# self-service Published articles for self-service. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building an AI-native data & insights operating system at Webflow DevFeed: [Building an AI-native data & insights operating system at Webflow]() Original publisher: [Read original article]() Author: Ashwini Chaube Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [data](), [Artificial Intelligence](), [Platform Engineering](), [Data Science](), [decision-making](), [Authorization]() Tags: [agentic](), [ai](), [data](), [data-insights](), [inside-webflow](), [permissions](), [review](), [self-service](), [skills]() ### AI overview Webflow describes how its Data & Insights team built an AI-native operating system for trusted self-service analytics. The approach combines governed data, encoded business context, reusable skills and agents, permissions, architectural controls, review practices, and human judgment, while also changing how the team works through agent-first workflows, learning, and experimentation. ### Source excerpt How we built the governed foundations for trusted self-service analytics while transforming the way our own team works. ## Internal Developer Portals: Why Native CI/CD Drives Scale DevFeed: [Internal Developer Portals: Why Native CI/CD Drives Scale]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [CI/CD](), [developer velocity](), [Automation](), [Scalability]() Tags: [automation](), [ci-cd](), [developer-velocity](), [idp](), [scalability](), [self-service](), [velocity]() ### AI overview This article argues that internal developer portals need native CI/CD integration to move beyond service catalogs and documentation. It explains that integrated pipelines can reduce context switching, support self-service workflows, help enforce standards, and improve developer velocity and scalability. ### Source excerpt Internal developer portals need native CI/CD integration to scale effectively. Learn how integrated pipelines improve velocity. Explore Harness IDP. | Blog ## From Bottleneck to Breakthrough: Centralizing GitOps at Enterprise Scale with VCF 9.1.1 DevFeed: [From Bottleneck to Breakthrough: Centralizing GitOps at Enterprise Scale with VCF 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T14:03:46Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [GitOps](), [Platform Engineering](), [CI/CD](), [Automation](), [Cloud](), [Provisioning](), [Tooling]() Tags: [argo-cd](), [automation](), [cloud-infrastructure](), [cloud-native](), [continuous-delivery](), [deployment](), [gitops](), [home-page](), [platform](), [platform-engineering](), [provisioning](), [self-service](), [vcf-9-1](), [vcf-automation](), [vmware](), [vmware-cloud-foundation]() ### AI overview This VMware article announces VCF 9.1.1, which introduces a native GitOps service for VCF Automation Org users in Tech Preview. By integrating Argo CD, the update aims to centralize declarative application delivery, continuous delivery pipelines, and lifecycle management at enterprise scale, reducing manual work and operational friction for platform engineering teams. ### Source excerpt In today's fast-paced digital economy, where AI-driven innovation demands unprecedented agility, IT leaders are tasked with more than just provisioning infrastructure; they are expected to deliver a frictionless, self-service platform that accelerates software delivery. For platform engineers, cloud architects, and IT executives, the goal is to bridge the gap between robust infrastructure and modern application ... Continued The post From Bottleneck to Breakthrough: Centralizing GitOps at Enterprise Scale with VCF 9.1.1 appeared first on VMware Blogs. ## Beyond the Dashboard: Accelerating Real-Time Intelligence in the Age of AI DevFeed: [Beyond the Dashboard: Accelerating Real-Time Intelligence in the Age of AI]() Original publisher: [Read original article]() Author: Kostiantyn Okhrimenko Published: 2026-08-27T11:15:58Z Content type: article Language: en Sources: [Booking.com Development - Medium]() Topics: [semantic-layer](), [genai](), [analytics stack](), [Large Language Model](), [Artificial Intelligence](), [data](), [dashboards](), [Query (disambiguation)]() Tags: [agent](), [ai](), [ai-tools](), [analytics-stack](), [bi-tools](), [dashboards](), [data](), [genai](), [real-time](), [self-service](), [self-serving-analytics](), [semantic-layer](), [sql]() ### AI overview The article examines how GenAI-driven natural-language interfaces can help stakeholders obtain trusted data answers without repeatedly interrupting data and engineering teams. It argues that a robust semantic layer is necessary to make self-service analytics reliable and precise. ### Source excerpt When an urgent request for a report or dashboard arrives, often just before an executive meeting, data and engineering teams must drop planned work to respond. One request may be reasonable, but repeated interruptions come at a cost: important work, such as scaling infrastructure, improving reliability, models optimization, gets pushed back, while quick, one-off dashboards become more technical debt to maintain. For managers and other decision-makers, the need is real: they require reliable data to make decisions quickly. But getting an answer often depends on someone who knows SQL, understands the data structure, and has time to help. When those people are already busy, the question waits, even when the answer is sitting in the data warehouse. By the time the report is ready, the decision window may have passed. This is not just a prioritization issue. We need a better way for people to get trusted answers quickly without constantly pulling teams away from building and improving the data platform. All of the above can be illustrated by the image: Image 1: Typical reporting circleWhat we will talk about The explosion of GenAI over the last few years has shifted the focus for the modern analytics stack. We are evolving beyond traditional Data Democratization, which often gave teams access to complex pre-AI tools without clear governance, toward natural language data interaction: asking questions in plain English -- Talk to your data concept. In the traditional stack, the "interface" to data was either a dashboard or a SQL editor. This created a high barrier to entry that caused the friction. By properly architecting and utilizing GenAI-driven tools, we can finally bridge the gap between intent and insight. Talk to your data is a self-serve ecosystem where any stakeholder can bypass the traditional ticketing queue and, instead of waiting for an engineer to interpret a requirement and translate it into a query, the user engages with a specialised agent. The challenge, h ## Developer Self-Service Pipelines with Harness IDP DevFeed: [Developer Self-Service Pipelines with Harness IDP]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [CI/CD](), [Deployment](), [Provisioning](), [Microservice]() Tags: [ci-cd](), [deployment](), [developer](), [idp](), [platform](), [provisioning](), [self-service]() ### AI overview This article explains how platform teams can connect Harness IDP self-service workflows to CI/CD and deployment pipelines. It covers triggering deployments, provisioning infrastructure, and managing promotion logic while preserving governance and operational consistency. ### Source excerpt Connect developer self-service to production with Harness IDP's pipeline integration. Automate deployments and boost velocity. Learn more. | Blog ## Internal Developer Platform Golden Paths Guide DevFeed: [Internal Developer Platform Golden Paths Guide]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-08-25T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [internal developer platform](), [Platform Engineering](), [Developer Platform](), [Template]() Tags: [catalog](), [guide](), [internal-developer-platform](), [platform-engineering](), [productivity](), [self-service](), [visibility](), [workflows]() ### AI overview This guide explains how platform engineers can evolve an internal developer platform from a basic service catalog into maintained golden paths. It describes opinionated workflows, self-service capabilities, and embedded operational practices intended to improve developer adoption, speed, and reliability. ### Source excerpt Transform your service catalog into golden paths that developers love. Build an IDP that boosts productivity. Learn more. | Blog ## Continuous Delivery Excellence with Harness IDP DevFeed: [Continuous Delivery Excellence with Harness IDP]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Continuous Delivery (CD)](), [internal developer portal](), [CI/CD](), [Platform Engineering](), [Deployment](), [Automation](), [Orchestration](), [Provisioning]() Tags: [automation](), [ci-cd](), [continuous-delivery](), [deployment](), [developer-velocity](), [idp](), [internal-developer-portal](), [orchestration](), [platform-engineering](), [provisioning](), [self-service](), [tooling]() ### AI overview This article explains how Harness Internal Developer Portal can support continuous delivery through self-service workflows, standardized deployment pipelines, and unified service catalogs. It argues that delivery friction often comes from cognitive overhead, unclear prerequisites, fragmented permissions and configuration, and insufficiently discoverable workflows rather than a lack of pipeline capability. ### Source excerpt Achieve continuous delivery excellence using Harness Internal Developer Portal. Streamline deployments, boost velocity, and empower developers. Learn more. | Blog ## Unlocking your data: the value is in collaboration DevFeed: [Unlocking your data: the value is in collaboration]() Original publisher: [Read original article]() Author: Sam Perridge Published: 2026-08-12T14:59:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [data](), [datasets](), [Artificial Intelligence](), [decision-making](), [systems]() Tags: [ai](), [business-intelligence](), [claude](), [collaboration](), [data](), [data-democratisation](), [data-engineering](), [data-maturity](), [data-platform](), [datasets](), [decision-making](), [governance](), [guardrails](), [reporting](), [self-service]() ### AI overview This opinion article argues that organisations unlock more value from data when datasets are connected and insights are accessible across teams. It describes a progression from paper records and siloed systems to connected and democratised data, including self-service analytics and AI, while emphasising governance and practical adoption. ### Source excerpt Organisations often focus on collecting data and connecting systems, but the greatest value comes from helping datasets work together and making insights accessible to the people who need them. In this post, I explore the journey from siloed data to democratised access, showing how self-service analytics and AI can unlock hidden value, while strong governance provides the guardrails for confident decision-making. ## DevOps vs Platform Engineering & Software Platforms DevFeed: [DevOps vs Platform Engineering & Software Platforms]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [Platform Engineering](), [Software Engineering](), [CI/CD](), [Infrastructure as code](), [Monitoring](), [Artificial Intelligence]() Tags: [ai](), [ci-cd](), [devops](), [infrastructure-as-code](), [monitoring](), [platform-engineering](), [policy-as-code](), [self-service](), [software-engineering]() ### AI overview This article explains how DevOps and platform engineering address different parts of software delivery. It describes DevOps as a culture and set of practices, while platform engineering provides internal tooling and self-service infrastructure to make those practices repeatable at scale. It also discusses when platform engineering becomes useful, how software engineering platforms combine both approaches, and why AI increases the need for governed self-service and policy-as-code. ### Source excerpt DevOps vs platform engineering: what is the difference? Learn how they relate, when to use each, and how a software engineering platform combines both. | Blog ## Self-service peering with a PeeringDB login DevFeed: [Self-service peering with a PeeringDB login]() Original publisher: [Read original article]() Author: Bastiaan Brink Published: 2026-07-27T00:00:00Z Content type: opinion Language: en Sources: [AS215248 - Notes]() Topics: [Networks](), [OAuth](), [Server]() Tags: [automation](), [bgp](), [dashboard](), [ipv4](), [ipv6](), [ixp](), [login](), [mail](), [networks](), [oauth](), [peering](), [permission](), [request](), [self-service]() ### AI overview The article describes a self-service peering portal that uses PeeringDB OAuth to authenticate users, verify ASN update rights, and offer exchanges where both networks are present. Requests create disabled sessions for manual approval, with inherited filtering and validation policies, notifications, and approval-based removal. ### Source excerpt Peering requests used to be mail ping-pong. Now you log in with PeeringDB on the peering page, pick an exchange, and the session is waiting for my approval. ## From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer DevFeed: [From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-01T13:43:11Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [Artificial Intelligence](), [cobol](), [Data Science](), [mainframes](), [Software Engineering](), [AI Strategy](), [self-service](), [dashboards](), [jupyter notebooks]() Tags: [adoption](), [ai](), [business-intelligence](), [cobol](), [data-science](), [enterprise](), [jupyter-notebooks](), [mainframes](), [programming](), [self-service](), [semantic]() ### AI overview A podcast conversation with Dave Langer about nearly three decades spanning COBOL, enterprise architecture, business intelligence, analytics, data science, machine learning, and AI. It discusses persistent data-industry problems, self-service analytics, dimensional modeling, AI adoption, semantic layers, governance, and career advice for data professionals. ### Source excerpt What happens when someone who started programming on a Commodore 64 watches AI reshape the entire data industry? ## PayPal Recurring Payments: Setup and Limits in 2026 DevFeed: [PayPal Recurring Payments: Setup and Limits in 2026]() Original publisher: [Read original article]() Author: Aarthi Poonia Published: 2026-06-27T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [API](), [Software as a service]() Tags: [2026](), [api](), [billing](), [checkout](), [how-to](), [payments](), [paypal](), [saas](), [self-service](), [subscriptions](), [webhooks]() ### AI overview This guide explains how PayPal recurring payments work in 2026, including subscription plans, billing cycles, setup through the dashboard or Subscriptions API, customer cancellation, and webhook handling. It also discusses constraints that SaaS businesses may encounter as their subscription operations grow. ### Source excerpt How PayPal recurring payments work in 2026: setting up subscriptions, managing and canceling them, the limitations for SaaS, and stronger alternatives. ## Future of IaC: Continuous Governance Through a Control Plane DevFeed: [Future of IaC: Continuous Governance Through a Control Plane]() Original publisher: [Read original article]() Author: Mrinalini Sugosh Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [Infrastructure as code](), [cloud-infrastructure](), [Provisioning](), [Terraform](), [GitOps](), [CI/CD]() Tags: [ci-cd](), [cloud](), [cloud-infrastructure](), [configuration](), [gitops](), [iac](), [infrastructure](), [platform-engineering](), [policy](), [provisioning](), [self-service](), [terraform]() ### AI overview This article explains why platform engineering teams are moving beyond point-in-time infrastructure provisioning toward continuous governance through infrastructure control planes. It focuses on reducing drift, enforcing policy, coordinating configuration and operational changes, and enabling safer self-service workflows. ### Source excerpt Learn how platform engineering teams use infrastructure control planes to reduce drift, enforce governance, and scale self-service safely. | Blog ## Why your platform control plane belongs on Temporal DevFeed: [Why your platform control plane belongs on Temporal]() Original publisher: [Read original article]() Author: Joshua Smith Published: 2026-03-31T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [control-plane](), [Automation](), [Orchestration](), [Terraform]() Tags: [automation](), [control-plane](), [error](), [infrastructure](), [orchestration](), [platform](), [platform-teams](), [provisioning](), [scripts](), [self-service](), [temporal-voices]() ### AI overview This article argues that Temporal can serve as a durable backbone for platform teams building internal control planes. It describes the difficulty of managing infrastructure across creation, monitoring, upgrades, configuration changes, and decommissioning, and contrasts Temporal's orchestration model with Terraform's declarative provisioning and reconciliation focus. ### Source excerpt Discover why platform teams use Temporal to build resilient internal control planes. Learn how Durable Execution solves brittle infrastructure automation. ## Introducing Fulfillment Dashboard: New artifact requests are now self-serve DevFeed: [Introducing Fulfillment Dashboard: New artifact requests are now self-serve]() Original publisher: [Read original article]() Published: 2026-02-12T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [dashboards](), [chainguard containers](), [chainguard](), [Containers](), [Development]() Tags: [chainguard](), [chainguard-containers](), [chainguard-factory](), [chainguard-images](), [console](), [customers](), [fulfillment-dashboard](), [real-time](), [scale](), [search](), [secure-software](), [self-service](), [self-service-container-images](), [visibility](), [workflows]() ### AI overview Chainguard introduces Fulfillment Dashboard, a self-service console for submitting, tracking, searching, and voting on secure container image requests. It provides lifecycle visibility, target delivery dates, deduplication, and community upvoting to help prioritize fulfillment. ### Source excerpt Fulfillment Dashboard gives Chainguard customers real-time visibility, tracking, and community voting for new secure container image requests. ## From Bash to Bliss: Scaling Vespa Operations with Temporal DevFeed: [From Bash to Bliss: Scaling Vespa Operations with Temporal]() Original publisher: [Read original article]() Author: Martynas Jakimčikas Published: 2026-01-21T00:00:00Z Content type: article Language: en Sources: [Vinted]() Topics: [Platform Engineering](), [Orchestration](), [Scripting, bash](), [Automation](), [health checks](), [upgrade]() Tags: [automation](), [commands](), [complexity](), [health-checks](), [maintenance](), [orchestration](), [platform](), [scheduled](), [script](), [self-service](), [upgrade]() ### AI overview This article describes how Vinted's platform team handled growing maintenance demands for its Vespa search infrastructure. As the environment expanded from hundreds to more than a thousand nodes and included many deployments, Bash scripts and Knife commands became difficult to manage. The team moved toward durable orchestration with Temporal, automated health checks, scheduled upgrades, and self-service guardrails. ### Source excerpt Growing platform - Growing maintenance Keeping the Lights On (KTLO) is an essential, yet often taxing, part of a platform engineer's role. It represents the routine operational work required to keep the business running and the platform stable. For our team, this primarily involves maintenance on our search engine, Vespa - ranging from version upgrades and service restarts to draining traffic from nodes for hardware replacements. ## Chargebee Review 2026: Pricing, Features and Fit DevFeed: [Chargebee Review 2026: Pricing, Features and Fit]() Original publisher: [Read original article]() Author: Joshua D'Costa Published: 2025-12-16T00:00:00Z Content type: comparison Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [A/B Testing]() Tags: [2026](), [a-b-testing](), [alternatives](), [automated](), [billing](), [checkout](), [customer](), [enterprise](), [experiments](), [features](), [payments](), [paypal](), [retry](), [review](), [saas](), [self-service](), [subscription](), [teams]() ### AI overview A review of Chargebee's pricing, subscription management features, payment integrations, trial workflows, and fit for SaaS and digital businesses. It highlights Chargebee's enterprise focus, broad capabilities, and steeper learning curve for smaller teams. ### Source excerpt Comprehensive Chargebee review covering pricing tiers, subscription management features, enterprise focus, and which SaaS teams benefit most in 2026. ## Doing our best work: Chainguard's engineering principles in practice DevFeed: [Doing our best work: Chainguard's engineering principles in practice]() Original publisher: [Read original article]() Published: 2025-12-03T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [Architecture & Design](), [Scalability](), [Security](), [Open Source]() Tags: [architecture](), [chainguard](), [chainguard-containers](), [chainguard-engineering](), [chainguard-factory](), [chainguard-libraries](), [chainguard-vms](), [complexity](), [engineering](), [open-source](), [reduce](), [scalability](), [security](), [self-service](), [shared-responsibility](), [working-for-chainguard]() ### AI overview Chainguard describes four Engineering Principles--reducing complexity, empowering individuals, engineering value, and ensuring production excellence--and explains how the principles guide engineering practices and recognition. ### Source excerpt Learn more about Chainguard's Engineering Principles: reduce complexity, empower individuals, engineer value, and ensure production excellence. ## Agentic analytics in Slack with ClickHouse, MCP, and PydanticAI DevFeed: [Agentic analytics in Slack with ClickHouse, MCP, and PydanticAI]() Original publisher: [Read original article]() Author: Al Brown Published: 2025-07-23T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Slack](), [MCP Server](), [Large Language Model](), [anthropic](), [SQL](), [Bot](), [GitHub]() Tags: [agentic](), [anthropic](), [api](), [clickhouse](), [code](), [github](), [llm](), [mcp-server](), [self-service](), [slack](), [sql](), [tools]() ### AI overview This tutorial shows how to build a self-service analytics bot in Slack that queries a ClickHouse data warehouse through the ClickHouse MCP server. It uses PydanticAI and the Anthropic API to interpret natural-language questions, invoke MCP tools, generate SQL, interpret query results, and return natural-language answers. ### Source excerpt Let's build a self-service analytics agent that we can talk to directly in Slack, that transparently queries our ClickHouse data warehouse. ## Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform DevFeed: [Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform]() Original publisher: [Read original article]() Author: Karen Fehmer Published: 2025-06-02T03:53:58Z Content type: article Language: en Sources: [Kogan.com]() Topics: [data](), [Architecture & Design](), [BigQuery](), [CI/CD](), [GitHub Actions](), [Git]() Tags: [architecture](), [bigquery](), [ci-cd](), [data](), [dbt](), [github-actions](), [looker](), [self-service](), [tableau]() ### AI overview Kogan.com describes replacing a request-based BI model with a governed self-service data platform built around BigQuery, dbt, Looker, and Acryl (DataHub). The architecture uses layered models, GitHub Actions CI/CD, testing, documentation, and controlled environments to support trusted analysis and reporting. ### Source excerpt At Kogan.com, our data needs have grown alongside the business. As more teams relied on insights to move quickly, it became clear our request-based BI model couldn't scale. We needed a platform that empowered teams to answer their own questions, trust the numbers, and move independently. That journey led us to build a self-service platform grounded in governance, transparency, and scalability--powered by dbt, Looker, and Acryl (DataHub). Rethinking Our BI Model We originally relied on Tableau. It served us well but had limitations: duplicated logic, inconsistent metrics, and limited collaboration with dbt. Tableau workbooks weren't version-controlled, which made maintaining consistency difficult. To bridge modeling and reporting, we often created extra presentation tables in dbt, adding complexity. We needed a platform that integrated tightly with dbt and supported governed exploration. A New Architecture: Modular, Transparent, Scalable We redesigned the platform around a clean, modular flow: Raw Sources -> BigQuery -> dbt -> Looker -> Acryl (DataHub) Our data transformations are built in dbt, where we follow a layered modeling structure. While we use stg_ (staging) and int_ (intermediate) models primarily for data cleaning and standardization, the marts_ models are the ones that power our analysis and reporting. These models contain our fact and dimension tables, fully aligned with business logic and ready for consumption in Looker. We've integrated CI/CD pipelines using GitHub Actions, and every change is tested before deployment. This includes dbt tests, schema validations, and model documentation to ensure confidence at every layer. Why Looker Was the Right Fit for Self-Service Looker offered a structured, governed approach that aligned with our dbt-first architecture. LookML let us centralize business logic, version it with Git, and deploy changes through CI/CD. With support for multiple environments (UAT and Production), we can test safely before releasing to users ## Platform Engineering as a Service DevFeed: [Platform Engineering as a Service]() Original publisher: [Read original article]() Author: Nerved-Dev Published: 2024-11-14T20:47:29Z Content type: opinion Language: en Sources: [Brave New Geek]() Topics: [Platform Engineering](), [DevOps](), [CI/CD](), [Infrastructure as code]() Tags: [ci-cd](), [culture](), [devops](), [iac](), [infrastructure](), [konfigurate](), [platform-engineering](), [product-development](), [self-service](), [software-engineering]() ### AI overview The article argues that DevOps can create duplicated effort, inconsistent tooling, technical debt, and fragmented processes as organizations scale. It presents platform engineering as a centralized, productized approach that provides reusable self-service platforms, standardizes infrastructure and developer tools, and supports governance while allowing developers to focus on product code. ### Source excerpt Like most industry jargon, "DevOps" means a lot of things to a lot of different people. While many folks view it as specific to certain tooling or practices, such as CI/CD or Infrastructure as Code (IaC), I've always viewed it as an organizational model for how software is built and delivered. In particular, my interpretation is that DevOps is about shifting more responsibilities "left" onto developers, moving away from the more traditional "throw it over the wall" approach to IT operations. No doubt this encompasses tooling or practices like CI/CD and IaC, which are responsibilities that developers now shoulder, perhaps with the support of dev tools, productivity, or enablement teams--some companies just call this the "DevOps" team. ## Understanding Konfig's Opinionation DevFeed: [Understanding Konfig's Opinionation]() Original publisher: [Read original article]() Published: 2024-06-11T19:59:57Z Content type: opinion Language: en Sources: [Brave New Geek]() Topics: [Google Cloud Platform (GCP)](), [GitLab](), [Deployment](), [Development](), [Architecture & Design]() Tags: [architecture](), [deployment](), [deployment-driven-development](), [development](), [gcp](), [gitlab](), [governance](), [idp](), [internal-developer-platform](), [konfigurate](), [platform-engineering](), [real-kinetic](), [self-service]() ### AI overview This opinion article explains how Konfig, an opinionated enterprise cloud platform, aims to reduce cloud-platform ownership costs and shorten software delivery timelines. It describes built-in security, governance, organizational standards, developer self-service, and Deployment-Driven Development, with a focus on Google Cloud Platform and GitLab. ### Source excerpt In my last post, I talked about the benefits of an opinionated platform. An opinionated platform allows your engineers to focus on things that matter to your business, such as shipping and improving customer-facing products and services. This is in contrast to engineers spending substantial time on non-differentiating work like platform infrastructure. Rather than infrastructure architecture, developers can focus more on the product architecture. Konfig is an opinionated platform which provides two key value drivers: 1) reducing the investment and total cost of ownership needed to have an enterprise cloud platform and 2) minimizing the time to deliver new software products. ## Platform Engineering for APIs DevFeed: [Platform Engineering for APIs]() Original publisher: [Read original article]() Author: Ishwari Lokare Published: 2024-03-19T09:30:00Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [Platform Engineering](), [API Platform](), [GraphOS](), [GraphQL](), [Architecture & Design](), [Developer experience]() Tags: [adobe](), [api-platform](), [apis](), [apollo](), [apollo-federation](), [developer-experience](), [developer-platform](), [engineering](), [graphos](), [graphql](), [internal-developer-platform](), [kubecon](), [netflix](), [platform](), [platform-engineering](), [self-service]() ### AI overview This article explains how platform engineering teams can apply platform-engineering practices to APIs using Apollo GraphOS. It presents federated GraphQL as an API composition layer over existing REST services and discusses automation, self-service access, and developer experience. ### Source excerpt The CNCF's flagship KubeCon Europe 2024 conference is scheduled in Paris from 19-22 March 2024. Platform engineering continues to be a prominent theme this year, for which the CNCF recently issued a set of recommendations and a maturity model defining it. Apollo GraphQL helps API platform teams apply these recommendations to the API layer with Apollo GraphOS, its flagship product. ## GraphQL in a Platform Engineering World DevFeed: [GraphQL in a Platform Engineering World]() Original publisher: [Read original article]() Author: Michael Watson Published: 2024-01-04T14:00:53Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [internal developer portal](), [Platform Engineering](), [API](), [Developer experience](), [Application Development](), [real-time](), [Caching]() Tags: [apis](), [application-development](), [caching](), [developer](), [developer-portal](), [graphql](), [observability](), [platform](), [platform-engineering](), [real-time](), [self-service]() ### AI overview The article explains how platform engineering teams use GraphQL to power self-service developer portals. It describes GraphQL's query, mutation, and subscription model, its coexistence with REST APIs, and benefits including developer experience, observability, application development, and API flexibility. ### Source excerpt In today's digital world, optimizing APIs for efficiency and real-time performance is crucial. Your applications need to keep evolving by introducing new features or capabilities over time. Many organizations utilize platform engineering teams to create a self-service developer portal with the goal of accelerating their developers velocity building these new features. [Next page]()