GCP: Complete Google Data Engineer and Cloud Architect Guide
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GCP: Complete Google Data Engineer and Cloud Architect Guide

The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop
Bestselling
4.4 (44 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
698 students enrolled
Created by Loony Corn
Last updated 8/2017
English
Current price: $10 Original price: $50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 22.5 hours on-demand video
  • 2 Articles
  • 45 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Deploy Managed Hadoop apps on the Google Cloud
  • Build deep learning models on the cloud using TensorFlow
  • Make informed decisions about Containers, VMs and AppEngine
  • Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub
View Curriculum
Requirements
  • Basic understanding of technology - superficial exposure to Hadoop is enough
Description

This course is a really comprehensive guide to the Google Cloud Platform - it has ~20 hours of content and ~60 demos.

The Google Cloud Platform is not currently the most popular cloud offering out there - that's AWS of course - but it is possibly the best cloud offering for high-end machine learning applications. That's because TensorFlow, the super-popular deep learning technology is also from Google.

What's Included:

  • Certification stuff - Covers pretty much all of the material you ought to need to get past the Google Data Engineer and Cloud Architect certification tests
  • Compute and Storage - AppEngine, Container Enginer (aka Kubernetes) and Compute Engine
  • Big Data and Managed Hadoop - Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub 
  • TensorFlow on the Cloud - what neural networks and deep learning really are, how neurons work and how neural networks are trained.
  • DevOps stuff - StackDriver logging, monitoring, cloud deployment manager
  • Security - Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
  • Networking - Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
  • Hadoop Foundations: A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)


Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!



Who is the target audience?
  • Yep! Anyone looking to use the Google Cloud Platform in their organizations
  • Yep! Anyone looking to clear the Google Data Engineer or Cloud Architect certification tests
  • Yep! Anyone looking to build TensorFlow models and deploy them on the cloud
Students Who Viewed This Course Also Viewed
Curriculum For This Course
167 Lectures
22:42:10
+
You, This Course and Us
3 Lectures 02:31

Important! Delete unused GCP projects/instances
00:20

Course Materials
00:10
+
Introduction
7 Lectures 01:05:34

Why Cloud?
09:42


On-premise, Colocation or Cloud?
10:05

Introducing the Google Cloud Platform
13:20


Lab: Using The Cloud Shell
06:01

GCP Introduction
9 questions
+
Compute Choices
13 Lectures 01:32:14
Compute Options
09:16

Google Compute Engine (GCE)
07:38

More GCE
08:12


Lab: Editing a VM Instance
04:45

Lab: Creating a VM Instance Using The Command Line
04:43

Lab: Creating And Attaching A Persistent Disk
04:00

Google Container Engine - Kubernetes (GKE)
10:33

More GKE
09:54

Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container
06:55

App Engine
06:48

Contrasting App Engine, Compute Engine and Container Engine
06:02

Lab: Deploy And Run An App Engine App
07:29

Compute
21 questions
+
Storage
9 Lectures 01:06:16
Storage Options
09:48

Quick Take
13:41

Cloud Storage
10:37

Lab: Working With Cloud Storage Buckets
05:25

Lab: Bucket And Object Permissions
03:52

Lab: Life cycle Management On Buckets
05:06

Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage
07:09


Lab: Migrating Data Using The Transfer Service
05:32
+
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
7 Lectures 54:46
Cloud SQL
07:40

Lab: Creating A Cloud SQL Instance
07:54

Lab: Running Commands On Cloud SQL Instance
06:31

Lab: Bulk Loading Data Into Cloud SQL Tables
09:09

Cloud Spanner
07:25

More Cloud Spanner
09:18

Lab: Working With Cloud Spanner
06:49
+
The Hadoop Ecosystem
17 Lectures 02:10:44
Introducing the Hadoop Ecosystem
01:34

Hadoop
09:42

HDFS
10:55

MapReduce
10:34

Yarn
05:29

Hive
07:19

Hive vs. RDBMS
07:10

HQL vs. SQL
07:35

OLAP in Hive
07:34

Windowing Hive
08:22

Pig
08:03

More Pig
06:38

Spark
08:54

More Spark
11:45

Streams Intro
07:44

Microbatches
05:40

Window Types
05:46

Hadoop Ecosystem
6 questions
+
BigTable ~ HBase = Columnar Store
6 Lectures 54:18

Columnar Store
08:12

Denormalised
09:02

Column Families
08:09

BigTable Performance
13:19

Lab: BigTable demo
07:39
+
Datastore ~ Document Database
2 Lectures 20:52
Datastore
14:10

Lab: Datastore demo
06:42
+
BigQuery ~ Hive ~ OLAP
11 Lectures 01:30:56
BigQuery Intro
11:03

BigQuery Advanced
09:59


Lab: Running Queries On Big Query
05:26

Lab: Loading JSON Data With Nested Tables
07:28

Lab: Public Datasets In Big Query
08:16

Lab: Using Big Query Via The Command Line
07:45

Lab: Aggregations And Conditionals In Aggregations
09:51

Lab: Subqueries And Joins
05:44

Lab: Regular Expressions In Legacy SQL
05:36

Lab: Using The With Statement For SubQueries
10:45
+
Dataflow ~ Apache Beam
10 Lectures 01:35:33
Data Flow Intro
11:04

Apache Beam
03:42

Lab: Running A Python Data flow Program
12:56

Lab: Running A Java Data flow Program
13:42

Lab: Implementing Word Count In Dataflow Java
11:17

Lab: Executing The Word Count Dataflow
04:37

Lab: Executing MapReduce In Dataflow In Python
09:49

Lab: Executing MapReduce In Dataflow In Java
06:08

Lab: Dataflow With Big Query As Source And Side Inputs
15:50

Lab: Dataflow With Big Query As Source And Side Inputs 2
06:28
8 More Sections
About the Instructor
Loony Corn
4.3 Average rating
4,985 Reviews
38,989 Students
77 Courses
An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  working in tech, in the Bay Area, New York, Singapore and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)