Vaquar Khan vaquarkhan

Coursera Specializations and Courses

Architecting with Google Kubernetes Engine Specialization

Implement solutions using Google Kubernetes Engine, or GKE, including building, scheduling, load balancing, and monitoring workloads, as well as providing for discovery of services, managing role-based access control and security, and providing persistent storage to these applications.

ECS Best Practices

Understand and check Service Quota of ECS/Fargate and other related services
Cluster
- Use IaC for setting up resources eg. CloudFormation, AWS CDK, Terraform
  - Use copilot for simple setup
  - CloudFormation reference architecture template
    - https://github.com/nathanpeck/ecs-cloudformation
    - https://github.com/awslabs/aws-cloudformation-templates/tree/master/aws/services/ECS
CDK for ECS: blog

Kubernetes Cheat Sheet

A cheat sheet for Kubernetes commands.

Kubectl Alias

Linux

alias k=kubectl

##Google Interview Questions: Product Marketing Manager

Why do you want to join Google? -- Because I want to create tools for others to learn, for free. I didn't have a lot of money when growing up so I didn't get access to the same books, computers and resources that others had which caused money, I want to help ensure that others can learn on the same playing field regardless of their families wealth status or location.
What do you know about Google’s product and technology? -- A lot actually, I am a beta tester for numerous products, I use most of the Google tools such as: Search, Gmaill, Drive, Reader, Calendar, G+, YouTube, Web Master Tools, Keyword tools, Analytics etc.
If you are Product Manager for Google’s Adwords, how do you plan to market this?
What would you say during an AdWords or AdSense product seminar?
Who are Google’s competitors, and how does Google compete with them? -- Google competes on numerous fields: --- Search: Baidu, Bing, Duck Duck Go

Cassandra Notes

Introduction

Apache Cassandra is an open source, distributed database management system. Cassandra is designed to handle large amounts of data across many commodity servers. Cassandra uses a query language named CQL.

Cassandra's data model is a partitioned row store; Cassandra combines elements of key-value stores and tabular/columnar databases. Like a relational database, Cassandra stores data in tables, called column families, that have defined columns and associated data types. Each row in a column family is uniquely identified by a key. Each row has multiple columns, each of which has a timestamp, name, and value. Unlike a relational database, each row in a column family does not need to have the same set of columns. At any time, a column may be added to one or more rows. If this explanation is unclear, you might think of column families instead as sets of key-value pairs, in which the values are nested sets of key-value pairs.

The following depicts two rows of a column-family fro

import boto3

client = boto3.client('glue')

response = client.create_crawler(
    Name='SalesCSVCrawler',
    Role='AWSGlueServiceRoleDefault',
    DatabaseName='sales-cvs',
    Description='Crawler for generated Sales schema',

	// EMR Applications: Spark, Hive
	// spark-shell --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4 --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer' --conf "spark.sql.hive.convertMetastoreParquet=false"

	// EMR configuration:
	// {
	// "classification": "hive-site",
	// "properties": {
	// "hive.metastore.client.factory.class": "com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory",
	// "hive.metastore.schema.verification": "false"
	// }

	/**
	* Definition for a binary tree node.
	* public class TreeNode {
	* int val;
	* TreeNode left;
	* TreeNode right;
	* TreeNode(int x) { val = x; }
	* }
	*/
	/*

	################################### Keras2DML: Parallely training neural network with SystemML#######################################
	import tensorflow as tf
	import keras
	from keras.models import Sequential
	from keras.layers import Input, Dense, Conv1D, Conv2D, MaxPooling2D, Dropout,Flatten
	from keras import backend as K
	from keras.models import Model
	import numpy as np
	import matplotlib.pyplot as plt

	package com.issue.chaos.monkey.demo;

	import org.springframework.boot.SpringApplication;
	import org.springframework.boot.autoconfigure.SpringBootApplication;

	@SpringBootApplication
	public class DemoApplication {

	public static void main(String[] args) {
	SpringApplication.run(DemoApplication.class, args);