Curated articles, resources, tips and trends from the DevOps World.
Recently, a suggestion was made to adopt Tim Peters’ “The Zen of Python” as an overall guiding principle for designing good automation content. That gave me pause because it didn’t seem like the right thing to me.
By 2017, Uber’s codebase was incredibly fragmented to the point where problems would bleed into library versions, build tools, dependency management and collaboration, and code sharing was deeply affected.
If you’re an organization building a platform engineering team, that team’s new customers are application developers. This may seem obvious, but it’s a huge shift from the usual way infrastructure builders think about their job.
And down went all Microsoft 365 services around the world. One popular argument against putting your business trust in the cloud is that if your hyper-cloud provider goes down, so does your business. Well, on the early U.S. East coast morning, it happened.
The IOTA Foundation, the organization behind the IOTA open source distributed ledger technology built for the Internet of Things, envisions a future where every single trade item in the global supply chain is tracked and its provenance sourced using distributed ledgers.
By now, almost everyone agreed platform engineering is probably a good idea, in which an organizations builds an internal development platform to empower coders and speed application releases.
Serverless functions, which account for around half of the workloads today at cloud-first companies, have become a popular means of deploying applications largely because serverless minimizes the number of complexities that teams need to think about.
Like every product at AWS, these were scaled with a product-led growth (PLG) market motion: no gated features, no subscription tiers to choose from. Instead, a Usage-Based Pricing (UBP) model is used to charge based on consumption, directly correlated to the value being delivered to the user.
Robert Nishihara is the co-founder and CEO of Anyscale, the company behind the open source platform, Ray — the distributed machine learning framework being used by ChatGPT and other highly-scaled products, such as Uber.
With more independent software vendor (ISV) applications being packaged to be deployed via Kubernetes to allow for the simple shifting between execution venues, stateful workloads are becoming a standard pattern within these clusters.
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