# gcp Published articles for gcp. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic DevFeed: [How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic]() Original publisher: [Read original article]() Author: Khan Academy Published: 2020-05-09T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Scalability](), [Architecture & Design](), [Serverless](), [Caching](), [Google Cloud Platform (GCP)](), [Load Balancing]() Tags: [architecture](), [caching](), [cdn](), [engineering](), [gcp](), [google-cloud](), [load-balancing](), [news](), [scalability](), [serverless](), [web]() ### AI overview Marta Kosarchyn explains how Khan Academy handled site usage reaching 2.5 times the previous year's level during the coronavirus pandemic. The article attributes the scalability to Google Cloud services, serverless infrastructure, Datastore, Memcache, Fastly CDN caching, and advance preparation. ### Source excerpt By Marta Kosarchyn Talk about rapid scaling... A few months ago I posted some thoughts on scaling and ... Read more ## Announcing ClickHouse Managed Postgres on Google Cloud DevFeed: [Announcing ClickHouse Managed Postgres on Google Cloud]() Original publisher: [Read original article]() Author: Kunal Gupta Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Google](), [Databases](), [cloud-infrastructure]() Tags: [analytics](), [clickhouse](), [gcp](), [google-cloud](), [postgres](), [replication]() ### AI overview ClickHouse announces a private preview of its managed Postgres service on Google Cloud, with NVMe-backed storage, native CDC into ClickHouse, and pg_clickhouse for a unified query layer. ### Source excerpt ClickHouse Managed Postgres is expanding to Google Cloud, bringing NVMe-backed storage, native CDC into ClickHouse, and a unified query layer via pg_clickhouse to GCP private preview customers. ## The Pulse: Grok's CLI caught uploading all your local files to the cloud DevFeed: [The Pulse: Grok's CLI caught uploading all your local files to the cloud]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-19T14:21:33Z Content type: news Language: en Sources: [The Pragmatic Engineer]() Topics: [Command-line interface](), [Artificial Intelligence](), [Git](), [.env](), [Google Cloud Platform (GCP)]() Tags: [ai](), [cli](), [files](), [gcp](), [git](), [secret]() ### AI overview The article reports claims that the Grok Build coding CLI uploaded local workspace data to cloud storage, including repository contents, Git history, and .env file contents in documented tests. ### Source excerpt Devs discover that Grok CLI pushed all local files, .env files and git history have been pushed to a GCP bucket, unencrypted. SpaceX's initial reaction? Blame the devs ## Cloud provider postmortems: volume vs depth DevFeed: [Cloud provider postmortems: volume vs depth]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [Cloud](), [incident](), [SRE](), [site-reliability-engineering](), [engineering-culture]() Tags: [2017](), [2025](), [2026](), [aws](), [azure](), [cloud](), [dynamodb](), [engineering-culture](), [gcp](), [incident](), [incident-management](), [postmortems](), [s3](), [sre](), [transparency](), [writeup]() ### AI overview The article compares public postmortem practices among Google Cloud, Azure, and AWS. It argues that Google Cloud emphasizes high volume and speed, Azure emphasizes detailed transparency and customer accountability, and AWS publishes fewer writeups with greater depth and industry influence. ### Source excerpt GCP publishes 100+ postmortems a year. AWS publishes almost none. Azure has become the transparency leader. What each posture reveals about engineering culture, and what SREs should steal from all three. ## Announcing Port's new GCP integration DevFeed: [Announcing Port's new GCP integration]() Original publisher: [Read original article]() Author: Matan Geva Published: 2026-07-30T10:06:55Z Content type: release Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Google](), [cloud-infrastructure](), [Software]() Tags: [bigquery](), [cloud](), [cloud-run](), [devops](), [gcp](), [github](), [integration](), [software-catalog]() ### AI overview Port announces a new GCP integration that replaces its previous GCP exporter. The integration brings live Google Cloud resource metadata, runtime data, state, and real-time changes into Port's software catalog across projects and services. ### Source excerpt You can now integrate live cloud resource data into the software catalog, displaying both metadata and runtime data in context, making it easy. ## ITIL vs SRE: why the big clouds went their own way DevFeed: [ITIL vs SRE: why the big clouds went their own way]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-07-26T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [SRE](), [site-reliability-engineering](), [Development](), [Terraform](), [pulumi](), [Availability](), [feature flags](), [incident]() Tags: [automated](), [aws](), [azure](), [cloud](), [feature-flags](), [gcp](), [human-review](), [hyperscaler](), [incident](), [incident-management](), [infrastructure-as-code](), [itil](), [on-call](), [postmortems](), [pulumi](), [release](), [reliability](), [service-catalog](), [sre](), [terraform]() ### AI overview This opinion article compares ITIL practices with SRE operations at hyperscaler scale. It argues that human change boards, single production instances, developer-to-operations handoffs, documentation-first configuration management, and weekly release windows do not fit environments serving millions of external customers. It describes automated approvals, gradual deployments, service-team ownership, infrastructure as code, continuous release, error budgets, SLOs, and blameless postmortems as alternatives. ### Source excerpt The big clouds don't run ITIL. Five assumptions ITIL makes that break at hyperscaler scale, and what AWS, Azure, GCP, and OCI use instead. ## New reference architecture for an AI/ML Internal Developer Platform on GCP DevFeed: [New reference architecture for an AI/ML Internal Developer Platform on GCP]() Original publisher: [Read original article]() Author: Luca Galante Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [internal developer platform](), [Artificial Intelligence](), [Machine learning](), [Architecture & Design](), [Data Management](), [cloud-infrastructure](), [GPU](), [Inference](), [Monitoring](), [observability](), [Security]() Tags: [ai](), [ai-ml](), [architecture](), [data](), [gcp](), [gpu](), [inference](), [infrastructure](), [internal-developer-platform](), [monitoring](), [observability](), [security](), [technical]() ### AI overview The article presents a six-plane reference architecture for an AI/ML internal developer platform on GCP. It addresses the distinct needs of data-intensive and compute-heterogeneous workloads, including data management, dynamic CPU/GPU/TPU provisioning, model lifecycle management, security, and observability. ### Source excerpt Bridge the gap between AI pilots and production. Explore the new six-plane reference architecture for an AI/ML Internal Developer Platform (IDP) on GCP, designed to handle data-intensive and complex ML workloads. ## New reference architectures for IDPs on AWS, GCP, and Azure: Version 2.0 now available DevFeed: [New reference architectures for IDPs on AWS, GCP, and Azure: Version 2.0 now available]() Original publisher: [Read original article]() Author: Luca Galante Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [internal developer platform](), [Platform Engineering](), [Architecture & Design](), [Cloud](), [Amazon Web Services](), [Artificial Intelligence](), [Security]() Tags: [ai](), [architecture](), [aws](), [azure](), [cloud](), [code](), [configuration](), [gcp](), [identity-control](), [idp](), [internal-developer-platform](), [platform-engineering](), [policy](), [secrets-management](), [security](), [workflows]() ### AI overview Version 2.0 of cloud-agnostic internal developer platform reference architectures for AWS, GCP, and Azure reflects real-world platform implementations and enterprise operating patterns. The update emphasizes multi-platform support, dedicated AI integration, embedded security, and code-first declarative and GitOps workflows. ### Source excerpt Explore version 2.0 of cloud-agnostic IDP reference architectures for AWS, GCP, and Azure, featuring multi-platform support, AI integration, and embedded security for modern enterprise platform teams. ## Kubernetes Agent Sandbox: What It Is and How to Deploy It with Pulumi DevFeed: [Kubernetes Agent Sandbox: What It Is and How to Deploy It with Pulumi]() Original publisher: [Read original article]() Author: Adam Gordon Bell Published: 2026-07-21T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [Kubernetes](), [AI Agent](), [Docker](), [Containers](), [Security]() Tags: [agent](), [agents](), [ai](), [gcp](), [gke](), [how-to](), [kubernetes](), [pulumi](), [security](), [tutorials]() ### AI overview This tutorial explains Kubernetes Agent Sandbox, a Kubernetes resource for running coding agents in disposable, kernel-isolated environments using gVisor or Kata Containers. It describes common deployment patterns and shows how to deploy the sandbox on GKE with Pulumi. ### Source excerpt When you use a coding agent, it can seem like there's a trade-off between autonomy and permissions. If you approve every command, it's safe but slow. Let it do whatever it likes and it works more autonomously, but as the nx supply-chain attack showed, that can go badly. The fix is to give the agent a sandbox: a box it's allowed to wreck, with limited permissions and scoped network access. The only files are the checkout you handed it, the only credentials are the task's own, and trashing the machine just means a disposable pod gets garbage-collected early. Pulumi Neo works this way, and if you want to scale that pattern up inside your own organization, the Kubernetes project Agent Sandbox is a great path to building your own. This post is what it is and how to deploy it on GKE with Pulumi. What is Agent Sandbox? Agent Sandbox is a Kubernetes SIGs project that gives AI agents isolated, disposable environments as Kubernetes resources: a Sandbox custom resource, backed by gVisor or Kata Containers for kernel-level isolation. You could build Agent Sandbox yourself. You'd need gVisor support, a userspace kernel that sits between the agent's code and your host. With that in place, you could approximate a sandbox for every agent by stringing together a StatefulSet of size one, a headless Service, and a PersistentVolumeClaim, plus some lifecycle machinery to keep warm pools of nodes around. Agent Sandbox wraps all of that up as a CRD, so you can run a Kubernetes cluster where each sandbox is a disposable, kernel-isolated environment a coding agent runs in. apiVersion: agents.x-k8s.io/v1beta1 kind: Sandbox metadata: name: demo-sandbox spec: podTemplate: spec: runtimeClassName: gvisor containers: - name: agent image: ubuntu:24.04 # swap in your coding-agent image command: ["sleep", "infinity"] The Sandbox CRD, the whole idea in one manifest. There are two common patterns for using it. In the first, every coding agent session in the organization maps to its own pod with a pers ## Eliminate Reliability Blind Spots in AWS, Azure, and GCP DevFeed: [Eliminate Reliability Blind Spots in AWS, Azure, and GCP]() Original publisher: [Read original article]() Author: Andre Newman Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Cloud](), [Kubernetes](), [Resilience](), [Amazon Web Services](), [Azure](), [Chaos Engineering]() Tags: [availability](), [aws](), [azure](), [containers](), [deployment](), [experimentation](), [features](), [gcp](), [gremlin](), [kubernetes](), [orchestration](), [outages](), [resilience](), [uptime]() ### AI overview Gremlin's expanded Detected Risks feature automatically identifies high-priority reliability risks across AWS, Azure, GCP, and Kubernetes environments. It is designed to reveal issues such as misconfigured deployments, crash-looping containers, missing readiness probes, and poor pod distribution before they cause outages. ### Source excerpt Discover reliability risks without running a single test. See how Gremlin identifies high-priority reliability risks across AWS, Azure, and GCP to prevent outages. ## Automating cloud infrastructure with Cursor DevFeed: [Automating cloud infrastructure with Cursor]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-07-07T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [cursor](), [cloud-infrastructure](), [Code](), [Cloud](), [IAM](), [Amazon Web Services](), [Google Cloud Platform (GCP)]() Tags: [aws](), [cloud](), [code](), [cursor](), [database](), [gcp](), [github](), [iam](), [infrastructure](), [open-source](), [postgres](), [typescript]() ### AI overview Encore describes how Cursor can provision cloud infrastructure declared alongside application code, allowing teams to deploy services to their own AWS or GCP accounts while retaining platform-team controls and review. ### Source excerpt Now a Cursor agent can provision your cloud infrastructure from the same code it writes, on your own AWS or GCP. ## Fully Automated AI Inference on AWS, Azure, and Google Cloud with Pulumi DevFeed: [Fully Automated AI Inference on AWS, Azure, and Google Cloud with Pulumi]() Original publisher: [Read original article]() Author: Engin Diri Published: 2026-06-30T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [Ollama](), [Cloud](), [Infrastructure as code](), [model-serving](), [GPU](), [Artificial Intelligence](), [OpenID connect (OIDC)](), [Ubuntu]() Tags: [ai](), [aws](), [azure](), [cloud](), [gcp](), [google-cloud](), [gpu](), [inference](), [infrastructure-as-code](), [model-serving](), [oidc](), [ollama](), [pulumi-esc](), [tutorials](), [typescript](), [ubuntu]() ### AI overview This tutorial shows how to use Pulumi to provision GPU virtual machines on AWS, Azure, and Google Cloud, install drivers, run Ollama, and pull an open-source model without manual setup. It also uses Pulumi ESC with OIDC instead of a long-lived access key and avoids treating model-download completion as an infrastructure resource. ### Source excerpt Putting Ollama on a cloud GPU is something I keep coming back to. A while ago I wrote up running open-source LLMs on an AWS EC2 box with Ollama and Pulumi, and the shape never really changes: a GPU instance, a model server, and a firewall rule in front. Infrastructure as code earned its place by making that kind of setup predictable and repeatable, and AI infrastructure is no exception. A GPU box serving a model is still a VM, a disk, and a firewall rule, and it should be declared like one. Thorsten Hans made exactly that case in his Akamai post, Fully Automated AI Infrastructures with Terraform and Akamai Cloud, which stands up a single GPU instance on Linode, installs the drivers, runs Ollama, and pulls a model, with no manual steps after terraform apply. I liked the shape of it, so this post ports the same idea to Pulumi and runs it across AWS, Azure, and Google Cloud instead of one. The result is one program shape per cloud: a single pulumi up brings up a GPU box that installs its own driver, runs Ollama, and pulls a model with no manual steps, and a single pulumi destroy takes it back down. Along the way it drops the two imperative bits the Terraform version leans on: a static access token sitting in an environment variable, and a null_resource running a shell loop to wait for the model. The first becomes an OIDC login from a Pulumi ESC environment, so no long-lived key lives anywhere. The second turns out not to be a resource at all. What you are building Strip away the per-cloud naming and every version of this is the same three things: a GPU virtual machine, a firewall in front of it, and a cloud-init script that turns a bare Ubuntu box into a running inference server. The model serving runs on Ollama, which exposes an HTTP API on port 11434 and keeps the model resident in GPU memory between requests. flowchart LR Dev([Your machine / curl]) -->|"HTTP :11434"| FW["Firewall / security group (allow 11434, optional 22)"] FW --> VM["GPU VM (Ubuntu 24.04) ## How Alien uses Turso to make BYOC deployments reliable DevFeed: [How Alien uses Turso to make BYOC deployments reliable]() Original publisher: [Read original article]() Author: Alon Gubkin Published: 2026-06-22T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Deployment](), [Cloud](), [Databases](), [AWS IAM](), [telemetry](), [on-prem]() Tags: [aws](), [azure](), [database](), [embedded](), [gcp](), [iam](), [infrastructure](), [monitoring](), [on-prem](), [security](), [software](), [telemetry](), [turso]() ### AI overview Alien uses an embedded, encrypted Turso database to support reliable Bring Your Own Cloud deployments across customer-controlled AWS, GCP, Azure, and on-premises environments. The article explains how Alien's centralized control plane and local operator handle deployments, updates, telemetry, and failures without requiring unrestricted access to customer infrastructure. ### Source excerpt How Alien uses an embedded, encrypted Turso database to keep BYOC deployments manageable inside environments it does not control. ## GCP Pub/Sub connector for ClickPipes is now in Private Preview DevFeed: [GCP Pub/Sub connector for ClickPipes is now in Private Preview]() Original publisher: [Read original article]() Author: Marta Paes Published: 2026-06-17T20:53:04Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Messaging](), [real-time](), [Cloud](), [data](), [Monitoring](), [JSON](), [OpenAPI Specification](), [Terraform]() Tags: [analytics](), [clickhouse](), [cloud](), [data](), [event](), [events](), [gcp](), [infrastructure](), [json](), [logs](), [messaging](), [metrics](), [monitoring](), [openapi](), [real-time](), [terraform](), [tooling]() ### AI overview ClickHouse announces a private-preview GCP Pub/Sub connector for ClickPipes, enabling real-time streaming into ClickHouse Cloud without routing through GCS or Dataflow. The connector supports JSON, Avro, Protobuf, schema registry integration, message filtering, seek options, ordered delivery, scaling, retries, metrics, monitoring, and detailed logs. ### Source excerpt Stream data from Pub/Sub in a few clicks for blazing-fast analytics over GCP infrastructure logs, application events, and operational signals. No routing through GCS or Dataflow required. ## DataAgents: How we turned 9 months of analysis into 10 days DevFeed: [DataAgents: How we turned 9 months of analysis into 10 days]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-06-09T22:42:18Z Content type: tutorial Language: en Sources: [Capital One Tech]() Topics: [Cloud](), [AI Agent](), [Amazon Web Services](), [Azure](), [Google Cloud Platform (GCP)](), [data]() Tags: [agentic-ai](), [ai](), [aws](), [azure](), [cloud](), [cloud-platform](), [data-analysis](), [false-positives](), [gcp](), [least-privilege](), [security]() ### AI overview This engineering deep dive describes the DataAgents pattern for analyzing heterogeneous cloud resources at scale. It focuses on cloud resource dormancy detection across AWS, Azure, and Google Cloud Platform, using entity-specific criteria, confidence-based prioritization, and documented reasoning. The article reports reducing the analysis effort from an estimated 6-9 months to 10 days. ### Source excerpt An engineering deep dive into the pattern that changed how we approach large-scale classification problems. Every engineering team has that project sitting in the backlog. The one where someone says, "We really should analyze all of these," and the room goes quiet. Everyone knows what "all of these" means -- hundreds of entities, complex rules, no clear starting point. For us, it was cloud resource dormancy detection. We had around 350 distinct cloud resource types spread across AWS, Azure and Google Cloud Platform (GCP). Each type has different behavior patterns. An EC2 instance sitting idle looks nothing like a dormant Amazon S3 (S3) bucket or an unattached Elastic IP. Detecting dormancy required understanding what "active" means for each specific resource, then writing detection logic that wouldn't flood operations teams with false positives. Traditional estimate: 6-9 months of expert analysis. Actual time: 10 days. Here's how we did it, and more importantly, here's the reusable pattern behind it. The problem with large-scale analysis: Before we get to the solution, it's worth naming the pattern that makes these projects so painful. It shows up everywhere: Cloud resources - Which of our 350 resource types are dormant? Data governance - Which of our 800 tables have quality issues we should monitor? Security - Which of our access entitlements violate least-privilege principles? Compliance - Which of our 500 policy controls need remediation? In every case, the structure is similar -- a large catalog of heterogeneous entities, entity-specific rules that don't generalize, unknown priorities and a high cost for getting it wrong. The traditional approach is not just slow. It's structurally limited. You get coverage of the "obvious" cases, inconsistent logic across analysts, and tribal knowledge that evaporates when people leave. What you need is something that can assess each entity, apply consistent criteria, prioritize by confidence and document its reasoning. The DataA ## Shai-Hulud Miasma: Inside the Compromise of Red Hat Packages DevFeed: [Shai-Hulud Miasma: Inside the Compromise of Red Hat Packages]() Original publisher: [Read original article]() Author: Roshan Piyush Published: 2026-06-05T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [npm packages](), [Malware](), [Security](), [CI/CD](), [Cloud](), [Encryption](), [Open Source]() Tags: [azure](), [ci-cd](), [encryption](), [gcp](), [malware](), [miasma](), [npm](), [npm-packages](), [security](), [shai-hulud](), [supply-chain]() ### AI overview This article examines the Miasma supply chain attack that compromised official Red Hat npm packages. It describes credential theft, abuse of trusted publishing and CI/CD pipelines, cloud identity enumeration, and per-infection payload encryption, while discussing mitigation. ### Source excerpt An in-depth look at the Miasma supply chain attack that compromised Red Hat npm packages. Learn how the malware spread, stole credentials, abused trusted publishing, and the steps teams can take to mitigate risk. | Blog ## Building Svitgrid, a Solar Monitoring Platform for Home Systems DevFeed: [Building Svitgrid, a Solar Monitoring Platform for Home Systems]() Original publisher: [Read original article]() Author: Ivan Ursul Published: 2026-05-21T00:00:00Z Content type: article Language: en Sources: [Ivan Ursul]() Topics: [Monitoring](), [dashboards](), [real-time](), [Fastify](), [Flutter](), [Modbus](), [TypeScript](), [Cloud Run](), [ESP-IDF](), [ESP32-S3](), [Firestore](), [Mobile]() Tags: [android](), [battery](), [cloud-run](), [energy-monitoring](), [esp-idf](), [esp32](), [esp32-s3](), [firestore](), [flutter](), [gcp](), [iot](), [java](), [migrations](), [mobile](), [modbus](), [mongodb](), [monitoring](), [real-time](), [side-project](), [solar](), [typescript](), [web]() ### AI overview This article explains how Svitgrid, a solar monitoring platform for residential systems, was built. It covers the product's goals, cross-platform clients, cloud backend, ESP32-S3 edge hardware, inverter data paths, and support for multiple inverter systems over Modbus TCP. ### Source excerpt Svitgrid dashboard: real-time power flow from PV to battery, home, and grid ## Keeping it boring: the incident.io technology stack DevFeed: [Keeping it boring: the incident.io technology stack]() Original publisher: [Read original article]() Author: Matthew Barrington Published: 2026-02-26T15:28:47Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [Cloud](), [Kubernetes](), [Containers](), [Google](), [cloud-infrastructure](), [configuration](), [observability](), [Processes]() Tags: [cloud](), [configuration](), [containers](), [customers](), [gcp](), [google-cloud](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [kubernetes](), [observability](), [outage](), [platform](), [post-mortem](), [processes](), [slack-incident](), [software](), [technology-stack](), [virtual-machines]() ### AI overview incident.io describes an intentionally simple technology stack built around managed Google Cloud services and a small platform team. Its default compute environment is GKE Autopilot, while workloads with long-lived processes or substantial storage or resource needs run on Google Compute Engine virtual machines. The article discusses the operational trade-offs of managed Kubernetes, including limitations around node lifetimes, system namespaces, and DNS configuration, and explains the use of Cloud Init for immutable, short-lived hosts. ### Source excerpt This is the story of how incident.io keeps its technology stack intentionally boring, scaling to thousands of customers with a lean platform team by relying on managed GCP services and a small set of well-chosen tools. ## How Vinted Standardizes Large-Scale Decentralized Data Pipelines DevFeed: [How Vinted Standardizes Large-Scale Decentralized Data Pipelines]() Original publisher: [Read original article]() Author: Oscar Ligthart Published: 2025-12-29T00:00:00Z Content type: article Language: en Sources: [Vinted]() Topics: [Data Infrastructure](), [Orchestration](), [airflow](), [Cloud]() Tags: [airflow](), [business-logic](), [cloud](), [data](), [dependencies](), [domain](), [gcp](), [infrastructure](), [orchestration](), [pipelines](), [scale]() ### AI overview Vinted describes its migration to cloud-based data infrastructure and the challenges of coordinating dependencies across decentralized teams and domains. The article explains why relying on teams to hand-craft Airflow DAGs would add unnecessary complexity, and discusses adapting the conventional dbt-with-Airflow approach to Vinted's scale and cost constraints. ### Source excerpt TL;DR: How Vinted standardizes large-scale decentralized data pipelines. ## Moving Beyond Terraform CDK: Encore's Infrastructure-from-Code Approach DevFeed: [Moving Beyond Terraform CDK: Encore's Infrastructure-from-Code Approach]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2025-12-11T00:00:00Z Content type: comparison Language: en Sources: [Encore Updates]() Topics: [Terraform](), [Code](), [Amazon Web Services](), [Deployment](), [Self-hosted](), [Database]() Tags: [aws](), [code](), [deployment](), [gcp](), [high-availability](), [infrastructure](), [least-privilege](), [postgresql](), [security](), [self-hosting](), [terraform]() ### AI overview The article discusses Terraform CDK's sunset and explains Encore's infrastructure-from-code approach. Encore embeds infrastructure declarations in application code, provisions resources through AWS or GCP APIs, and supports Docker-based self-hosting. ### Source excerpt How infrastructure-from-code works when infrastructure declarations are part of your application ## What's new in Redpanda Cloud: A simpler Serverless now on GCP DevFeed: [What's new in Redpanda Cloud: A simpler Serverless now on GCP]() Original publisher: [Read original article]() Author: Towfiqa Yasmeen Published: 2025-11-25T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Redpanda-Connect](), [Serverless](), [Google Cloud Platform (GCP)](), [Streaming](), [Kafka](), [User experience (UX)]() Tags: [announcement](), [gcp](), [kafka](), [redpanda-connect](), [serverless](), [stream-processing](), [streaming](), [usability](), [ux]() ### AI overview Redpanda Serverless introduces a guided onboarding experience, availability on Google Cloud Platform (GCP), and a simpler path to deploying Kafka-compatible streaming. The update targets developers and platform leaders with managed, usage-based streaming that removes broker, cluster, scaling, and infrastructure-management concerns. ### Source excerpt Redpanda Serverless gives you the shortest path from idea to impact with a new onboarding UX, availability on GCP, and more. Here's what's new. ## Introducing external backups on ClickHouse Cloud DevFeed: [Introducing external backups on ClickHouse Cloud]() Original publisher: [Read original article]() Author: Aashish Kohli & Garrett Thomas Published: 2025-10-14T21:44:54Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [Amazon S3](), [AWS IAM](), [tenant data protection]() Tags: [authentication](), [aws](), [azure](), [clickhouse](), [cloud](), [external](), [feature](), [gcp](), [how-to](), [iam](), [json](), [s3](), [storage]() ### AI overview ClickHouse Cloud introduces external backups, allowing users to export backups to storage on the same cloud provider. The article outlines AWS S3 setup using an IAM role and permissions policy. ### Source excerpt ClickHouse Cloud is launching external backups, letting you export full, daily backups to your own storage on the same cloud provider (AWS/GCP/Azure) for complete control over retention, region placement, cold storage, and costs. ## Scaling Trust Through Partnership: Introducing the Chainguard Partner Program DevFeed: [Scaling Trust Through Partnership: Introducing the Chainguard Partner Program]() Original publisher: [Read original article]() Published: 2025-08-13T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [Open Source](), [open-source-security]() Tags: [amazon](), [aws](), [azure](), [chainguard](), [channel](), [gcp](), [google](), [open-source](), [open-source-artifacts](), [open-source-software](), [partner](), [partnership](), [program]() ### AI overview Chainguard announces a global Partner Program designed to help channel partners deliver trusted open source software to customers. The program includes a two-tier structure, partner incentives, technical enablement, and joint go-to-market support. ### Source excerpt The Chainguard Partner Program is a global initiative to empower our channel partners to deliver trusted open source software to customers around the world. ## Behind the scenes: Redpanda Cloud's response to the GCP outage DevFeed: [Behind the scenes: Redpanda Cloud's response to the GCP outage]() Original publisher: [Read original article]() Author: Camilo Aguilar Published: 2025-06-20T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Google Cloud Platform (GCP)](), [incident](), [Complex Systems](), [Monitoring](), [observability](), [Cloud](), [Self-hosted](), [dashboards]() Tags: [2025](), [availability](), [cloud-outage-incident-response](), [cloud-platform-safety-practices](), [cloud-service-outage-management](), [cloud-service-reliability-measures](), [complex-systems](), [gcp](), [gcp-automated-quota-update-issue](), [gcp-outage-impact-on-customers](), [gcp-outage-lessons-learned](), [gcp-outage-response](), [google-cloud-platform-outage-2025](), [handling-cloud-platform-outages](), [incident-response](), [monitoring](), [monitoring-cloud-services-during-outage](), [observability](), [outage](), [production](), [redpanda-cloud-gcp-outage](), [self-hosting](), [thought-leadership]() ### AI overview Redpanda Cloud describes how its GCP clusters remained stable during the June 12, 2025 global GCP outage. The article explains the role of cell-based architecture, degraded monitoring, and reliability practices in the company's response. ### Source excerpt On June 12, GCP went down. Here's how we responded at Redpanda Cloud and what it taught us about safety and reliability. [Next page]()