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Building a Zero-Cost Daily Job Alert Pipeline on GitHub Actions
Run a daily cron job on GitHub Actions for free by committing a JSON file back to the repo as your database, plus the gotchas from 139 production runs.
September 1, 2026
by Mandar Chaudhari
· 3,560 Views · 1 Like
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How I Built a SQL Diagnostic Tool That Works Without Touching Your Database
Learn how I built an open-source SQL query analyzer that generates dialect-correct index recommendations across multiple dialects.
August 31, 2026
by Sudhakararao Sajja
· 1,944 Views
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Inside terraform-provider-archive: A Memory Pattern From 2016 That Scales With Your Lambdas
archive_file buffers whole files in memory. Enough lambdas and terraform apply OOM-kills your CI runner. The fix is ten lines of Go.
August 31, 2026
by Oleg Mamiev
· 1,652 Views · 1 Like
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Deliberate Decoupling: 6 Architectural Patterns From a Regulated WAS-to-AWS Migration
Six risk-driven patterns from a Fortune 50 insurer's first WebSphere-to-AWS migration — and why decoupling decided the outcome.
August 28, 2026
by Alka Nimje
· 2,575 Views · 3 Likes
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Member Spotlight: Shamsher Khan
We caught up with Shamser to talk about golden prompts, AI-assisted engineering, and how teams can build more consistent and governed AI workflows.
August 28, 2026
by Dominique Roller
· 2,914 Views · 2 Likes
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Running Sentiment Analysis Inside Neo4j With a Java Plugin
A Java UDF that runs sentiment analysis directly inside the Neo4j database engine — no external APIs, no application-layer round-trips, callable from any Cypher query.
August 27, 2026
by Akmal Chaudhri DZone Core CORE
· 3,029 Views · 2 Likes
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Part 1: Building Governed MCP Tool Services With Quarkus LangChain4j and Goose
Build governed, cloud-native Java MCP tool services for Goose agents using Quarkus LangChain4j, Java 25, and Jakarta Bean Validation.
August 26, 2026
by Daniel Oh DZone Core CORE
· 3,362 Views · 3 Likes
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Containerizing Spark and Lakehouse Development with Docker
Use Docker to create a local lakehouse environment that mirrors production, while improving data engineering workflows, Spark testing, and CI reliability.
August 25, 2026
by Aniket Abhishek Soni
· 2,341 Views · 3 Likes
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Multi-Account AWS Architecture: Isolating PHI Workloads Without Slowing Down Engineering Teams
Multi-account AWS architecture enforces PHI workload isolation at the boundary level — making access control provable rather than arguable during security reviews.
August 24, 2026
by Garik H
· 2,105 Views
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Ground Truth for AI-Written Code: Why Context Matters More Than Prompts
AI coding assistants become significantly more powerful when they understand Git history, project architecture, and shared engineering context.
August 24, 2026
by Troian Serhii
· 2,037 Views · 2 Likes
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Agents and Tools in Agentic AI: A Simple Explanation
This article provides a simple introduction to agents and tools in agentic AI. It explains why we need tools and the role of the agent and the model in this process.
August 21, 2026
by Ruchi Saini
· 1,774 Views · 3 Likes
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AWS Bedrock vs Vertex AI vs Azure Foundry: Stop Comparing Benchmarks, Start Asking This Instead
Compare AWS Bedrock, Google Vertex AI, and Azure AI Foundry to choose the right cloud for your AI workloads based on data, models, and governance.
August 20, 2026
by Balaji Venkatasubramaniyar DZone Core CORE
· 2,234 Views · 2 Likes
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How Docker Is Becoming an AI Development Platform
Local AI dev chaos fixed by moving LLM, vector DB, and app into one Compose file, reproducible, but it's not a Kubernetes replacement.
August 19, 2026
by Pruthvi Raj Seknametla
· 31,906 Views · 5 Likes
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Containerizing LLMs: Best Practices for Docker-Based AI Workloads
Bloated LLM Docker images and silent OOM kills taught me: separate weights from images, use runtime, not devel bases, and budget GPU/host memory separately.
August 19, 2026
by Pruthvi Raj Seknametla
· 29,302 Views · 4 Likes
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How Different Docker Engine Versions Led to Partial Traffic Unavailability in Docker Swarm
This article is based on a real-world production case. Different Docker Engine versions on Swarm nodes led to partial traffic degradation on one of the manager nodes.
August 19, 2026
by Denis Tiumentsev
· 1,845 Views · 1 Like
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Real-Time Supply Chain Event Streaming With Kafka and Neo4j
A Kafka producer publishes shipment events, a Python consumer writes them into Neo4j, and a live Plotly dashboard shows network health updating as events arrive.
August 18, 2026
by Akmal Chaudhri DZone Core CORE
· 2,074 Views
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Arm64 Is No Longer the Edge Case
Arm64 has become a first-class Linux platform, with upstream development and native testing improving kernel reliability, portability, and maintenance.
August 18, 2026
by Craig Hardy
· 1,549 Views
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Building Internal Developer Platforms on Kubernetes: The Abstraction Problem Nobody Warns You About
Most Kubernetes platforms stop at infrastructure. Wrapping complexity in a CRD abstraction and admission webhooks, developers should specify intent, not YAML.
August 18, 2026
by Pruthvi Raj Seknametla
· 34,077 Views
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Building Data Pipelines: Here's What Palantir Foundry Did That Surprised Me.
A senior data engineer's honest first impressions after a Palantir Foundry bootcamp: Five things to know before evaluating the platform.
August 18, 2026
by Sashank siwakoti
· 1,676 Views · 1 Like
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From raw manifests to self-service Kubernetes apps: creating enterprise-ready open platforms
Sponsored By: Nutanix The following is sponsored content. It may not reflect the views of our editorial staff. The Kubernetes scaling problem nobody talks about Enterprise platform teams encounter the same pattern repeatedly: a Kubernetes platform works well enough that nobody wants to change it. This happens gradually as teams make reasonable technology choices: selecting different ingress controllers, secrets management tools, CD platforms, or observability software. Individually, none of these decisions is a problem. Months later, however, they’ve created a Kubernetes environment that only a handful of people understand. As soon as that one person gets sick or leaves the company, maintaining or improving the platform becomes much more difficult. Mark Dastmalchi-Round, a Solutions Architect at Nutanix with decades of experience in platform engineering, describes the pattern in blunt terms: “Configuration drift, exacerbated by the fact that multicloud is increasingly becoming the new reality.” Over time, that drift compounds. Companies get acquired, technology merges, and silos form. Suddenly, organizations are managing clusters that look nothing alike and are often held together by institutional knowledge. As a solution, proprietary overlays have sought to address these issues, with mixed results. They tend to reduce overall surface area (fewer choices lead to fewer points of divergence), but often at a cost to portability and extensibility, which is what made Kubernetes so attractive in the first place. A more durable approach is to build on Kubernetes-native primitives, adding governance and operational consistency without replacing the workflows teams already use. The remainder of this article will demonstrate what that looks like in practice. What an open platform actually means in enterprise Kubernetes “Open platform” is a common phrase in the Kubernetes ecosystem, but it’s worth defining what that term actually means in practice. Dastmalchi-Round defines an open platform as one that “exposes industry-standard APIs and, where possible, uses pure upstream open-source projects.” The distinction isn't whether the platform is open source. It's whether it relies on Kubernetes-native APIs and tooling or introduces proprietary CRDs, workflows, and CLIs that make migration difficult. As he notes, "You can still get lock-in with open source, because if it is only one vendor's solution and they layer all of their stuff on top of standard tooling, you are now dependent on their abstractions." The difference is easier to see when comparing an open platform with a proprietary overlay. Comparing Open Kubernetes Platforms and Proprietary Overlays Dimension Open Platform (NKP) Proprietary Overlay Core CRDs Standard upstream (Cluster API, FluxCD, Helm) Vendor-specific, migration cost is high GitOps engine FluxCD (CNCF project) Proprietary sync engine App packaging Helm + OCI (industry standard) Custom catalog format Monitoring stack Pure upstream CNCF (Prometheus, Grafana) Wrapped / vendor-branded Exit cost Clusters survive platform removal Manifests tied to platform APIs Third-party tooling Works if it runs on Kubernetes Requires certified integration Nutanix Kubernetes Platform (NKP) applies these principles by building on upstream Kubernetes components rather than replacing them. As Dastmalchi-Round puts it, the real test is what survives if you remove the platform. "With NKP, the clusters are pure upstream Kubernetes,” says Dastmalchi-Round. “The monitoring stack is pure upstream CNCF projects. GitOps is provided by FluxCD. Your manifests and charts are standard Helm." In other words, the operational tooling may change, but the underlying applications and deployment artifacts remain portable. Raw manifests to managed artifacts: Helm and OCI packaging in NKP Most enterprise teams start with a collection of Kubernetes YAML manifests that work for a single application or environment. While those manifests are typically stored in version control, they aren't easily reusable across environments, self-service for other teams, or packaged in a way that supports consistent versioning and rollback. Helm addresses those limitations by packaging manifests into versioned, parameterized charts. For existing applications, the process typically starts by converting Kubernetes manifests into a standard Helm chart, either manually or with tools such as Helmify. The result is a familiar Helm project structure built around Chart.yaml, parameterized templates, and a values.yaml file, giving teams a reusable deployment artifact instead of a collection of static manifests. Deployment-specific settings, such as image tags, replica counts, and resource limits, move into a values.yaml file, while the underlying templates remain unchanged. Those deployment-specific settings are defined in the chart's values.yaml file. For example: # values.yaml — the self-service interface for application teams replicaCount: 2 image: repository: registry.example.com/myapp tag: "2.1.0" pullPolicy: IfNotPresent resources: limits: cpu: 500m memory: 256Mi requests: cpu: 250m memory: 128Mi ingress: enabled: true host: myapp.internal.example.com annotations: kubernetes.io/ingress.class: "traefik" serviceAccount: create: true name: "myapp-sa" Versioning makes deployments reproducible across environments while providing a clear history of releases. Teams can promote the same chart through development, staging, and production with confidence, then roll back to a previous version if needed. OCI registries address the next challenge: distributing and versioning those charts. Instead of relying on a separate chart repository, teams can store Helm charts alongside container images as immutable, versioned artifacts. Because chart versions can't be overwritten, deployments are reproducible and easier to audit. The approach also fits existing registry workflows. Organizations using Harbor, Amazon ECR, or similar registries can manage container images and Helm charts in the same place, using the same authentication, access controls, and security policies. For example: # Package the chart locally helm package ./myapp --version 2.3.0 # Authenticate to the OCI registry (same registry as your container images) helm registry login registry.example.com \ --username $REGISTRY_USER \ --password $REGISTRY_PASSWORD # Push is stored as an OCI artifact alongside container images helm push myapp-2.3.0.tgz oci://registry.example.com/charts # Any team can pull without touching the source repo helm pull oci://registry.example.com/charts/myapp --version 2.1.0 # Inspect the chart before deploying helm show values oci://registry.example.com/charts/myapp --version 2.1.0 The goal of packaging is to create a self-service deployment model. Once packaged, Helm charts are registered with the NKP catalog, where they appear alongside built-in platform applications as versioned deployment artifacts. Application teams can deploy them by configuring only the settings that vary between environments, while platform teams focus on maintaining reusable application catalogs instead of manually managing deployments. FluxCD deployments, overrides, and upgrades Once Helm charts are stored in an OCI registry, FluxCD keeps deployed clusters aligned with the desired state defined in Git. It continuously reconciles each cluster against that source of truth, automatically correcting configuration drift. In multi-cluster environments, each cluster follows the same reconciliation process using its own configuration. NKP's FluxCD implementation centers on two resources: HelmRepository, which points to the OCI registry, and HelmRelease, which specifies the chart version, configuration values, and target namespace. # Source: points FluxCD at your OCI chart registry apiVersion: source.toolkit.fluxcd.io/v1beta3 kind: HelmRepository metadata: name: internal-charts namespace: flux-system spec: type: oci url: oci://registry.example.com/charts interval: 5m # poll for new chart versions every 5 minutes # Release: declares desired state for a specific deployment apiVersion: helm.toolkit.fluxcd.io/v2beta3 kind: HelmRelease metadata: name: myapp-production namespace: production spec: interval: 10m chart: spec: chart: myapp version: "2.3.0" sourceRef: kind: HelmRepository name: internal-charts namespace: flux-system values: replicaCount: 3 resources: limits: cpu: 1000m memory: 512Mi ingress: host: myapp.prod.example.com Although teams interact with NKP through its web interface, those actions are ultimately represented as standard Kubernetes resources. Configuration changes become declarative objects that FluxCD reconciles like any other GitOps workflow, making the deployment model transparent and compatible with standard Kubernetes tooling without relying on proprietary deployment workflows. Teams typically promote the same chart version from development to staging and production while applying environment-specific overrides through HelmRelease values rather than modifying the chart itself. Promotion becomes a Git commit instead of a manual deployment, with FluxCD automatically reconciling and applying the change. FluxCD also provides continuous drift detection. If someone manually changes a resource in the cluster, FluxCD restores it to the state defined in Git during the next reconciliation cycle. Rolling back a deployment is simply a Git revert, with Git history providing a complete audit trail of configuration changes. How to integrate third-party tools without losing openness Enterprise platform teams are often asked to integrate tools such as vulnerability scanners, cost management dashboards, and application performance monitoring (APM) platforms. The tools themselves aren't the problem. The problem is managing each one through a separate deployment and maintenance process, increasing operational complexity over time. NKP addresses this by treating third-party software like any other platform application. Whether it's an upstream open-source project or a commercial product distributed as a Helm chart, it follows the same Helm-over-OCI packaging model and is deployed and managed through FluxCD. The outcome is a consistent deployment and lifecycle workflow across both first- and third-party applications. For example, an upstream Helm chart such as Redis can be published to the NKP catalog and managed through the same deployment workflow as a first-party application, avoiding the need for a separate integration process. Because this approach relies on standard Kubernetes resources, Helm charts, Git, and Kubernetes RBAC, those workloads remain portable across platforms. As Dastmalchi-Round summarizes, "If it works on Kubernetes, it will work on NKP." Dastmalchi-Round notes that the biggest integration challenges typically come from tools that rely on rigid deployment models, particularly older operator-based packages that expose little configuration. "A few years ago, there was a trend of people overusing the operator pattern for packaging applications," he says. "Operators have their uses, but when they became the distribution artifact, they often resulted in big, opaque blobs running in your cluster. If they didn't do exactly what you needed, you were out of luck." As more vendors have adopted Helm-based packaging, those limitations have become less common. Examples of Third-Party Tool Integrations in NKP Integration Type Packaging Model Configuration Upgrade Path NKP Catalog Security scanner (e.g., Trivy) Helm chart via OCI values.yaml in Git FluxCD HelmRelease bump Yes Custom Grafana dashboard Helm chart + ConfigMap Dashboard JSON in Git Chart version update Yes Cost management (e.g., OpenCost) Helm chart via OCI values.yaml in Git FluxCD HelmRelease bump Yes Service mesh (e.g. Istio) Helm chart via OCI IstioOperator CRDs in Git Controlled chart upgrade Yes Legacy operator-only tool Operator bundle Operator-managed CRDs Operator version update Requires evaluation In practice, the less a tool depends on proprietary deployment mechanisms, the easier it is to integrate, manage, and move between Kubernetes platforms. Conclusion: the platform that gets out of the way NKP doesn't replace Kubernetes workflows—it builds on them. Helm packages applications, OCI registries distribute them, Git defines the desired state, and FluxCD keeps deployments in sync. Instead of introducing proprietary workflows, NKP brings these familiar tools together with the governance, lifecycle management, and self-service capabilities required for enterprise-scale operations. It standardizes these workflows across any environment, including public clouds, on-premises, and edge locations. For enterprise teams, the value lies in achieving consistency without sacrificing portability. As Dastmalchi-Round notes, the question isn't whether lock-in exists, but how costly it is to leave. By relying on upstream Kubernetes components, Helm charts, and GitOps workflows, organizations retain portable applications and deployment artifacts even if they choose a different platform in the future. In the end, an open platform shouldn’t be defined by its licensing model. It should be defined by how much of your platform remains yours if you decide to move on.
August 14, 2026
by DZone Staff
· 11,184 Views
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