so0k

resource "aws_budgets_budget" "cloudwatch" {
  provider          = "aws.billing"
  name              = "budget-cloudwatch-monthly"
  budget_type       = "COST"
  limit_amount      = "1000"
  limit_unit        = "USD"
  time_period_end   = "2087-06-15_00:00"
  time_period_start = "2017-07-01_00:00"
  time_unit         = "MONTHLY"

SarasaGunawardhana

#!/bin/bash
## Generic installation on all nodes

sysctl -w net.ipv4.ip_forward=1
sed -i 's/#net.ipv4.ip_forward=1/net.ipv4.ip_forward=1/g' /etc/sysctl.conf 
sudo sysctl -p /etc/sysctl.conf

swapoff -a
sed -i '2s/^/#/' /etc/fstab 

hexfusion

# sniff hypervisor
 dmesg | grep Hypervisor  
[    0.000000] Hypervisor detected: KVM


# get key in bytes
etcdctl get $key -w fields | grep -oP "(?<=Value\" : \").*" | wc -c

# defrag status in MB
cat etcd_info/endpoint_status.json | jq '(.[0].Status.dbSize - .[0].Status.dbSizeInUse)/1000/1000' 

redmcg

#!/usr/bin/env bash

NODESAPI=/api/v1/nodes

function getNodes() {
  kubectl get --raw $NODESAPI | jq -r '.items[].metadata.name'
}

function getPVCs() {
  jq -s '[flatten | .[].pods[].volume[]? | select(has("pvcRef")) | '\

moosh3

version: '3.3'

services:
  prometheus:
    container_name: prometheus
    image: prom/prometheus:v2.7.2
    container_name: prometheus
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
      - '--web.console.libraries=/etc/prometheus/console_libraries'

moosh3

version: '3.6'

services:
  elasticsearch: &service
    container_name: elasticsearch
    image: docker.elastic.co/elasticsearch/elasticsearch:6.7.1
    # deploy:
    #   mode: global
    #   restart_policy:
    #     condition: on-failure

j-mprabhakaran

The access pattern fits Nearline storage class requirements and Nearline is a more cost-effective storage approach than Multi-Regional. 
The object lifecycle management policy to delete data is correct versus changing the storage class to Coldline.

Google Cloud Storage supports Multi-Regional buckets that synchronize data across regions automatically.
Google Cloud SQL instances are deployed within a single region.
Google Cloud Bigtable data is stored within a single region.
Google Cloud Datastore is stored within a single region.

Using a shared VPC allows each team to individually manage their own application resources, while enabling each application to communicate 
between each other securely over RFC1918 address space.

j-mprabhakaran

GCP Cloud Architect - Part 3
Case Studies 
Refreshed Nov 9th 2018; JencoMart completely dropped
Overview - 3 case studies; 40-50% on the exam; Question on one side, Case study on other side
Layout of Case study - 1.Company Overview 2.Solution concept - current goal 3.Existing Technical Env 4.Requirements(Tech/Business) 5.Executive statement 
Mountkirk Games
Dress4Win
TerramEarth

Mountkirk Games

j-mprabhakaran

Dataflow lifecycle
migration concerns from migrating from on-premises over into google cloud
code snippet to troubleshoot and diagnose

Part 2 - Hands-on with tools

Role of Cloud Architect
plans, designs and builds the infrastructure for an org to host their workload on GCP; able to plan to scale;
scalability and automation

j-mprabhakaran

Google Certified Professional Cloud Architect - Part 1
======================================================
GCP Overview
Google's suite of cloud computing services; run on same infra and network as google 
Compute -> App Engine, Container Engine, Compute Engine 
Storage -> Bigtable, Cloud Storage, Cloud SQL, Cloud Datastore
Big Data -> BigQuery, Pub/Sub, Dataflow, Dataproc, Datalab
Machine Learning -> Vision API, Machine Learning, Speech API, Translation API 
https://cloud.google.com/pricing
Per second pricing for instances;Private Global Fiber network;Live migration of VMs;Better performance;Industry leading security;access to innovative resources(Big data,ML)