GCP: Complete Google Data Engineer and Cloud Architect Guide

You_ This Course and Us
1 You_ This Course and Us
2 Important! Delete unused GCP projects_instances
3 Course Materials

Introduction
4 Theory_ Practice and Tests
5 Why Cloud_
6 Hadoop and Distributed Computing
7 On-premise_ Colocation or Cloud_
8 Introducing the Google Cloud Platform
9 Lab_ Setting Up A GCP Account
10 Lab_ Using The Cloud Shell

Compute Choices
11 Compute Options
12 Google Compute Engine (GCE)
13 More GCE
14 Lab_ Creating a VM Instance
15 Lab_ Editing a VM Instance
16 Lab_ Creating a VM Instance Using The Command Line
17 Lab_ Creating And Attaching A Persistent Disk
18 Google Container Engine – Kubernetes (GKE)
19 More GKE
20 Lab_ Creating A Kubernetes Cluster And Deploying A WordPress Container
21 App Engine
22 Contrasting App Engine_ Compute Engine and Container Engine
23 Lab_ Deploy And Run An App Engine App

Storage
24 Storage Options
25 Quick Take
26 Cloud Storage
27 Lab_ Working With Cloud Storage Buckets
28 Lab_ Bucket And Object Permissions
29 Lab_ Life cycle Management On Buckets
30 Lab_ Running A Program On a VM Instance And Storing Results on Cloud Storage
31 Transfer Service
32 Lab_ Migrating Data Using The Transfer Service

Cloud SQL_ Cloud Spanner ~ OLTP ~ RDBMS
33 Cloud SQL
34 Lab_ Creating A Cloud SQL Instance
35 Lab_ Running Commands On Cloud SQL Instance
36 Lab_ Bulk Loading Data Into Cloud SQL Tables
37 Cloud Spanner
38 More Cloud Spanner
39 Important! Delete unused GCP projects_instances
40 Lab_ Working With Cloud Spanner

Hadoop Pre-reqs and Context
41 Hadoop Pre-reqs and Context

BigTable ~ HBase = Columnar Store
42 BigTable Intro
43 Columnar Store
44 Denormalised
45 Column Families
46 BigTable Performance
47 Important! Delete unused GCP projects_instances
48 Lab_ BigTable demo

Datastore ~ Document Database
49 Datastore
50 Lab_ Datastore demo

BigQuery ~ Hive ~ OLAP
51 BigQuery Intro
52 BigQuery Advanced
53 Lab_ Loading CSV Data Into Big Query
54 Lab_ Running Queries On Big Query
55 Lab_ Loading JSON Data With Nested Tables
56 Lab_ Public Datasets In Big Query
57 Lab_ Using Big Query Via The Command Line
58 Lab_ Aggregations And Conditionals In Aggregations
59 Lab_ Subqueries And Joins
60 Lab_ Regular Expressions In Legacy SQL
61 Lab_ Using The With Statement For SubQueries

Dataflow ~ Apache Beam
62 Data Flow Intro
63 Apache Beam
64 Lab_ Running A Python Data flow Program
65 Lab_ Running A Java Data flow Program
66 Lab_ Implementing Word Count In Dataflow Java
67 Lab_ Executing The Word Count Dataflow
68 Lab_ Executing MapReduce In Dataflow In Python
69 Lab_ Executing MapReduce In Dataflow In Java
70 Lab_ Dataflow With Big Query As Source And Side Inputs
71 Lab_ Dataflow With Big Query As Source And Side Inputs 2

Dataproc ~ Managed Hadoop
72 Data Proc
73 Lab_ Creating And Managing A Dataproc Cluster
74 Lab_ Creating A Firewall Rule To Access Dataproc
75 Lab_ Running A PySpark Job On Dataproc
76 Lab_ Running The PySpark REPL Shell And Pig Scripts On Dataproc
77 Lab_ Submitting A Spark Jar To Dataproc
78 Lab_ Working With Dataproc Using The GCloud CLI

Pub_Sub for Streaming
79 Pub Sub
80 Lab_ Working With Pubsub On The Command Line
81 Lab_ Working With PubSub Using The Web Console
82 Lab_ Setting Up A Pubsub Publisher Using The Python Library
83 Lab_ Setting Up A Pubsub Subscriber Using The Python Library
84 Lab_ Publishing Streaming Data Into Pubsub
85 Lab_ Reading Streaming Data From PubSub And Writing To BigQuery
86 Lab_ Executing A Pipeline To Read Streaming Data And Write To BigQuery
87 Lab_ Pubsub Source BigQuery Sink

Datalab ~ Jupyter
88 Data Lab
89 Lab_ Creating And Working On A Datalab Instance
90 Lab_ Importing And Exporting Data Using Datalab
91 Lab_ Using The Charting API In Datalab

TensorFlow and Machine Learning
92 Introducing Machine Learning
93 Representation Learning
94 NN Introduced
95 Introducing TF
96 Lab_ Simple Math Operations
97 Computation Graph
98 Tensors
99 Lab_ Tensors
100 Linear Regression Intro
101 Placeholders and Variables
102 Lab_ Placeholders
103 Lab_ Variables
104 Lab_ Linear Regression with Made-up Data
105 Image Processing
106 Images As Tensors
107 Lab_ Reading and Working with Images
108 Lab_ Image Transformations
109 Introducing MNIST
110 K-Nearest Neigbors as Unsupervised Learning
111 One-hot Notation and L1 Distance
112 Steps in the K-Nearest-Neighbors Implementation
113 Lab_ K-Nearest-Neighbors
114 Learning Algorithm
115 Individual Neuron
116 Learning Regression
117 Learning XOR
118 XOR Trained

Regression in TensorFlow
119 Lab_ Access Data from Yahoo Finance
120 Non TensorFlow Regression
121 Lab_ Linear Regression – Setting Up a Baseline
122 Gradient Descent
123 Lab_ Linear Regression
124 Lab_ Multiple Regression in TensorFlow
125 Logistic Regression Introduced
126 Linear Classification
127 Lab_ Logistic Regression – Setting Up a Baseline
128 Logit
129 Softmax
130 Argmax
131 Lab_ Logistic Regression
132 Estimators
133 Lab_ Linear Regression using Estimators
134 Lab_ Logistic Regression using Estimators

Vision_ Translate_ NLP and Speech_ Trained ML APIs
135 Lab_ Taxicab Prediction – Setting up the dataset
136 Lab_ Taxicab Prediction – Training and Running the model
137 Lab_ The Vision_ Translate_ NLP and Speech API
138 Lab_ The Vision API for Label and Landmark Detection

Networking
139 Virtual Private Clouds
140 VPC and Firewalls
141 XPC or Shared VPC
142 VPN
143 Types of Load Balancing
144 Proxy and Pass-through load balancing
145 Internal load balancing

Ops and Security
146 StackDriver
147 StackDriver Logging
148 Cloud Deployment Manager
149 Cloud Endpoints
150 Security and Service Accounts
151 OAuth and End-user accounts
152 Identity and Access Management
153 Data Protection

Appendix_ Hadoop Ecosystem
154 Introducing the Hadoop Ecosystem
155 Hadoop
156 HDFS
157 MapReduce
158 Yarn
159 Hive
160 Hive vs_ RDBMS
161 HQL vs_ SQL
162 OLAP in Hive
163 Windowing Hive
164 Pig
165 More Pig
166 Spark
167 More Spark
168 Streams Intro
169 Microbatches
170 Window Types