Read some good case studies today, as well as principles and patterns that can help you build a good technology team. Check it out!
[blog] How to test technical documentation usability. You’ve published docs. Amazing! Are they any good? Here’s a good post on what you should look for when assessing the quality of your documentation.
[blog] EA Principles Series: Steward our Technology Portfolio and Minimize Long-Term Technical Debt. At plenty of companies, the enterprise architecture team has the reputation as being aloof, dogmatic, and impractical. That’s not the case at Chick-fil-A. Here’s another great post from Brian that explains how one core principle looks out for their product teams and IT health.
[blog] 3 common DevOps antipatterns and cloud native strategies that can help. Good stuff on the GitHub blog. Relates, in part, to things I’ve advised in recent Pluralsight courses.
[blog] A reference architecture for transforming insurance claims processing with Google Cloud. We do a lot of work with insurance companies, and it yields reference architectures like this one. Even if you’re not in this industry, you might get inspiration from how others solve common problems.
[article] Poisoned Lolip0p PyPI Packages. That’s a VERY geeky headline, but the main point here is that “official” repositories of application packages are at risk of compromise, and you likely want an intentional strategy for how you source, store, and scan the outside dependencies that go into your apps.
[article] What Great Sponsors Do Differently. This is something I’m still working on, and hopefully getting better at. You can make a big impact on someone by not just mentoring them, but sponsoring them.
[blog] Effective Failure Handling in Flipkart’s Message Bus. Here’s a deep dive into the message bus architecture of the giant Indian ecommerce company.
[blog] A journey from App Engine to Cloud Run: Adopting containers and reducing infrastructure costs by 70 percent. Move from one service to another in the same cloud, and save money? We should talk more about those scenarios.
[blog] How to do multivariate time series forecasting in BigQuery ML. Even if you don’t use BigQuery, you’ll probably learn something about forecasting by reading this post. I sure did.
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