http://www.oreilly.com/programming/free/files/microservices-for-java-developers.pdf
http://www.oreilly.com/programming/free/files/microservices-for-java-developers.epub
http://www.oreilly.com/programming/free/files/microservices-for-java-developers.mobi
http://www.oreilly.com/programming/free/files/modern-java-ee-design-patterns.pdf
http://www.oreilly.com/programming/free/files/modern-java-ee-design-patterns.epub
http://www.oreilly.com/programming/free/files/modern-java-ee-design-patterns.mobi
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Making sense of Durable Functions blog post by Mikhail Shilkov
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Serverless on Azure vlog by Marc Duiker
Lvl 0 | Lvl 1 | Lvl 2 | Lvl 3 | Computer Science |
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[ ] | [ ] | [ ] | [ ] | data structures |
[ ] | [ ] | [ ] | [ ] | algorithms |
[ ] | [ ] | [ ] | [ ] | systems programming |
Lvl 0 | Lvl 1 | Lvl 2 | Lvl 3 | Software Engineering |
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[ ] | [ ] | [ ] | [ ] | source code version control |
[ ] | [ ] | [ ] | [ ] | build automation |
I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.
I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real
Solution for collecting, storing, visualizing and alerting on time-series data at scale. All components of the platform are designed to work together seamlessly.
- Telegraf: Collects time-series data from a variety of sources
- InfluxDB:
- Chronograf: Visualizes and graphs
- Kapacitor: Alerting, ETL and detects anomalies in time-series data
- Open Source - MIT
- Integrated - Data collection, storage, visualization and alerting
Driver: | |
-Write the code according to the navigator's specification | |
-Listen intently to the navigators instructions | |
-Ask questions wherever there is a lack of clarity | |
-Offer alternative solutions if you disagree with the navigator | |
-Where there is disagreement, defer to the navigator. If their idea fails, get to failure quickly and move on | |
-Make sure code is clean | |
-Own the computer / keyboard | |
-Ignore larger issues and focus on the task at hand | |
-Trust the navigator - ultimately the navigator has the final say in what is written |
I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.
I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real