GCP: Complete Google Data Engineer and Cloud Architect Guide
31,859 students enrolled
GCP: Complete Google Data Engineer and Cloud Architect Guide
The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop
Bestseller
31,859 students enrolled
Created by
Loony Corn
Last updated 7/2018
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Current price: $64.99
Original price: $99.99
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This course includes
- 28 hours on-demand video
- 25 articles
- 48 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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What you'll 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
Course content
Expand all
226 lectures
27:51:50
+
–
Introduction
4 lectures
23:46
Lab: Using The Cloud Shell
06:01
Important! Delete unused GCP projects/instances
00:20
+
–
Compute
14 lectures
01:32:29
About this section
00:15
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
15 lectures
01:33:01
About this section
00:11
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
Fix for AccessDeniedException: 403 Insufficient Permission
00:30
Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage
07:09
Preview
05:06
Lab: Migrating Data Using The Transfer Service
05:32
gcloud init
00:05
Lab: Cloud Storage ACLs and API access with Service Account
07:49
Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management
09:27
Lab: Cloud Storage Versioning, Directory Sync
08:41
+
–
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
9 lectures
55:23
About this section
00:15
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
Just wanted to send along an important note for anyone learning a cloud technology like GCP - please be sure to delete your projects, instances and in general to free up your resources after you are done using them. Resources like BigTable, Cloud Spanner are pretty expensive - if you happen to create one, then forget to free it up, you could be hit with real sticker shock when you get your next invoice.
Just something important to keep in mind if you are new to using pay-as-you-go technologies:-)
00:21
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–
Hadoop Pre-reqs and Context
1 lecture
00:18
Hadoop Pre-reqs and Context
00:18
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–
BigTable ~ HBase = Columnar Store
9 lectures
55:10
About this section
00:15
Preview
07:57
Columnar Store
08:12
Denormalised
09:02
Column Families
08:09
BigTable Performance
13:19
Getting the HBase Prompt
00:15
Lab: BigTable demo
07:39
An important note for anyone learning a cloud technology like GCP - please be sure to delete your projects, instances and in general to free up your resources after you are done using them. Resources like BigTable, Cloud Spanner are pretty expensive - if you happen to create one, then forget to free it up, you could be hit with real sticker shock when you get your next invoice.
Just something important to keep in mind if you are new to using pay-as-you-go technologies:-)
00:21
+
–
Datastore ~ Document Database
3 lectures
21:07
About this section
00:15
Datastore
14:10
Lab: Datastore demo
06:42
Datastore
3 questions
+
–
BigQuery ~ Hive ~ OLAP
12 lectures
01:31:11
About this section
00:15
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
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–
Dataflow ~ Apache Beam
11 lectures
01:35:37
About this section
00:04
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
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 ~25 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:
- 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)
Who this course is for:
- Yep! Anyone looking to use the Google Cloud Platform in their organizations
- Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP
- Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud
- Yep! Anyone looking to build TensorFlow models and deploy them on the cloud
Featured review
Joy B
(
668 courses,
33 reviews
)
8 months ago
This Course is really Great.. This gives a meticulous focus on the topics with a sincere attitude..
The only reason I am putting 4.5 (not 5) is that I am not sure if this course is being updated per the Real Exam updates as of 2019.