sprawl.

Option B: GitOps (common for Kubernetes)
Cluster reconciler pulls desired state from Git and applies it; Git is the source of truth. CNCF highlights the Argo CD vs Flux ecosystem as the dominant GitOps toolkit for Kubernetes. ()

My bias (from experience):
If you’re on Kubernetes and you care about auditability + drift control, GitOps is usually the cleanest operational model.

Step 7 — Implement safe rollout strategies (progressive delivery)

This is where “continuous deployment” becomes safe enough to be automatic.

Core strategies

• Rolling updates (baseline for many services) ()
• Blue/Green (fast rollback by switching traffic) ()
• Canary (small % traffic first, verify, then expand) ()
• Feature flags (deploy code, control exposure without redeploy)

Progressive delivery is widely recommended because it reduces blast radius and makes rollbacks routine. ()

Tools

• Kubernetes-native: Argo Rollouts (blue/green, canary) ()
• Service mesh (when needed): Istio / Linkerd for traffic shaping
• Feature flags: LaunchDarkly / Unleash, etc.

Step 8 — Observability and automated release validation (don’t deploy blind)

If you automate deployments, you must automate confidence.

Practices

• Instrument services with metrics/logs/traces
• Define “release health” signals: error rate, latency, saturation, key business KPIs
• Gate canary promotion on telemetry trends
• Keep rollback automatic for known bad states

OpenTelemetry is explicitly designed as a vendor-neutral way to generate and export telemetry (traces/metrics/logs). ()

Tools

• Metrics: Prometheus + Grafana (or cloud-native equivalents)
• Tracing: Jaeger / Tempo / vendor backends
• Logging: Loki / ELK / cloud logging
• OTel Collector for consistent enrichment and exporting ()

Step 9 — Secure the pipeline and the supply chain (because attackers love CI/CD)

I’ve seen orgs spend millions on app security and forget the pipeline—until a breach proves CI/CD is part of prod.

Practices

• Least privilege for CI runners, short-lived credentials, isolated build environments
• Dependency scanning, container scanning, IaC scanning
• Artifact signing and provenance (especially for regulated/high-risk systems)
• Protect against pipeline tampering: approvals for workflow changes, restricted secrets, segregated environments

OWASP provides CI/CD security best practices and also maintains a “Top 10 CI/CD Security Risks” project—use those as a checklist. ()
NIST SP 800-204D is directly focused on software supply chain security in DevSecOps CI/CD pipelines for cloud-native systems. ()
SLSA levels provide a practical maturity path for integrity guarantees. ()

Step 10 — Close the loop with metrics (continuous deployment without measurement is theater)

Track:

• Deployment frequency
• Lead time
• Change failure rate
• MTTR

DORA defines these metrics and they’ve become the common language across engineering leadership. ()

Then run a monthly “pipeline retrospective”:

• What slowed us down?
• What caused rollbacks?
• What’s noisy/flaky?
• Which guardrail prevented an incident?

That’s how you mature without burning out teams.

5) Toolchain reference architecture (practical, not dogmatic)

6) Common failure patterns (I see these repeatedly)

1. “We want continuous deployment” but main isn’t releasable → long-lived branches, huge PRs, merge conflicts. Trunk-based fixes most of this. ()
2. Pipelines are slow and flaky → engineers bypass them; quality collapses.
3. No progressive delivery → every deploy is a big bang; fear grows; CD rolls back to “monthly release”.
4. No observability gating → you deploy blind and notice failures via customers. OpenTelemetry-first approach helps standardize signals. ()
5. Security bolted on late → attackers target CI/CD; supply chain risk spikes. Use OWASP + NIST guidance early. ()

7) Skill checklist (what to learn, in the right order)

If you’re a DevOps engineer aiming to be “continuous deployment strong,” here’s the order I’d follow:

1. Git + trunk-based development fundamentals ()
2. CI pipeline design (fast, repeatable, secure)
3. Artifacts + versioning + registries
4. IaC basics + environment parity
5. Kubernetes deployment mechanics + rollout strategies ()
6. GitOps (Argo CD or Flux) ()
7. Progressive delivery (canary/blue-green/flags) ()
8. Observability with OpenTelemetry + SLO thinking ()
9. CI/CD security + supply chain maturity (NIST, OWASP, SLSA) ()
10. DORA metrics + continuous improvement loop ()

8) A realistic maturity ladder (so you know where you are)

• Level 0: Manual deployments, tribal knowledge
• Level 1: CI + scripted deploys, basic rollback
• Level 2: Strong test gates + immutable artifacts + IaC parity
• Level 3: GitOps or structured CD control plane + progressive delivery
• Level 4: Automated promotion/rollback based on telemetry + measured improvements via DORA ()

Closing: the mindset that makes continuous deployment work

Tools matter—but the engineering habits matter more. Continuous deployment happens when teams treat the pipeline like production software, reduce risk with progressive delivery, validate with observability, and measure with DORA metrics. The moment you do those consistently, “deploying to production” stops feeling like an event and starts feeling like a routine. ()

Table of Contents

1. Introduction
2. Why Container Security Matters in CI/CD
3. Common Security Risks in Containers and CI/CD
4. Key Principles for Securing CI/CD Pipelines
5. Automation Strategies for Container Security
6. Tools and Technologies to Automate Container Security
7. Best Practices for DevOps Teams
8. Real-World Use Case
9. Final Thoughts

1. Introduction

With the rise of DevOps, Continuous Integration/Continuous Delivery (CI/CD) pipelines have become the backbone of modern software development. At the heart of these pipelines are containers—lightweight, portable units that package software and its dependencies together.

However, as the speed of deployment increases, so does the attack surface. Containers, by design, are ephemeral and scalable, but they also introduce unique security challenges. Manual security measures can no longer keep up. This is where automated container security becomes essential.

This guide is a complete blueprint for DevOps teams looking to automate container security across their CI/CD pipelines.

2. Why Container Security Matters in CI/CD

Containers are used to build, test, and deploy applications at scale. While containers offer speed and portability, they also:

• Share OS kernels, increasing risk from kernel exploits.
• Often use images from public registries, which can be compromised.
• Are dynamic and short-lived, making manual security nearly impossible.
• Integrate deeply with CI/CD tools, risking widespread damage if compromised.

In a CI/CD environment, a single insecure container can lead to:

• Supply chain attacks (compromised base images)
• Credential leaks
• Unauthorized access to production
• Compliance violations

Therefore, securing containers—and automating this security—is no longer optional. It’s a necessity.

3. Common Security Risks in Containers and CI/CD

a. Insecure Images

Images pulled from public repositories may contain known vulnerabilities, malware, or misconfigurations.

b. Secrets in Containers

Hardcoded credentials, API keys, and tokens inside container images pose a significant risk if exposed.

c. Inadequate Isolation

Containers share the host kernel. If improperly configured, one container can escape and affect others.

d. Outdated Base Images

Using outdated images increases the chance of having unpatched CVEs in the container stack.

e. Unvalidated Inputs and Dependencies

Uncontrolled or unscanned libraries may introduce malicious code during build.

f. Weak CI/CD Pipeline Security

Unprotected Jenkins agents, GitHub Actions runners, or GitLab CI configurations are vulnerable to attacks.

4. Key Principles for Securing CI/CD Pipelines

1. Shift Left Security
   Integrate security early in the development lifecycle—from code commit to image build.
2. Zero Trust Architecture
   Assume no service or image is inherently trustworthy.
3. Policy-as-Code
   Define security policies programmatically using tools like OPA (Open Policy Agent).
4. Immutable Infrastructure
   Avoid mutable containers. Rebuild and redeploy instead of patching in place.
5. Security Observability
   Make security events visible to teams using logging and alerting tools.

5. Automation Strategies for Container Security

Here are key areas where DevOps teams should automate container security:

a. Automated Image Scanning

Use scanners like Trivy, Grype, or Anchore to:

• Detect known vulnerabilities (CVEs).
• Identify outdated packages.
• Check for embedded secrets.

Integrate scanners in:

• Pre-commit hooks
• CI build pipelines
• Container registries

b. CI/CD Pipeline Security Gates

Enforce security policies at each stage:

• Fail builds with critical CVEs.
• Block deployments from unauthorized registries.
• Allow only signed images.

c. Secrets Management

Use tools like HashiCorp Vault, AWS Secrets Manager, or Kubernetes Secrets to:

• Inject secrets dynamically.
• Rotate credentials automatically.
• Avoid hardcoding sensitive data.

d. Runtime Threat Detection

Monitor containers post-deployment using tools like:

• Falco (open-source runtime security)
• Sysdig
• Aqua Security

These tools detect suspicious activities like file modifications, reverse shells, or process anomalies.

e. Compliance Automation

Use tools like OpenSCAP, Kube-bench, or Checkov to:

• Enforce CIS benchmarks.
• Validate compliance with GDPR, HIPAA, or PCI-DSS.
• Automate audit trails.

6. Tools and Technologies to Automate Container Security

7. Best Practices for DevOps Teams

✅ 1. Use Minimal Base Images

Prefer distroless or Alpine-based images to reduce the attack surface.

✅ 2. Implement Image Provenance

Sign images with tools like Cosign and verify them in CI/CD before deployment.

✅ 3. Restrict Network Access

Define strict network policies (via Calico, Cilium) to prevent lateral movement.

✅ 4. Enable Namespace Isolation

Separate build, test, and production environments using Kubernetes namespaces or separate clusters.

✅ 5. Limit Container Privileges

Avoid running containers as root. Use capabilities and securityContext settings in Kubernetes.

✅ 6. Audit Everything

Enable audit logging for container runtime, CI/CD actions, and registry access.

8. Real-World Use Case: Securing CI/CD for a FinTech Startup

Problem:
A FinTech startup built their app on Kubernetes using GitHub Actions. They used public base images and embedded secrets in CI scripts.

Risks Identified:

• Images contained critical CVEs.
• Credentials to payment gateways were exposed in logs.
• Production containers ran with elevated privileges.

Automated Solutions Implemented:

• Trivy scans integrated into GitHub Actions.
• Secrets managed using HashiCorp Vault and injected via environment variables.
• Cosign used for image signing and verification.
• Falco deployed for real-time runtime anomaly detection.
• OPA policies enforced no privileged containers.

Outcome:

• Reduced vulnerabilities by 80% in three months.
• Passed PCI-DSS compliance audit.
• Incident response time dropped from 3 hours to 15 minutes.

9. Final Thoughts

Containerization is revolutionizing software development—but without security, it’s a double-edged sword. Manual processes can’t keep up with the scale and speed of modern CI/CD. Automating container security is the only way to ensure continuous delivery doesn’t become continuous risk.

DevOps teams must embrace security as code, automate relentlessly, and adopt tools that make security part of the development culture—not a bottleneck.

💡 Security is not a phase. It’s a mindset baked into every layer of your CI/CD pipeline.

To knowing DevOps. First, you have to understand DevOps. But today we are not going to put some definitions. Now we are willing to understand the whole concept of DevOps professionals. We are going to discuss every aspect of DevOps professionals. We have to understand the old days of software delivery. How you were consuming the software in old days. Actually, you were using multiple software in the back years like the 19th century includes 95, 97, and 98. Now, we are seeing many sorts of applications concerning delivering performance in new days. We have seen so many times of using applications by users through downloading and installation process.

Now, come to the point, we are talking about DevOps. The meaning of DevOps is development and operations collaboration which is done by software teams. By development of software. How it can redefine the whole automation of the software industry? It enhances your mindset for software development. There are many assumptions along with software operations. By the development of software. We take the following steps like manipulating software through engineers from one phase to another and by checking the functions of applications that are out to date and up to date.

What are the benefits of DevOps professionals?

After looking at the term ‘DevOps’. DevOps is a very popular trend in the software industry.  Now, we are going to talk about the privileges or benefits of DevOps professionals. There are many sorts of benefits along with the DevOps software industry. Some of the main benefits associated as:

• Release velocity: It helps in increasing the release velocity. We can release code for more production and with confidence.
• Development cycle: Through it, the complete development cycle from initial design to production deployment becomes shorter.
• Deployment rollback: In this field, we plan for any failure in deployment rollback due to an error in code or issue in production. This gives confidence in feature release without worry about the rollback in spare time.
• Defect detection: With DevOps associate, we can catch defects much faster than releasing them to production. It simplifies the software quality.
• Recovery from failure: In case of collapse, we can recover very fast with this process.
• Collaboration: Through it, a collaboration between development and operations professionals increase.
• Performance-oriented: Through it, entity follows performance-oriented culture in which teams become more productive and more innovative.

Why we should learn DevOps?

After learning the benefits of it. We are going to discuss the reasons why we learn DevOps? There are certainly reasons to learn about DevOps. It covers continuous software delivery, less complexity to manage, and faster resolution to problems in technical areas. In cultural areas, it handles the teams are more productive and happier, the employees are more engaging, and greater professional development opportunities. In business areas, it manages faster delivery time, improved communication and collaboration and more time to innovate, and greater customer experiences. With its exposure to trending technologies and advanced tools by DevOps certifications, you can increase your professional credibility.

How our DevOps course/ training would help?

Now, after getting the learning process. I will explain to you how DevOps courses/ training helps you to make a software developer or software developer engineer from beginner to advanced level? it gives you knowledge- based on training or courses which will help you in the field of software development. DevOps gives you lots of popularity around the world. It skills huge demand and DevOps professionals are one of the highest-paid in the IT industry. If you become a DevOps engineer/ developer then it makes huge impacts on the sectors of IT. This is the recent situation in which you can get jobs as a DevOps engineer. Through this tutorial, I do assume that you will get all your answers to questions.  

The recent study on Continuous Deployment Solution market provides information about this industry with regards to thorough assessment of this business. The Continuous Deployment Solution market is appropriately divided into pivotal segments according to the report. A synopsis of the industry with regards to market size concerning renumeration and volume aspects along with the current Continuous Deployment Solution market scenario is offered in the report.

The report also presents a thorough qualitative and quantitative data pertaining to the projected impact of these factors on market’s future growth prospects. With the inclusive market data concerning the key elements and segments of the global Continuous Deployment Solution market that can influence the growth prospects of the market, the report makes for a highly informative document.

Competition Landscape

A scrupulous analysis on the competitive landscape of the global Continuous Deployment Solution market follows the market’s segmentation analysis. This chapter profiles key market players in detail, providing analysis on them in terms of SWOT analysis, key financials, key developments, product overview, and company overview. Occupancy of the market players has been depicted in this chapter through an intensity map. This chapter is priceless for report readers as it enabled them in making correct and effective business decisions.

Continuous Deployment Solution market competition by top manufacturers/players:

AWS Octopus Deploy Jenkins JetBrains SaaS.group GitLab Atlassian CircleCI CloudBees Google RedHat Scaled Agile IBM Micro Focus Oracle Microsoft Huawei Alibaba Tencent
The research study is based on a combination of primary as well as secondary research. It throws light on the key factors responsible for driving and restricting market growth. Furthermore, the recent mergers and acquisition by leading players in the market have been discussed at length. Moreover, the historical information and present growth of the market have been provided in the scope of the research report. The latest trends, product portfolio, demographics, geographical segmentation, and regulatory framework of the market have also been included in the study.

The Continuous Deployment Solution market report answers the following queries:

Why the demand for segment increasing in region?
At what rate the Continuous Deployment Solution market is growing?
What factors drive the growth of the global Continuous Deployment Solution market?
Which market players currently dominate the global Continuous Deployment Solution market?
What is the consumption trend of the Continuous Deployment Solution in region?
Breakdown Data by Type

Cloud Based and On-premises
Breakdown Data by Application:

Enterprises , Individuals , ,By Region , North America , United States , Canada , Europe , Germany , France , U.K. , Italy , Russia , Nordic , Rest of Europe , Asia-Pacific and Chin

The Goal Of The Report:

The main goal of this research study is to provide a clear picture and a better understanding of the market for research report to the manufacturers, suppliers, and the distributors operational in it. The readers can gain a deep insight into this market from this piece of information that can enable them to formulate and develop critical strategies for the further expansion of their businesses.

Table of Contents of the study: –

Chapter 1 Overview of Continuous Deployment Solution Market

1.1 Brief Overview of Continuous Deployment Solution Industry

1.2 Development of Continuous Deployment Solution Market

1.3 Status of Continuous Deployment Solution Market

Chapter 2 Manufacturing Technology of Continuous Deployment Solution Industry

2.1 Development of Continuous Deployment Solution Manufacturing

2.2 Analysis of Continuous Deployment Solution

2.3 Trends of Continuous Deployment Solution

Chapter 3 Analysis of Global Continuous Deployment Solution Market Key Manufacturers

3.1.1 Company Profile

3.1.2 Product Information

3.1.3 2013-2020 Production Information

3.1.4 Contact Information

Only 1 in 10 developers deploy software on demand, multiple times per day, according to the Cloud Delivery Foundation’s 2021 State of Continuous Delivery report.

Continuous delivery is a key part of how organizations deliver value to their customers, providing the ability to make small changes to software reliably and at any time, according to the report.

The study, completed by research firm SlashData from data collected from more than 19,000 developers, asked for information on lead time for changes, deployment frequency, time to restore services, and change fail rates.

Two-thirds of respondents indicated it takes at least a week for code to go from committed to successfully run in production. Only about 6 percent said they can move code from commit to production in under an hour.

The programming languages that rank highest for speed and stability in software delivery performance are the shell scripting languages (Bash and PowerShell), with Go/Golang and JavaScript being the next best choices. Interestingly, Ruby, Python and Java — three of the most popular languages based on Tiobe rankings, were poorest in terms of software delivery performance.

“Having access to new relevant data is critical to assessing and making the right decisions. By building this State of CD Report, we have produced an exclusive dashboard for use by our community that shows the current state of continuous delivery for deployment frequency, lead time for changes, time to restore service, and more,” said Tracy Miranda, Continuous Delivery Foundation executive director. “Moving forward, we will publish this report annually in order to track the world’s capacity to deliver software with speed and security.”

OVHcloud today has announced the European launch of its Web PaaS solution powered by Platform.sh, which facilitates the development of applications and websites.

As a result of the close collaboration between the two European cloud companies, Web PaaS powered by Platform.sh is 100% integrated into OVHcloud’s portfolio of solutions. Both GAIA-X member companies are convinced of the need to build a European cloud alternative and have designed a solution that is 100% hosted in Europe and meets all the requirements of the General Data Protection Regulation.

Combining the robustness of OVHcloud infrastructure with the flexibility of Platform.sh technology, the Web PaaS powered by Platform.sh enables easier production and higher security of websites and web applications in an open and reversible cloud. Platform.sh technology is compatible with all market leading web platforms, such as Drupal, Magento, WordPress, Symfony, and can be used to deploy components written in PHP, Node.js, Python, Go, Rust, .Net or Java.

Web PaaS powered by Platform.sh is integrated into the OVHcloud Control Panel, allowing teams of developers to focus on their projects and coding, using infrastructure managed by OVHcloud. This way, they can develop applications based on their choice of runtime or web service environment, utilising pre-defined templates to get their projects off the ground. With the ability to quickly add vCPUs, additional storage or additional test environments to a production environment, the solution offers agility and is tailored to meet the needs of developers. Continuous deployment is integrated with native features (built-in GitHub, CI/CD, etc.) and accurate role management makes collaboration around web projects more efficient. Every team member – developers, designers, editors – is able to work in parallel on several environments.

The solution is divided into three levels: START, DEVELOP and EXPAND:
• START offer is adapted to freelancers with a one-user licence
• DEVELOP offers 3-5 licences and
• EXPAND (10 licences and above) is designed to meet the needs of larger teams of developers, either based in a web agency or at the advertiser

“We are proud to announce this partnership with Platform.sh, which once again demonstrates the strength and importance of our ecosystem. Data control continues to be a concern for users, and we would like to support the changing needs in PaaS with trusted solutions, without compromising on performance. We are now taking an important step by jointly deploying a solution that is revolutionising the development of web projects – using a public cloud platform that can guarantee that its users have full reversibility of their data. By integrating Platform.sh technology, we are confirming our move towards PaaS and are offering our European customers a solution that combines data sovereignty, simplicity and agility,” explains Michel Paulin, CEO of OVHcloud.

“We are very excited about this partnership with OVHcloud, firstly because together we’ve built an ambitious, elegant and easy-to-use product. It required a year of collaboration between our teams to design and integrate this common solution, aimed at meeting the needs of the web developer community, who are committed to automation, quality and economic efficiency. Secondly, the PaaS web hosting plan will enable European startups to expand quickly, allowing them to focus on their core products rather than infrastructure management. And finally, because we share our convictions on the importance of offering Europeans a product made in Europe by European companies, providing all the necessary guarantees on compliance with European data and laws,” comments Frédéric Plais, CEO Platform.sh.

Weaveworks has added observability tools to its GitOps platform for Kubernetes environments with no additional configuration effort required.

Company COO Steve George says the addition of an observability portal to the Weave Kubernetes Platform (WKP) to monitor the health of clusters and overall system performance is a natural extension, given the scope of the technology.

In addition, with the release of version 2.5 of WKP, Weaveworks is enabling IT teams to troubleshoot entire fleets of clusters on which the company’s GitOps platform is deployed across. Support for standard metrics and logs along with aggregation of alerts are all included, says George.

IT teams can also leverage open source Prometheus monitoring software to collect endpoint data from other platforms running on Kubernetes in addition to the alerts and cluster events generated by Prometheus.

Finally, Weaveworks has added support for single sign-on capabilities and deepened the level of integration it provides with the Amazon Elastic Kubernetes Service (EKS).

Weaveworks is making the case for a GitOps platform based on Flux, an open source tool that automatically ensures the state of a cluster matches the configuration stored in a Git repository. It uses an operator in the cluster dubbed Flagger to trigger application deployments to Kubernetes without requiring IT teams to acquire and deploy a dedicated continuous delivery platform. Flux monitors all image repositories, detects new images, triggers deployments and updates configurations accordingly.

WKP adds on top of that core platform Team Workspaces, a workflow application for tracking changes to Git-based deployments that can be used by multiple DevOps teams. Each workspace can span multiple Kubernetes clusters to simplify the rollout of applications across a fleet of Kubernetes clusters.

That approach provides the added benefit of improving security and auditing because each pull request made from the repository needs to be authorized and logged, notes George. That audit trail also accelerates troubleshooting, he adds, by making it easier to discover the root cause of an issue across the full stack of a Kubernetes environment that otherwise would be challenging to diagnose.

It’s not clear at what rate organizations are adopting GitOps as a more opinionated instance of a DevOps workflow. However, it’s apparent there is a high correlation between the adoption of GitOps and the transitions organizations are making to Kubernetes. Given the rapid pace of change to microservices-based applications, organizations are attempting to leverage the standard set of application programming interfaces (APIs) that Kubernetes clusters present to automate continuous delivery especially.

IT teams will require observability tools that provide more context than traditional IT monitoring tools to achieve that goal. Each IT team will need to decide for themselves to what degree they want to employ observability tools for a specific application deployment environment versus relying on platforms that observe both legacy IT and more modern Kubernetes environments. Regardless of the path chosen, one way or another observability as a core tenet of a best DevOps workflow is finally becoming a lot easier to achieve.

Forrester research shows organisations with a high level of software delivery maturity are three times more likely to grow at 15% or more annually.

The study was commissioned for CloudBees, an enterprise software delivery company. The research also finds organisations not modernising their software delivery processes may face roadblocks limiting growth, slow DevOps transformations, and exposed security and risk vulnerabilities.

“The organisations that unravel the complexities of the software delivery process exceed business objectives and grow revenue faster than those that do not,” says CloudBees CEO, Stephen DeWitt.

“Those that don’t, face issues with collaboration and communication across their organisations, which can lead to siloed development, delivery bottlenecks, and ultimately customer dissatisfaction.

“It’s like driving on a dirt road instead of a four-lane highway to achieve your goals. You can get where you’re going faster and with fewer pot-holes along the way,” he says.

The study, titled “Modernizing Software Delivery with End-to-End Automation, Orchestration and Collaboration,” is based on a survey of 317 IT decision-makers.

According to the research, organisations pairing delivery automation capabilities with effective collaboration practices, will see more successful digital transformations and exceed business expectations at a higher rate than those that cannot. It finds software delivery maturity is correlated with improved market share, user adoption, innovation and customer loyalty.

Forrester says organisations reaching high levels of software delivery maturity also generate continuous business benefits.

According to the study, they are more likely to:

Succeed with DevOps transformations: High-maturity respondents are more than five times as likely to say their DevOps transformation is exceeding business expectations.
Grow revenue faster: High-maturity organisations are more likely to grow revenue at a higher rate than their less mature peers, they are almost three times as likely to be growing at 15% or more year over year.
Adapt to changing market conditions: High-maturity respondents in the survey are nearly twice as likely as low-maturity respondents to say their organisations responded to the pandemic better than their peers.
Although progress has been considerable, many find they are still facing difficulties. Only 27% of respondents say they practise continuous deployment with automation, from commit through to testing, build/integration, package, and final release.

And 25% of respondent rate their governance, risk, and compliance capabilities for software delivery as very mature, with 40% saying they are not mature.

Fewer than three in 10 high-maturity respondents say they have all the information they need in a single examination to understand software release status, dropping to 2% for low maturity. While less than half of high-maturity respondents, and only one in 10 low-maturity, say they can locate the bottlenecks in their delivery processes in order to fix them.

CloudBees, the enterprise software delivery company, has shared results from a study conducted on its behalf by Forrester Consulting which revealed organisations with a high level of software delivery maturity are three times more likely to grow at 15% or more annually. Conversely, those not modernising their software delivery processes face increasing roadblocks that limit growth, slow DevOps transformations and expose security and risk vulnerabilities.

“The organisations that unravel the complexities of the software delivery process exceed business objectives and grow revenue faster than those that do not,” said Stephen DeWitt, CEO at CloudBees. “Those that don’t, face issues with collaboration and communication across their organisations which can lead to siloed development, delivery bottlenecks and ultimately, customer dissatisfaction. It’s like driving on a dirt road instead of a four-lane highway to achieve your goals. You can get where you’re going faster and with fewer pot-holes along the way.”

The study, Modernizing Software Delivery with End-to-End Automation, Orchestration and Collaboration, was commissioned by CloudBees and is based on a survey of 317 IT decision-makers.

According to the study, ‘Organisations that pair delivery automation capabilities with effective collaboration practices see more successful Digital Transformations and exceed business expectations at a higher rate than those that cannot. Software delivery maturity is correlated with improved market share, user adoption, innovation and customer loyalty.”

Organisations that reach high levels of software delivery maturity also generate continuous business benefits. According to the study, they are more likely to:

Succeed with their DevOps transformations: High-maturity respondents are more than five times as likely to say their DevOps transformation is exceeding business expectations (35% to 6%).
Grow revenue faster: High-maturity organisations are more likely to grow revenue at a higher rate than their less mature peers — they are almost three times as likely to be growing at 15% or more year over year.
Adapt to changing market conditions: High-maturity respondents in the survey are nearly twice as likely than low-maturity respondents to say their organisations responded to the pandemic better than their peers (49% to 27%).
Although there is considerable progress, perfection remains out of reach for most.

Not quite continuous deployment: Just 27% of respondents said that they practice continuous deployment with automation from commit through testing, build/integration, package and final release.
Risky business: Only 25% rate their governance, risk and compliance (GRC) capabilities for software delivery as very mature, with 40% saying they are not mature.
Status unknown: Fewer than three in 10 high-maturity respondents say they have all the information they need in a single study to understand software release status, dipping to a woeful 2% for low maturity.
Finding bottlenecks: Fewer than half of high-maturity respondents, and only one in 10 low-maturity, can pinpoint the bottlenecks in their delivery processes in order to fix them.

With technology evolving in leaps and bounds, AI is shaping the future of digital transformation for every business seeking speed, scalability, quality and flexibility.

Since DevOps is on the verge of delivering exceptional software development environments, DevOps in AI is undeniably the next breakthrough in technology. We’ve witnessed every industry racing toward the adoption of AI to transform their business. To support this transformation, smart automation solutions coupled with AI implementations is becoming the new normal.

So if you’re thinking about reinforcing your enterprise’s DNA by leveraging AI, DevOps principles for artificial intelligence are vital to these efforts. Here’s how businesses can unlock the true potential of AI and stay ahead of the curve.

DevOps and AI
DevOps has been s game-changer for numerous businesses. Applying AI can helps DevOps teams achieve the operational efficiency of machine learning (ML) models all the way through the journey from design to production.

Artificial intelligence undeniably enhances automation and fast-tracks the performance of DevOps. Since the digital transformation process is quite challenging for diverse businesses, DevOps and AI ensures the right processes are in place that can further bring flexibility throughout the journey.

DevOps enables continuous deployment and quick delivery of AI models via:

Speed: Improved overall development and delivery time by reducing unnecessary activities
Scalability: On demand and auto-scaling AI models
Quality: Boosting cleaning datasets and eventually promotes consistent learning that further enhances AI
Data monitoring: Decision making and computing helps handle huge amount of data with precise data monitoring
Benefits of Integrating AI in DevOps
There are endless benefits to integrating AI in DevOps.

Analysis
Data analysis is the key to business success, especially in times when there’s a huge list of competitors rushing to gain consumer trust. Since DevOps produces an enormous amount of data, it isn’t practical for humans to analyze the data without technological intervention.

Artificial intelligence’s smart analytics eases the process by identifying and solving problems in a
more systematic way, resulting in efficiency and increased consumer satisfaction.

Data Correlation
In the wider technology environment, development teams have their own set of issues and errors, which arrive while monitoring the processes. Since the communication scope is minimal and there’s a lack of mutual learning across teams, artificial intelligence can help to accelerate the learning cycle.

Artificial intelligence helps to improve data insights from multiple platforms, which, in turn promotes better communication and establishes better data correlation.

Failure Handling
Machine learning is paving the way to better error resolution based on data, and eventually artificial intelligence will be able to predict signs of failure by examining patterns. Since humans cannot match the observation capabilities of AI, certain indicators of failure can only be analyzed through AI. This further enhances the overall development processes, as quick identification helps identify issues before they can impact the software development life cycle.

Breaks Down Silos
DevOps with AI has the ability to streamline business processes and establish proper communication among diverse systems, removing barriers that slow down the development and deployment processes. Furthermore, the ability to automate, continuously integrate and enhance communication helps business leaders to focus more on other aspects that can further accelerate business growth.

Economic Growth
Since the basic function of an AI-based model is automation, it can help businesses save money and enhance human resources. For any business, eliminating repetitive human work is a tough nut to crack. but a necessity when businesses are demanding continuous innovations. Artificial intelligence could be the best way for enterprises to cut costs and eventually helps the organization to use human resources more creatively and productively.

Makes Software Intuitive
Besides endless business benefits, AI-based software is more intuitive and user-friendly, since it prioritizes the reduction of human error. While DevOps is a well-known, reliable, business-driven approach for delivering software, intermingling artificial intelligence could enhance the user experience by integrating technology more deeply into the system.

The perfect harmony of simplified interactions between humans and AI helps businesses achieve their desired goals of speed, efficiency, scalability and security.

AI-driven systems are providing promising and scalable solutions for enterprises that are on a hunt
for innovative and rapid ways to scale business growth. AI in DevOps is undoubtedly the best solution for accelerating growth through precise monitoring, automation and process improvement. This is the right time for businesses to ensure they operationalize artificial intelligence as a core business objective to stay ahead of their competitors.

Frederick, Maryland. – – Spirent Communications plc (LSE: SPT) today announced a collaboration with Amazon Web Services (AWS) to provide automated 5G testing capabilities to communications service providers (CSPs) building 5G networks on AWS. Spirent’s market-first Landslide 5GC automation package is designed to help carriers quickly deploy 5G networks on AWS, significantly reducing operational costs, time, and resources compared to manual testing.

By combining AWS’s continuous integration and continuous delivery (CI / CD) pipeline with Spirent’s vendor-independent 5G testing capabilities, CSP objectively provides mobile network capabilities and services across a wide range of requirements, including compliance. , You will be able to verify quickly and continuously. Capacity and performance dimensions. The integrated solution is aimed at mobile carriers who want to accelerate the delivery of 5G services on AWS.

Doug Roberts, General Manager of Spirent’s Lifecycle Services Assurance Business, said: “The competition for new 5G technology continues, but it is so complex and new that we have established the critical 5G core testing and validation needed for the success of these complex networks and services. There is a real lack of expertise and resources to provide. To the market. ”

With the introduction of 5G network validation on AWS with Spirent’s Landslide 5GC automation package, operators can easily take advantage of fully integrated 5G test automation capabilities to accelerate both 5G network capabilities and service deployment and updates. Can be tested and verified. It provides a continuous delivery framework using AWS CodePipeline and its security and observer best practices and is available in all AWS Regions. This solution is completely isolated, highly extensible, and supports many parallel tests, while enabling some types of network tests that can be automatically triggered as part of the 5G network application pipeline. ..

“Manually establishing such complex test requirements is a very time-consuming process, usually months,” says Roberts. “Thanks to the new collaboration with AWS, service providers can leverage Spirent’s 5G expertise and proven testing techniques to easily integrate automated 5G testing and validation into AWS for months. You can test with confidence, significantly reduce time to market, and analysis shows that you can achieve significant cost savings compared to traditional manual testing methods. It is shown. ”

CSPs can trigger tests and complete validation without the need for additional manual testing or the development of their own test suite. You can reduce the risk of service failures by testing multi-vendor 5G core elements and accelerate test automation without having to build from scratch.

Containers as a Service (CaaS) Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

Containers as a Service (CaaS) Market Insights 2020 is a professional and in-depth study on the current state of the global Containers as a Service (CaaS) industry with a focus on the Global market. The report provides key statistics on the market status of the Containers as a Service (CaaS) manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry. Overall, the report provides an in-depth insight of 2020-2026 global Containers as a Service (CaaS) market covering all important parameters.

This research focus on analysis progression, which will help investors to analyze the financial market and to perform desired operations to choose Containers as a Service (CaaS) Market investments in the forthcoming year 2020-2026.

Major Market Players Included in This Report:

Apcera (California
US)
AWS (Washington
US)
Docker (California
US)
Google (California
US)
IBM (New York
US)
Kontena (Helsinki
Finland)
Cloud 66 (England
UK)
CoScale (Belgium)
Microsoft (Washington and US
Highlights of the Report

Accurate market size and CAGR forecasts for the period 2020-2026
Identification and in-depth assessment of growth opportunities in key segments and regions
Detailed company profiling of top players of the global Containers as a Service (CaaS) market
Exhaustive research on innovation and other trends of the global Containers as a Service (CaaS) market
Reliable industry value chain and supply chain analysis
Comprehensive analysis of important growth drivers, restraints, challenges, and growth prospects
Segmentation by product type:

Management and Orchestration
Security
Monitoring and Analytics
Storage and Networking
Continuous Integration and Continuous Deployment (CI/CD)
Training and Consulting and Support and Maintenance
Segmentation by application: Banking, Financial Services, and Insurance (BFSI) , Retail and Consumer Goods , Healthcare and Life Sciences , Manufacturing and Others

Some important keys

Identify high potential categories and explore further market opportunities based on detailed value and volume analysis
Existing and new players can analyze key distribution channels to identify and evaluate trends and opportunities
Gain an understanding of the total competitive landscape based on detailed brand share analysis to plan effective market positioning
Our team of analysts has placed a significant emphasis on changes expected in the market that will provide a clear picture of the opportunities that can be tapped over the next five years, resulting in revenue expansion
Analysis of key macro-economic indicators such as real GDP, nominal GDP, consumer price index, household consumption expenditure, population (by age group, gender, rural-urban split, and employed people and unemployment rate. It also includes an economic summary of the country along with the labor market and demographic trends.

The Global “ Container Infrastructure Software Market” 2021 Research report produces information with reference to market size, share, trends, growth, cost structure, capacity, revenue, and forecast 2026. This report also contains the general and comprehensive study of the Container Infrastructure Software market with all its aspects influencing the growth of the market. This report is a full-scale quantitative survey of the Container Infrastructure Software industry and provides data for building strategies to expand the market growth and effectiveness.

Executive summary:

The research report on Container Infrastructure Software market comprises a thorough evaluation of the past and present business scenario to deliver a conclusive overview of the industry’s growth patterns over 2021-2026. It casts light on the sizes and shares the markets and sub-markets, emphasizing on the primary growth stimulants, obstacles, and lucrative prospects impacting the business dynamics.

As per expert analysts, Container Infrastructure Software market is predicted to record notable growth over the study period, bolstering at XX% CAGR throughout.

The research literature also includes actionable insights into the competitive landscape, stressing on the well-known strategies employed by major contenders to enjoy strong profit margins in the upcoming years. In addition, the document answers all queries relating to implications of COVID-19 pandemic.

Market snapshot:

Geographical outlook:

Key regional contributors of Container Infrastructure Software market are Americas, APAC, Europe, Middle East & Africa.
Market share in relation to the consumption volume and value of each regional market is taken into account.
Role of key regions to the overall industry progression is computed.
Product landscape outline:

The document segments the product terrain of Container Infrastructure Software market into Mobile Phone , Tablet and Computer.
Industry share of every product segment is given.
Data relating to overall revenue generated and total sales amassed by every product type are provided.
Application spectrum summary:

The application spectrum of Container Infrastructure Software market is bifurcated into Monitoring and Logging , Security , Continuous Deployment , Management and Orchestration , Networking and Data Management Services and Others.
Estimates for consumption volume and value of every application scope are supported with stats.
Approximations for each application segment’s market share over the analysis period are enumerated as well.
Competitive arena overview:

Top companies in Container Infrastructure Software market is Dell EMC , HPE , Microsoft , Broadcom , Canonical , Oracle Corporation , Cray , Cavium , Docker , Cisco , Mellanox , SUSE , Supermicro , IBM , Intel , Inspur , WiWyn , Lenovo , Huawei and White Box QCT.
The document offers a business overview of all the mentioned players.
Records of net revenue, pricing models, operating profits, sales, and other financial attributes of each organization are systematically sorted.
Manufacturing facilities and operating areas of the listed players are given.
Up-to-date data on partnerships, associations, and new entrants in the domain are also presented in the study.

What will be the Container Infrastructure Software market scale and growth rate in the estimate years from 2021 to 2026?
What are the significant driving factors of the Container Infrastructure Software market?
What are the opportunity and challenges in front of the market?
Who are the key vendors in the Container Infrastructure Software market?
What are the trending features manipulating market equity?
What are the key products of Porter’s five forces model?
what are the global prospects for expanding the global Container Infrastructure Software market?
Reasons why you should buy this report

Understand the current and future of the Container Infrastructure Software Market in both developed and emerging markets.
The report assists in realigning the business strategies by highlighting the Container Infrastructure Software business priorities.
The report throws light on the segment expected to dominate the Container Infrastructure Software industry and market.
Forecasts the regions expected to perceive Rapid growth.
The latest developments in the Container Infrastructure Software industry and details of the industry leaders along with their market share and strategies.
Saves time on the entry level research as the report contains important data concerning about growth, size, leading players and segments of the industry.
Save and cut time carrying out entry-level research by distinguishing the growth, size, leading players and segments within the global Market.

Cloud computing is an important part of the changing work landscape, but it is also affecting personal computer users as well. The positive changes that are offered by cloud computing cannot be overstated as we move into a nimble and responsive new reality with regard to software, hardware, and computation services.

Cloud computing is internet-based functionality for applications and programs that frees users from having to run the software and other programs on their local computer. Cloud computing makes it possible to share information readily between team members and locations and makes certain that teams of workers or multiple users in the same household all have access to necessary information and documents.

If you are ready to learn more about the benefits of cloud computing, read on for more information!

What is Cloud Computing?
We already discussed that cloud computing allows multiple users to access the same information from the same location without having to host applications and software on their local machines. You might be wondering how this is possible.

Cloud computing allows all of the storage that would normally be done on individual machines to be done remotely in the cloud. This means that all users and machines who have access to the data in the cloud will be able to get to this information and the services hosted in the cloud, no matter where they are located or what machine they are working on.

This offers powerful computing power and dynamic information sharing that cannot be done when individual users are all attempting to share local data across limited access points. Cloud computing also keeps data and software more secure in the case that a unique user’s computer is affected by malware or another cyber threat.

Main Benefits of Using Cloud Computing

1. Speed
Deployment and sharing of information happen at lightning speed with cloud computing on your side. There is no more agile way to apply changes to software and databases and cloud computing offers a uniformity in deployment that cannot be enjoyed with other kinds of data and asset management.

Cloud computing also offers unique access to testing new products and designs that are not available when you are trying to manage many unique assets that require the same changes to be made to their operating systems. Being able to test products and services before they are made available to the cloud is an ideal part of the use of cloud computing.

2. Automatic Updates
Continuous integration and continuous delivery are the biggest arguments for cloud computing that you can make. There is no better way to support the needs of your assets related to support, updates, and improvements.

The automatic nature of updates and support when using cloud computing means that your organization and your data will be far more secure and consistently managed than if you are trying to maintain unique machines that are running their own versions of applications and other software assets.

Developers also appreciate that cloud environment solutions also integrate with DevOps tools, making it much easier to detect issues in production processes.

3. Data Security
Data security is the leading concern of most businesses these days and data security is a major factor for personal use computers these days as well. Data security is increasingly difficult these days, especially with remote work requirements.

Cloud computing offers the ability to store your company or personal information securely and without the risk of a single machine affecting the entirety of your network. Granular permissions as well as role-based access can make it much more difficult for bad actors to get access to your network and your data.

Cloud computing comes with built-in encryption that is beyond the deployment skills of most unique users. This additional layer of improved cybersecurity can save you money on other security measures and you will have much better protection in the cloud than you would with numerous products deployed on unique machines in an attempt to protect each asset by itself.

4. Scalability
This benefit of cloud computing is a huge help when you are working with a large organization or a large number of unique machines and assets that need to have the same policies and access delivered to them. While you might not have hundreds of employees that you are trying to manage, there are many organizations that do have many hundreds of people who all need to have the same access and the same permissions.

Even in a smaller company, being able to scale your products and software solutions up and down as needed is a huge benefit of cloud computing. To make these changes, you will only have to update the cloud. Change processes that would take months without cloud computing can be handled in a day or less with access to a cloud-managed system.

5. Cost Reduction
Cloud computing can save your organization a lot of money because you will only have to spend money on your cloud hosting and the software that you need to utilize for your processes and business needs. You will save lots of money on hardware, facilities, utilities, and a large IT staff to take care of your needs.

Cloud computing moves all of your IT needs into one kind of service that you pay a flat rate for in most cases. This can also help you prevent downtimes and other slowdowns that might affect your bottom line if you are not able to do business during an outage.

Cloud Computing is Incredibly Beneficial

Cloud computing is the way of the future, and more and more businesses are going to adopt it over the course of the next year or two. Even personal use often dictates cloud solutions for many different kinds of needs.

If you are ready to work on solutions for your business needs in relation to the storage, deployment, and security, cloud computing is the perfect answer to these needs. You will find that cloud computing solves many of your current struggles and leaves you room to grow and improve in any direction that you want.

Software intelligence company Dynatrace announced the findings of an independent global survey of 700 CISOs, which reveals the rising adoption of cloud-native architectures, DevOps, and agile methodologies has broken traditional approaches to application security. As organizations shift more responsibility “left” to developers to accelerate innovation, increasingly complex IT ecosystems and outdated security tooling can slow releases by leaving blind spots and forcing teams to manually triage countless alerts, many of which are false positives reflecting vulnerabilities in libraries that are not used in production. Organizations are calling for a new approach that is optimized for multicloud environments, Kubernetes, and DevSecOps

This research reveals:

89% of CISOs say microservices, containers, and Kubernetes have created application security blind spots.
97% of organizations do not have real-time visibility into runtime vulnerabilities in containerized production environments.
Nearly two-thirds (63%) of CISOs say DevOps and Agile development have made it more difficult to detect and manage software vulnerabilities.
74% of CISOs say traditional security controls such as vulnerability scanners no longer fit today’s cloud-native world.
71% of CISOs admit they are not fully confident code is free of vulnerabilities before going live in production.
“The increased use of cloud-native architectures has fundamentally broken traditional approaches to application security,” said Bernd Greifeneder, Founder and Chief Technology Officer at Dynatrace. “This research confirms what we’ve long anticipated: manual vulnerability scans and impact assessments are no longer able to keep up with the pace of change in today’s dynamic cloud environments and rapid innovation cycles. Risk assessment has become nearly impossible due to the growing number of internal and external service dependencies, runtime dynamics, continuous delivery, and polyglot software development which uses an ever-growing number of third-party technologies. Already stretched teams are forced to choose between speed and security, exposing their organizations to unnecessary risk.”

Additional findings include:

On average, organizations need to react to 2,169 new alerts of potential application security vulnerabilities each month.
77% of CISOs say most security alerts and vulnerabilities are false positives that do not require actioning as they are not actual exposures.
68% of CISOs say the volume of alerts makes it very difficult to prioritize vulnerabilities based on risk and impact.
64% of CISOs say developers do not always have time to resolve vulnerabilities before code moves into production.
77% of CISOs say the only way for security to keep up with modern cloud-native application environments is to replace manual deployment, configuration, and management with automated approaches.
28% of CISOs say application teams sometimes bypass vulnerability scans to speed up software delivery.
“As organizations embrace DevSecOps, they also need to give their teams solutions that offer automatic, continuous, and real-time risk and impact analysis for every vulnerability, across both pre-production and production environments, and not based on point-in-time ‘snapshots’,” continued Greifeneder.

The report is based on a global survey of 700 CISOs in large enterprises with over 1,000 employees, conducted by Coleman Parkes and commissioned by Dynatrace in 2021. The sample included 200 respondents in the U.S., 100 in the UK, France, Germany, and Spain, and 50 in Brazil and Mexico, respectively.

In 2011, the Center for Devices and Radiological Health (CDRH) initiated the Case for Quality, which provided guidance on how software validation processes can be improved and streamlined for the medical device, pharmaceutical, and biotech industries. By refocusing on patient and product safety, quality assurance, and data integrity, some suggest that validation documentation can be reduced (between 20% and 40%, in my experience) by applying automated testing and deployment. The benefits are accurate code, faster development processes, and reduced validation documentation.

This article is part one of a three-part series and continues the discussion on computer software assurance (CSA), introduces new technologies and methods to achieve continuous software development, provides insight into new standards (e.g., ISO 9001 and ISO 13485-2016), and concludes with a few good reasons to join the growing number of medical device, pharma, and biotech companies with software specialists (or companies that work with software development organizations) who are creating and/or using test automation tools and applying them throughout the software development life cycle (SDLC) phases: inception, design, implementation, maintenance and monitoring, audit, and disposal.

As we await FDA’s communication on the CSA guidance, we have an opportunity to reevaluate the phases of the SDLC and start to think about using new software development methods, such as a combination of artificial intelligence (AI) and adaptive machine language (ML) to provide human-like instructions for automated testing. Challenges include how to evaluate your SDLC phases and make the transition to continuous test automation. What is most puzzling to many is the impact of making this transition from traditional computer system validation (CSV), effectively and efficiently, to a continuous validation life cycle, while applying the tenets of CSA: critical thinking, risk management, patient and product safety, quality assurance, and data integrity.

ISO Standards Address Risk Management
For those in the medical device industry, your software development team (or your software supplier that you work with) are required to comply with medical device regulations and standards, such as ISPE GAMP 5, ISO 9001, and ISO 13485-2016 – Medical Devices. The regulations and standards include a risk-based assessment (RBA) for all computerized software systems. ISO 9001 requires this assessment during the initial phase of the SDLC; ISO 13485-2016, however, requires that a risk-based assessment be updated at every stage of the SDLC to determine the level of risk and the validation and/or change control actions required to be compliant.

Applying the Agile methodology enables you to find opportunities to improve, implement, and comply with the regulations and standards since it uses an iterative approach to ensure quality: continuous code reviews, continuous testing, continuous deployment, continuous monitoring and maintenance, and continuous audit and disposal. Many software development organizations are already performing automated testing and updating their SDLC. Agile testing methods, such as behavior-driven development (BDD), test-driven development (TDD), and acceptance test-driven development (ATDD), are all different approaches to validating code and may be used for automated testing. With the Agile method and progress in development of intelligent technologies, manual testing and/or unscripted testing for low-risk features may become obsolete.

Information Technology Alignment With Software Engineering
Intelligent systems require IT and DevOps personnel, along with software developers, programmers, and software engineers, to acquire new skills and knowledge while applying the principles of software engineering. The ability to get back to basics and skills in software engineering principles, mathematics, and machine language will continue to be highly desired. Making the transition to continuous validation and continuous testing will not be an easy task, especially for mature companies that have been performing traditional validation since 2003 or earlier. Typically, the quality organization has been responsible for ensuring that the software application(s) are compliant and meet all required regulations for software validation. The process seemed straightforward: inception, design (analyze) implementation (build, test, deploy), maintenance and monitoring, audit, and disposal.

Quality assurance’s (QA) role was to oversee the validation effort by reviewing all computer system validation processes and documentation to ensure that they followed the company’s policies and standard operating procedures (SOPs) for compliance and met the associated regulations and standards. Deviations were documented, errors were corrected, and final documentation was reviewed, approved, and stored following 21 CFR Part 11 regulations. With continuous life cycle management for software development, QA’s focus will be on patient and product safety rather than manual testing and documentation, which will be replaced by test automation to enable continuous validation.

Opportunities For Continuous Life Cycle Management
By applying the concept of continuous life cycle management for software development, advantages such as the ability to address risk (critical, high, medium, etc.) during each stage of the development process are feasible. Performing iterative code reviews and automated testing enables early detection of errors, resulting in error-free code that can be shared with other software developers and programmers, minimizing change control actions after software is released. Organizations that will need to reinvent themselves include, but are not limited to, the following:

DevOps groups have developed a set of practices for software development (Dev) and IT operations (Ops). The purpose is to streamline the SDLC, applying validation methodologies, such as Agile, to software development, and providing continuous delivery.
Quality groups will need to change as intelligent technologies replace repetitive and mundane validation tasks by providing automated assurance that the software is performing as it was intended and according to the user requirements specifications.
Intelligent technologies and methods (e.g., AI and ML) will replace traditional/legacy technologies and methods that will over time become obsolete.
SDLC needs to be modernized and get back to basics by applying software engineering discipline and principles to new applications that enable continuous life cycle management.
AI And Advanced Technology And Research
AI presents a modern way for quality organizations to be compliant using test automation. AI is also a method used to advance technology and research by directing computers to perform automated tasks (using algorithms) through the application and translation of machine language. AI can teach machines to learn for themselves and find new avenues for learning.

As the life sciences industry starts taking advantage of AI and ML, its leaders will be known as future thought leaders, visionaries, and influencers. When we think of AI, some examples that come to mind are: adaptive (i.e., robust, efficient, and agile), prescriptive, and actionable data analytics; machine learning, automated administrative tasks (i.e., for patients, doctors, and healthcare personnel), as well as intelligent medical devices; and machines that can be designed to help and assist humans. Some of the top uses include, but are not limited to, diagnostic imaging, surgical robots, and faster, smarter, and secure tools. In fact, when looking at the world of robotics, machines can be designed in such a way as to imitate humans. By integrating engineering fields (i.e., mechanical engineering with computer engineering), software engineers can now develop programs in human-like language.

Some new challenges for software developers and software validation leads may include, but are not limited to, how do you test code when the outcome of the prescribed code potentially provides different results each time you test it? How do you test code when the inputs vary and the outputs are based upon other factors? How do you leverage the extensive work of the software developer/software engineer without duplicating the code? Answers to these and other questions will be discussed as we do a deeper dive into AI and ML.

Concepts For Intelligent Technologies
Concepts that enable intelligent technologies include, but are not limited to, the following:

AML: ACPI Machine Language (AML) is platform independent code.
Machine learning: Can machines learn by themselves? Maybe, if machines were coded to think like humans, learn to read text, and figure out the purpose of the text and/or understand music.
Neural networks are computer systems that simulate the human brain and can be taught to understand things based on their structure. Applied intelligence is AI services coupled with analytics and automation to create a strategy for your organization.
Predictive analytics uses data, statistical algorithms, and ML techniques to predict trends, human behaviors, and patterns based on current and historical data.
Conclusion

Biopharma, medical device, and pharmaceutical companies understand the benefits of test automation and many have already included continuous integration (CI) and continuous development and deployment (CDD) into their SDLC, while others are waiting for the new CSA guidance from FDA on how continuous testing will impact current validation regulations. Either way, intelligent, automated systems (e.g., AI and ML) are already here and will continue to be the future direction of technology development. The impact on these industries’ software and on the validation life cycle will be huge. The next article in this three-part series will continue to expand on validation challenges and next-generation intelligent technologies.

What is DevSecOps?
Briefly, DevSecOps (which stands for Development, Security, and Operations) refers to integrating security at every phase of software development. From the initial design of the product to development stages such as integration, testing, deployment, and delivery, security is tested at every stage of the process.

Earlier, the security of the software was tacked on at the end of the process. A security team would integrate security processes once the software was ready to be delivered. Then, a quality assurance team would perform a final check to ensure that the software and security were working as they should.

Such a process was efficient when there were only one or two software releases in a year. With the rapid advancement of technology and deployment of software, there’s a need to ensure security at every step of the process.

How Does DevSecOps Aid Cloud Security?
Everyone is moving to a digital world, and businesses are not far behind. Managing physical copies of files is not only an arduous task but prone to errors as well. Cloud systems not only ensure accessibility and efficient storage but security as well.

With such systems, teams can collaborate, deploy and rectify their ideas rapidly in real-time.

How can DevSecOps help with cloud security?
While cloud systems are backed by big-name companies such as AWS, Azure, IBM Cloud, and more, increasing cyber crimes leave businesses vulnerable. Business developing apps need to ensure security checks in their continuous integration and continuous delivery pipelines.

By implementing DevSecOps, businesses can perfectly align the development cycle with security operations. Teams can work together to ensure simultaneous integration and testing of security measures at each development process step.

DevSecOps processes ensure continuous investigations and tests for threats. Whenever a new code is added, it may introduce some vulnerabilities to the application. Hence, before the code is implemented, it should be tested for any security failings. Thanks to Cloud’s real-time collaboration option, users can do that rapidly.

At every step, teams scan the application for any failings but perform penetrative tests as well. Ultimately, with continuous checks, businesses are assured of the quality of the end product.

How Does DevSecOps Aid Compliance?
Various laws and compliance guidelines regulate every firm’s operations. Additionally, apps developed by these companies need to adhere to strict guidelines, especially those concerning privacy. When managing several projects at once, it is hard to know whether the software complies with the laws.

While a cloud system can help with compliance to guidelines such as HIPAA, it can be tricky for apps. With the help of DevSecOps, security (and thus, privacy protection) is verified at every step.

Teams can collaborate to understand how compliance guidelines can be implemented and tested to ensure that your business operations stay out of legal trouble. With a cloud system, teams can collect data regarding compliance in real-time and provide evidence when required.

With DevSecOps, a business can not only ensure quicker developments but can ensure efficient processes as well. Understanding DevSecOps can be tricky, but it seems like an insurmountable challenge when you factor in Cloud Systems and Compliance laws.

With services like Sonrai Security, it is much easier to understand the process and how it can be used. Successful implementation of DevSecOps helps businesses grow and deliver better results.

Telefónica on Friday said it reached a clear milestone for cloud-native 5G plans in Brazil, validating Amazon Web Services (AWS) Outposts as “an effective infrastructure option” to deploy core technology for standalone (SA) services in the country.

Telefónica is looking to increase automation and drive new revenues with cloud-based SA 5G platforms. In Brazil, the work with AWS means the operator’s Vivo brand has a way to build out 5G more quickly and introduce new network processes – with Telefónica calling out Continuous Integration, Continuous Deployment, and Continuous Testing (CI/CD/CT).

“Thanks to this project, Telefónica has reached a clear milestone in its 5G plans in Brazil and also for its deployment strategy in the group, where cloud native 5G platforms allow a wide range of private and public infrastructure options,” said Cayetano Carbajo, director of Core, Transport and Service Platforms for Telefónica’s global CTIO team, in a statement.

Testing by Telefónica’s Vivo brand demonstrated functionality, onboarding and spinning up network functions over AWS, in both the AWS South America Region and on-prem data centers via AWS Outposts.

A trio of vendors provided SA 5G core software, with network functions from Mavenir, Nokia and Oracle. Spirent Communications helped validate the end-to-end system, using its core network testing product to verify control plane functionality and high-load testing for user plane performance running in AWS South America Region and on AWS Outputs.

The tests showed that Telefónica’s Vivo can build its 5G core on AWS going forward, as one of the latest operators looking at cloud infrastructure for standalone 5G.

In the U.S. Dish Network is building out a brand-new SA 5G network with open radio access network (open RAN) architecture and using AWS to host, the first major telco to do so. Mavenir, Nokia and Oracle are suppliers for its 5G core.

Fabio Cerone, managing director of EMEA Telco for AWS, in a statement expressed excitement that Telefónica’s Vivo validated AWS Outposts “as one of their best options” for deploying a 5G core network.

“With the option to build their 5G Core on AWS, Telefónica is opening the door to leveraging AWS’s breadth and depth of cloud services across their on-premises locations,” Cerone continued. “With AWS, Telefónica can fully automate their network and dynamically scale and allocate their 5G network capacity to meet the needs of their business and consumer customers.”

Mavenir just disclosed tighter integration with AWS, collaborating to offer cloud-based network functions to telcos on AWS computing infrastructure.

The suite of cloud-based 5G products delivered on AWS include containerized and micro-serviced converged 4G/5G packet core, IMS, messaging, orchestration, AI, telco analytics and open RAN products.

Verizon also is working with AWS in the U.S., tapping the cloud player for mobile edge compute efforts.

Speaking earlier this week at a J.P. Morgan investor conference, Verizon CEO Hans Vestberg talked about the partnership with Amazon and how the carrier is creating applications for customers seeking super low latency and ultra-high throughput – both with public and private 5G MEC.

“There are other companies that are thinking about offloading their software, their radio technology and being run on a cloud infrastructure. That’s a different way,” Vestberg said, according to transcript. “We are virtualizing our networks as well. But we’re not doing it on standard cloud infrastructure.”

That’s one of the big differences, he said, between Amazon’s efforts with Verizon versus with Dish

When we talk about metrics in software delivery, a lot of developers think of execution metrics — things like throughput, delivery and number of deploys. But in reality, those metrics don’t motivate anyone — at least not without connecting them to a bigger picture.

I’ve worked in software for 23 years. I’m a three-time founder and four-time CTO, responsible for leading a 200+ member distributed engineering organization. I’m passionate about metrics because they can tell me a lot about where things are working and where they are not. On the other hand, there’s a risk that operating metrics become the focus, taking attention away from measures of business value.

Getting your developers to embrace metrics isn’t about hand-selecting specific metrics to track. It’s about disseminating the bigger picture and sharing measures of business impact that allow your team to see progress against those larger, shared goals.

When developers understand how their day-to-day work directly impacts the company’s goals and vision, they’re able to understand their impact, too, and will want to do better. When they want to do better, they want execution metrics to get them there.

Clarify Your Goals
If you want your developers to not only track their metrics but be excited to use them, there are a few things you need to do first:

Get clarity on the goals you’re trying to accomplish as a team and as an organization
Figure out how you’re executing against those goals
Find out what’s getting in the way
Developers shouldn’t be obsessing over execution metrics. Rather, you need them to understand what the company is trying to achieve. What is the outcome you’re aiming to deliver to customers and for the business overall?

If your developers have clarity on the company’s mission and vision, they understand how their work directly impacts the customer and the success of the organization, which is highly motivating.

Individual contributors will start asking for access to more information so they can diagnose their own problems and improve their work.

Are we spending too much time on technical investment? Does it feel like we have too many incidents, issues and escalations from customers? Are we far off on our estimates? By answering these questions, your team can determine which metrics to track.

Don’t Make These Mistakes
Using one metric to rule them all. A lot of engineering organizations try to rally entire departments around the same execution metrics. Velocity, or throughput, for example, is a great tool to expose other issues, but consistent velocity is not a guarantee of delivering business value. In fact, targeting consistent velocity is likely to result in tradeoffs you didn’t anticipate, like taking shortcuts to get things out the door.

Treating the metric as the goal. Meeting a metric shouldn’t be the goal; rather, metrics should be viewed as a way to ensure your team is meeting their goals. Organizing around business value is what really helps your team understand the purpose of their work. If you’re just rallying around how many times you can deploy per day, then sure, you can be an amazing software-deploying machine — but you might be deploying changes all day that have nothing to do with the actual goals of the business.

Being a blocker. Developers need autonomy and flexibility to do their jobs well. Give teams a high-level objective and let them determine which metrics they need to push, instead of giving everybody the same metrics and telling them to get better.

If engineers don’t have the autonomy to make their own decisions, then they’re going to feel like they’re just part of the machine. As engineers, and as humans, problem solving is what motivates us, along with the knowledge that our skills are contributing to a larger, shared goal.

A Team That Embraces Metrics: Putting it Into Practice
There are three channels that are essential to communicating business-value metrics to teams. At the company level, executives will introduce their high-level goals at all-hands meetings — where the company is trying to go this year, how you differentiate in the market — the things that really matter to a business.

One level down, during organizational or department meetings, is where to communicate how teams should think about the goals from a product and engineering perspective, while still thinking about them within that overall business context.

It’s also important that this is understood down through the management chain. An engineering manager should be able to explain to their team the overall business goals with local-level reinforcement and how it all ties together.

When you have that knowledge as a team, you know what you’re capable of executing because you understand what you’re trying to achieve for the company and the customer. That’s when developers start asking for more metrics because they want to understand how to get there faster.

If You’re Not Hitting Your Goals, You Should be Asking for the Data
The only real failure is not learning from failure. It should be okay to not hit your numbers. Set a bold target and push yourself, then learn something from your misses. Figure out what you didn’t understand, what got in your way and why.

Estimation is hard. Trying to plan work is hard. But if you know how much you were able to deliver last quarter, you can focus on getting something similar done this quarter instead of assuming this will be the one quarter where there are no interruptions, your initiatives will all be perfectly defined and nobody will get sick.

If you’re not hitting your business goals and you’re not looking at your historical data, then you’re missing your opportunity to improve.

Your Team Embraces Metrics. Now, How Do You Get Them Excited?
The first time I started using continuous deployment (CD), I was truly amazed at how quickly it allowed my team to put new functionality into the hands of our customers. We could instantly find out if they liked it, and if they didn’t, we would build another version tomorrow.

As an engineer, CD is a great way to feel more connected to your customers because you get to put something you built in front of them the same day you complete it. You also become interested in a different form of metrics: usage and adoption.

The connection between your work and the ultimate value to the user is much clearer when the feedback cycle is shorter. CD allows you to get that feedback from customers directly and really see the impact you have on them. That gets people excited.

There’s no Shortcut
You can try to drive an entire organization by execution metrics, but it simply won’t be fulfilling for anyone. If your team is aligned around what they’re trying to achieve, they will recognize the value of execution metrics and they will come looking for them.

There’s no shortcut.

If you start with the reason and you end with the numbers, you know you’re on the right track. You’ll know you’re getting there because you’ll see the numbers moving, your team will be excited by that, and they’ll be motivated to do even better.

DevOps isn’t just a cultural shift — it requires great tools to come to fruition. Below, we’ve pulled together a list of some of the most well-loved DevOps tools available today. But, throwing loads of money into fancy SaaS solutions can quickly gobble up the cloud budget. These DevOps tools all are open source, and enable everything from container builds and orchestration to microservices networking, configuration management, CI/CD automation, full-stack monitoring and more. Here are some of our favorite open source DevOps tools for 2021.

1. Kubernetes
With the ubiquity of microservices and container-based software, it’s no surprise that Kubernetes tops this year’s list of open source DevOps tools. Kubernetes, adoption of which rose by 48% in 2020, is used to orchestrate containers. Instead of releasing microservices manually, Kubernetes can automate deployment, maintenance and scaling of groups of containers in production. Kubernetes, sometimes written as K8s, is hosted by the Cloud-Native Computing Foundation (CNCF).

2. Docker
Docker, the software, is a free and open source platform used to build, ship and run an application as a lightweight container. Containers package up the binaries, libraries, configuration files and dependencies required for a program to run. Over the past decade, containers have played a key role in agile development, and Docker containers led the revolution. At its core is the Docker Engine. Docker Hub is also an excellent resource for finding and sharing prepackaged functions as containers. Also, to plug container vulnerabilities, it may be helpful to use open source container auditing tools like Docker Bench or Anchore.

3. Istio
Microservices are a handy development style, yet they bring new development and architecture concerns. Namely, how do we apply networking policies like security, encryption, observability and telemetry elements consistently across all our services? Well, service mesh is one answer. Service mesh places a sidecar proxy next to each container and abstracts these networking capabilities to a control plane. Istio is one such open source service mesh that has seen widespread adoption. Istio is built on Envoy, opening it up to plugins and extensibility options. We should also mention Linkerd and Kuma as viable open source service mesh alternatives.

4. GitHub Actions
GitHub is arguably the most popular source control and software collaboration platform on the planet. The GitHub platform itself, based on Git, has seen some significant updates in the past few years. Most notable is the GitHub Actions capability. GitHub Actions enable software packages hosted on GitHub to accept inputs and trigger other processes. This could help automate some cool DevOps workflows within GitHub, such as code reviews, branch management or CI/CD processes — the combinations of possibilities here are endless. GitHub Actions are, essentially, YAML files hosted in GitHub repositories that leverage GitHub webhooks. Though this is more of a feature than an open-source tool, we feel it’s important to include here. Actions is free for public repositories with a limit of 100 actions.

5. Jenkins
A big part of the DevOps philosophy is finding ways to automate and deploy new iterations more efficiently. Part of this goal is creating a streamlined continuous integration and continuous delivery (CI/CD) pipeline. Jenkins is an open source automation server with hundreds of plugins to automate the building, deployment and testing of software projects. Although GitHub Actions could theoretically replace a CI server in the future, CI tools like Jenkins, CircleCI, TravisCI and GitLab Community Edition still are preferred by many DevOps teams.

6. Prometheus
Metrics and alerting systems are crucial for site reliability engineers to visualize applications and react to issues. Prometheus, a graduated CNCF project, is a well-loved open source monitoring solution. A Prometheus server collects time-series metrics by scraping HTTP endpoints and generates a system to interact with this data, offering deep querying, visualization, storage and other capabilities. Check out this Awesome Prometheus list for Prometheus introductions and additional resources.

7. Ansible
Ansible is all about automation. Ansible, an open source project sponsored by Red Hat, can be used to automate things like cloud provisioning, networking, deployment, configuration management and other tasks. Ansible has a simple yet effective architecture that is relatively easy to assemble — all you need is a text editor and command line. You describe your infrastructure in a text document and organize your desired states in a playbook. For an example in practice, see how OpenIO uses Ansible. “Ansible is our standard tool not only to deploy the OpenIO core, but also our WebUI, OIO-FS and all upcoming options,” writes Cédric Delgehier, Ops at OpenIO.

8. Chef
Chef is another infrastructure-as-code (IaC) solution for automating your configuration management. Chef uses Ruby to automate server configurations and works well with all major cloud service providers (CSPs). This can be very useful when creating and provisioning large quantities of machines. Like other automation tools on this list, the user describes their components and their states in a declarative format. In Chef, these are known as “recipes,” which can be grouped together into “cookbooks.” You can’t diss Chef for not being on-theme!

9. Terraform
Terraform is another IaC tool that can be used for initiation of building, versioning and further automation using configuration files. “Terraform is a tool for building, changing and versioning infrastructure safely and efficiently,” as it is described on GitHub. Terraform follows an “execution plan” that a user creates with high-level syntax. One unique aspect of Terraform is its emphasis on versioning — this allows you to version the blueprint of your service just as you would your software.

10. JAMStack
As I’ve covered before, JAMStack combines JavaScript, APIs and markdown for constructing web-based applications. While more of a “headless development” methodology than a single open source tool, JAMStack projects are often built using open source components. For example, JAMStack often leverages open source headless content management systems such as Ghost, Strapi and/or Netlify CMS.

11. ELK Stack
ELK Stack is the union of three open source projects maintained by Elastic: Elasticsearch, Logstash and Kibana. With these three components, developers can take in and log data from any source and create helpful visualizations. This centralized logging is enabled with a NoSQL database for storage with Elasticsearch, processing and data collection with Logstash and visualization with Kibana. Increased visibility is vital for data analysis and helps identify errors to reduce mean time to recovery (MTTR).

Rounding Out The DevOps Toolchain for 2021
Programming isn’t only about shipping quality code, it’s about efficient execution. This drive to improve operations across the board is truly putting Ops into everything. With awesome open source DevOps tooling, it means more and more architects can adopt a DevOps approach within their deployment models.

DevOps as a practice, as well as its underlying technology, is always evolving. In 2021, a lot of effort is being placed on wrangling the effects of introducing a microservices development style. We’ve noticed the maturation of container orchestrators and service mesh, as a result. Blueprinting infrastructure-as-code and creating automated, repeatable configurations is another must-have for implementing automated build and release pipelines. Not to mention, many no-code and low-code platforms are opening up DevOps capabilities to non-programmers, albeit from a proprietary perspective.

It’s also helpful to note that some popular open source DevOps tools have been acquired — like Docker and Chef — blurring the lines between their business and their open source roots. When it comes to open source tools, it’s a good practice to adopt vendor-neutral tools with solid community support representing diverse stakeholders. This will help future-proof your project. Even though open source is “free,” users must ensure the benefits outweigh the time-to-onboard as well as the operational overhead of self-hosting.

DevOps tools are beneficial to automate software deployment. Here, we’ve attempted to list some of the most exciting tools to help automate that process. Of course, many new tools are emerging in the DevOps space, and we have hardly scratched the surface. Do you have a favorite DevOps tool that’s not mentioned above? Let us know in the comments below!