Carlos Aguni

Highly motivated self-taught IT analyst. Always learning and ready to explore new skills. An eternal apprentice.

September 2020 Interesting Reads

31 Aug 2020 » monthly-awesome
  • https://towardsdatascience.com/designing-data-intensive-applications-book-review-cc34ba1f90a7
  • https://levelup.gitconnected.com/dont-use-lambda-to-move-data-api-gateway-can-help-fe899df239e6
  • https://medium.com/@zaccharles/calculating-a-dynamodb-items-size-and-consumed-capacity-d1728942eb7c
  • Vmware Pallas
    • https://www.virtualizationhowto.com/2020/02/manage-esxi-hosts-without-vcenter-using-vmware-pallas/
    • https://flings.vmware.com/pallas#instructions
  • http://rachelbythebay.com/w/2020/08/14/jobs/
  • Vmware
    • Pyvmomi
      • Install
        • http://vmware.github.io/pyvmomi-community-samples/
      • samples
        • list vms
          • https://github.com/vmware/pyvmomi-community-samples/blob/master/samples/getallvms.py
        • exsi perf
          • https://github.com/vmware/pyvmomi-community-samples/blob/master/samples/esxi_perf_sample.py
    • Vmware exporter
      • https://github.com/pryorda/vmware_exporter
      • docker-compose
        • https://github.com/pryorda/vmware_exporter/blob/master/docker-compose.yml
  • YOW! 2018 Brendan Gregg - Cloud Performance Root Cause Analysis at Netflix #YOW
    • https://www.youtube.com/watch?v=tAY8PnfrS_k
    • Atlas: Metrics. 10% of netflix footprint
      • Stores metrics in-memory for faster graph pull
        • Pull out these graphics for these region, aplication, device.
        • For time range.
    • PICSOU: AWS Usage
      • Netflix cost
        • Understand cost and growth and target resources to where applications are growing
  • APCUPSD
    • https://jeff.mcfadden.io/posts/monitoring-an-apc-ups-with-a-raspberry-pi
      • http://www.anites.com/2013/09/monitoring-ups.html
  • AWS re:Invent 2017: Tooling Up for Efficiency: DIY Solutions @ Netflix (ABD319)
    • Serve every data related needs related to capacity planning
      • Own the investigation of trends, patterns and anomalies in core metrics
      • Suggest new data-driven approaches to existing workflows and goals
        • Don’t: waiting “Hey can you build this..”
        • Do: “Hey I’ve looked at this problem. Here’s a new solution for it”
      • Business-aware metric (streams clicks)
    • Success Criteria
      • Feedback from engineering teams (Also need buy in)
        • Regular use of our tools and insights
        • Raised awareness of their impact on efficiency
        • Pro-active engagement on efficiency projects
      • (Holy Grail) Embrace efficiency (success)
    • Hierarchy of needs:
      • Automation: Optimization and Machine Learning
      • Actionable Insights: Targeted alerts, summary emails and personalized dashboards
      • Deep Dives: Exploratory analyses and case studies
      • Transparency: Intuitive and Interactive Dashboards
  • https://fluidframework.com/
  • Python SNMP
    • https://technicalramblings.com/blog/setting-grafana-influxdb-telegraf-ups-monitoring-unraid/
  • https://medium.com/swlh/how-to-use-aws-codebuild-as-a-ci-for-your-python-project-82dd7dab7afb
  • https://screen-play.app/blog/qmake_to_cmake/

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