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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
Instructor:
Loony Corn
31,850 students enrolled
English [Auto-generated] More
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

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)

You, This Course and Us

1
You, This Course and Us
2
Course Materials

Introduction

1
Theory, Practice and Tests
2
Lab: Setting Up A GCP Account
3
Lab: Using The Cloud Shell
4
Important! Delete unused GCP projects/instances

Compute

1
About this section
2
Compute Options
3
Google Compute Engine (GCE)
4
Lab: Creating a VM Instance
5
More GCE
6
Lab: Editing a VM Instance
7
Lab: Creating a VM Instance Using The Command Line
8
Lab: Creating And Attaching A Persistent Disk
9
Google Container Engine - Kubernetes (GKE)
10
More GKE
11
Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container
12
App Engine
13
Contrasting App Engine, Compute Engine and Container Engine
14
Lab: Deploy And Run An App Engine App
15
Compute

Storage

1
About this section
2
Storage Options
3
Quick Take
4
Cloud Storage
5
Lab: Working With Cloud Storage Buckets
6
Lab: Bucket And Object Permissions
7
Lab: Life cycle Management On Buckets
8
Fix for AccessDeniedException: 403 Insufficient Permission
9
Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage
10
Transfer Service
11
Lab: Migrating Data Using The Transfer Service
12
gcloud init
13
Lab: Cloud Storage ACLs and API access with Service Account
14
Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management
15
Lab: Cloud Storage Versioning, Directory Sync

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

1
About this section
2
Cloud SQL
3
Lab: Creating A Cloud SQL Instance
4
Lab: Running Commands On Cloud SQL Instance
5
Lab: Bulk Loading Data Into Cloud SQL Tables
6
Cloud Spanner
7
More Cloud Spanner
8
Lab: Working With Cloud Spanner
9
Important! Delete unused GCP projects/instances

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:-)

Hadoop Pre-reqs and Context

1
Hadoop Pre-reqs and Context

BigTable ~ HBase = Columnar Store

1
About this section
2
BigTable Intro
3
Columnar Store
4
Denormalised
5
Column Families
6
BigTable Performance
7
Getting the HBase Prompt
8
Lab: BigTable demo
9
Important! Delete unused GCP projects/instances

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:-)

Datastore ~ Document Database

1
About this section
2
Datastore
3
Lab: Datastore demo
4
Datastore

BigQuery ~ Hive ~ OLAP

1
About this section
2
BigQuery Intro
3
BigQuery Advanced
4
Lab: Loading CSV Data Into Big Query
5
Lab: Running Queries On Big Query
6
Lab: Loading JSON Data With Nested Tables
7
Lab: Public Datasets In Big Query
8
Lab: Using Big Query Via The Command Line
9
Lab: Aggregations And Conditionals In Aggregations
10
Lab: Subqueries And Joins
11
Lab: Regular Expressions In Legacy SQL
12
Lab: Using The With Statement For SubQueries

Dataflow ~ Apache Beam

1
About this section
2
Data Flow Intro
3
Apache Beam
4
Lab: Running A Python Data flow Program
5
Lab: Running A Java Data flow Program
6
Lab: Implementing Word Count In Dataflow Java
7
Lab: Executing The Word Count Dataflow
8
Lab: Executing MapReduce In Dataflow In Python
9
Lab: Executing MapReduce In Dataflow In Java
10
Lab: Dataflow With Big Query As Source And Side Inputs
11
Lab: Dataflow With Big Query As Source And Side Inputs 2

Dataproc ~ Managed Hadoop

1
About this section
2
Data Proc
3
Lab: Creating And Managing A Dataproc Cluster
4
Lab: Creating A Firewall Rule To Access Dataproc
5
Lab: Running A PySpark Job On Dataproc
6
Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc
7
Lab: Submitting A Spark Jar To Dataproc
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