# operational-excellence Published articles for operational-excellence. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A Tale of Two Flink Autoscalers DevFeed: [A Tale of Two Flink Autoscalers]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-08-21T16:01:01Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [apache-flink](), [autoscaling](), [Netflix](), [stream-processing](), [Kafka](), [Open Source](), [telemetry](), [Network]() Tags: [apache-flink](), [autoscaling](), [aws](), [infrastructure](), [kafka](), [metrics](), [netflix](), [open-source](), [operational-excellence](), [scale](), [stream-processing](), [telemetry]() ### AI overview Netflix describes running two Apache Flink autoscalers: an older in-house system and a newer one from the Apache Flink community. The article explains why autoscaling is necessary for more than 30,000 Flink jobs across AWS regions, discusses workload variability and the cost of resizing stateful jobs, and introduces the metrics-driven design of the first autoscaler. ### Source excerpt Samuel Yeboah, Francesco Di Chiara and Mingliang Liu Today, Netflix runs two Flink autoscalers. That is exactly one more than we want. We built the first one in-house years ago, when there was no mature option suited to our platform. The second came from the Apache Flink community, and it can scale workloads our homegrown system was never designed for. We now run both in production and are steadily converging on the open-source one. Along the way we learned some hard lessons about metrics, cost, and the real price of maintaining infrastructure you could instead adopt, and we hope they are useful whether you run a handful of Flink jobs or tens of thousands. Why autoscaling is not optional at our scale Netflix has run stream processing on Apache Flink since 2017. As of 2026 we operate more than 30,000 Flink jobs across multiple AWS regions. Most are not deployed by hand; they are generated by our managed platform Data Mesh, so the majority of users never touch a Flink job directly. A smaller but growing set are custom jobs, built and operated by teams across the company for use cases like personalization, Ads, and Live events. They range from single-operator jobs that shuttle records between Kafka topics to stateful pipelines with branches, joins, and terabytes of state, and their load swings with daily cycles, launches, and regional failovers. Provisioning every one of those jobs for its peak is wasteful; provisioning for the average causes lag during surges. And in our platform a scaling action is not free: by default it means taking a savepoint, stopping the job gracefully, and restarting it at the new size, which for a large stateful job can take minutes. That leaves a genuinely hard question: how do you give each job the resources it needs, when it needs them, without a human in the loop and without breaking anything? The first autoscaler: watching from outside Our first answer, built around 2019, was an autoscaler shaped like a stream-processing job. It ran on M ## Subscription Model Best Practices: 10 Rules for SaaS Success DevFeed: [Subscription Model Best Practices: 10 Rules for SaaS Success]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-31T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Software]() Tags: [best-practices](), [billing](), [metrics](), [operational-excellence](), [payment-processing](), [pricing-strategy](), [retention](), [saas](), [strategy](), [subscription]() ### AI overview A guide to subscription model best practices for SaaS businesses, covering pricing, trials, billing, retention, relevant metrics, and global payment processing. ### Source excerpt 10 proven subscription model best practices for SaaS. Covers pricing, trials, billing, retention, and the metrics that matter. ## An Update on Heroku DevFeed: [An Update on Heroku]() Original publisher: [Read original article]() Author: Nitin T Bhat Published: 2026-02-06T15:19:16Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [Security]() Tags: [heroku](), [news](), [operational-excellence](), [platform](), [production](), [security](), [support](), [update]() ### AI overview Heroku is transitioning to a sustaining engineering model focused on stability, security, reliability, and support. The platform remains actively supported and production-ready, while product and engineering investments will emphasize quality and operational excellence rather than new features. Existing customers can continue using core services without changes, although Enterprise Account contracts will no longer be offered to new customers. ### Source excerpt Today, Heroku is transitioning to a sustaining engineering model focused on stability, security, reliability, and support. Heroku remains an actively supported, production-ready platform, with an emphasis on maintaining quality and operational excellence rather than introducing new features. We know changes like this can raise questions, and we want to be clear about what this means [...] The post An Update on Heroku appeared first on Heroku. ## The future of software engineering is SRE DevFeed: [The future of software engineering is SRE]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-01-24T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [site-reliability-engineering](), [SRE](), [future of software](), [agentic-coding](), [No-code]() Tags: [agentic](), [agentic-coding](), [engineering](), [macros](), [no-code](), [operational-excellence](), [sre]() ### AI overview The article argues that as code generation becomes cheaper, software engineering will increasingly center on SRE and operational excellence. It emphasizes that the difficult part is maintaining services as systems and business requirements change over time, reliably and at scale. ### Source excerpt When code gets cheap operational excellence wins. Anyone can build a greenfield demo, but it takes engineering to run a service. ## GraphOS Summer 2024 Launch Recap DevFeed: [GraphOS Summer 2024 Launch Recap]() Original publisher: [Read original article]() Author: Andrew I. Carlson Published: 2024-06-17T09:00:00Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphOS](), [GraphQL](), [API Platform](), [Platform Engineering]() Tags: [announcement](), [batching](), [graphos](), [graphql](), [graphql-federation](), [launch](), [operational-excellence](), [performance](), [platform-engineering](), [recap](), [telemetry]() ### AI overview Apollo's GraphOS Summer 2024 launch introduced capabilities aimed at improving the performance and operational excellence of GraphQL platforms. The article highlights GraphQL federation, supergraph operations, query batching, and telemetry and insights. ### Source excerpt On, June 12, 2024, we hosted our GraphOS summer launch event, where we announced new features designed to improve the performance and operational excellence of GraphQL platforms. These new capabilities improve how API platform teams implement and optimize GraphQL federation, providing faster baseline performance and greater control over API request execution in a supergraph. ## Heroku Announces New Hiring as It Invests in Platform, AI, and Developer Experience DevFeed: [Heroku Announces New Hiring as It Invests in Platform, AI, and Developer Experience]() Original publisher: [Read original article]() Author: Andrew Fawcett Published: 2023-08-31T02:04:27Z Content type: opinion Language: en Sources: [Heroku]() Topics: [Heroku](), [Development](), [Artificial Intelligence](), [Tooling]() Tags: [ai](), [developer-tools](), [dx](), [education](), [engineering](), [growth](), [hiring](), [innovation](), [join](), [news](), [operational-excellence](), [operations](), [product-features]() ### AI overview Heroku announces new hiring tied to a new phase of investment in its platform. The company says it plans to expand offerings, improve developer experience and integrations, and incorporate AI into customer applications and developer workflows. ### Source excerpt While it's not our usual approach to announce hiring updates on our product blog, we're pleased to share our ongoing dedication to improving the Heroku experience. Focused Growth and Progress Heroku is entering a new phase of investment, and as a part of this initiative, we are opening up new positions for individuals who would [...] The post Join us for a New Chapter of Growth and Innovation appeared first on Heroku. ## incident.io: A scalable incident management solution built for enterprises DevFeed: [incident.io: A scalable incident management solution built for enterprises]() Original publisher: [Read original article]() Author: Luis Gonzalez Published: 2023-08-04T14:55:23Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [incident management](), [incident](), [Security](), [Software as a service](), [Single sign-on (SSO)](), [Automation](), [SOC]() Tags: [automation](), [catalog](), [enterprise](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [operational-excellence](), [outage](), [post-mortem](), [saas](), [security](), [slack-incident](), [soc](), [sso](), [workflows]() ### AI overview This article presents incident.io as a scalable incident management solution for enterprises with complex products, large customer bases, and many incident responders. It highlights enterprise requirements such as SCIM, SSO, service catalogs, webhooks, adoption, automation, security, and robust workflows. The Catalog feature helps responders understand service ownership and incident context, while the broader feature set is intended to improve incident response, customer trust, and operational excellence. ### Source excerpt With incident.io, enterprise businesses have an incident management solution that can navigate their complex needs and improve their response processes. ## SLA vs KPI: Breaking down the differences, and similarities, of these important metrics DevFeed: [SLA vs KPI: Breaking down the differences, and similarities, of these important metrics]() Original publisher: [Read original article]() Author: Luis Gonzalez Published: 2023-07-03T17:23:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [incident management](), [Incident response]() Tags: [article](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [operational-excellence](), [outage](), [post-mortem](), [slack-incident]() ### AI overview This article explains the differences and overlap between service-level agreements (SLAs) and key performance indicators (KPIs) in incident management. KPIs measure progress toward business goals, while SLAs define a service provider's contractual commitments, including service standards, expectations, and response times. It also discusses how both can support better incident response, operational decisions, and customer satisfaction. ### Source excerpt In this article, we'll lay out the differences between SLA and KPI, and explain how they impact performance management.