Take a look at the items I read today, and you may find something that interests you. There were lessons learned adopting microservices, a few pieces on (cloud) security, and a handful of items that might impact your application architecture.
[article] Airbnb at Scale: From Monolith to Microservices. Recording and transcript from InfoQ that explains lessons learned by Airbnb during a microservices transition, and what was more difficult than they thought.
[blog] Best Kept Security Secrets: How Assured Workloads accelerates security and compliance. More people should know about this. Get the benefits of regulatory-required isolation, but in the public cloud? Assured Workloads does that in Google Cloud, supporting everything from FedRAMP to PCI DSS and HIPAA.
[blog] Google Cloud Assured Open Source Software service is now generally available. This is also a big deal. Use 1000+ of the most popular Java and Python packages, signed and distributed by Google. TechCrunch covered the news as well.
[research] Reports of the Death of Enterprise Tech Spending Have Been Greatly Exaggerated: The March 2023 Battery Ventures State of Cloud Software Spending Report. Here’s a summary post with a link to the full report. It shows where some tech investments are going, and where enterprise buyers are focusing their spend.
[blog] 12 Insidious Biases that Impact Our Every Day Lives. If you don’t experience these on a daily basis, you’re a much better person than I am. It’s useful to be able to recognize and name the bias when you see it!
[article] Inside the never-ending race to update the Pentagon’s IT. Once you fall far behind on technology, it’s so tough to catch back up. A culture of continuous modernization seems like the only way to prevent that.
[article] Survey Shows Companies Moving away from DIY Kubernetes. I still have yet to read a public company’s annual report where they bragged about how well they ran Kubernetes. It’s not a differentiating component for most any business.
[blog] How to identify and reduce costs of your Google Cloud observability in Cloud Monitoring. I like these kinds of posts that explain what goes into the cost of a specific service, and how to optimize given that knowledge.
[blog] Free Dolly: Introducing the World’s First Truly Open Instruction-Tuned LLM. Every generative AI use case won’t demand a massive LLM that’s expensive to train and serve. Databricks made their trained model available for research or commercial use.
[blog] SQL Maxis: Why We Ditched RabbitMQ And Replaced It With A Postgres Queue. You can use a database as a queuing system. You give up some things, and it likely won’t scale to certain cases, but it’s not a terrible idea for basic use cases.
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