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GCP: Complete Google Data Engineer and Cloud Architect Guide

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Rating3.6
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GCP: Complete Google Data Engineer and Cloud Architect Guide, available at $99.99, has an average rating of 3.6, with 230 lectures, 6 quizzes, based on 7352 reviews, and has 50325 subscribers.

You will learn about 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 ideal for individuals who are Yep! Anyone looking to use the Google Cloud Platform in their organizations or Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP or Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud or Yep! Anyone looking to build TensorFlow models and deploy them on the cloud It is particularly useful for Yep! Anyone looking to use the Google Cloud Platform in their organizations or Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP or Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud or Yep! Anyone looking to build TensorFlow models and deploy them on the cloud.

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

Summary

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

Price: $99.99

Average Rating: 3.6

Number of Lectures: 230

Number of Quizzes: 6

Number of Published Lectures: 226

Number of Published Quizzes: 5

Number of Curriculum Items: 236

Number of Published Curriculum Objects: 231

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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

Target Audiences

  • 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

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)

Course Curriculum

Chapter 1: You, This Course and Us

Lecture 1: You, This Course and Us

Lecture 2: Course Materials

Chapter 2: Introduction

Lecture 1: Theory, Practice and Tests

Lecture 2: Lab: Setting Up A GCP Account

Lecture 3: Lab: Using The Cloud Shell

Lecture 4: Important! Delete unused GCP projects/instances

Chapter 3: Compute

Lecture 1: About this section

Lecture 2: Compute Options

Lecture 3: Google Compute Engine (GCE)

Lecture 4: Lab: Creating a VM Instance

Lecture 5: More GCE

Lecture 6: Lab: Editing a VM Instance

Lecture 7: Lab: Creating a VM Instance Using The Command Line

Lecture 8: Lab: Creating And Attaching A Persistent Disk

Lecture 9: Google Container Engine – Kubernetes (GKE)

Lecture 10: More GKE

Lecture 11: Lab: Creating A Kubernetes Cluster And Deploying A WordPress Container

Lecture 12: App Engine

Lecture 13: Contrasting App Engine, Compute Engine and Container Engine

Lecture 14: Lab: Deploy And Run An App Engine App

Chapter 4: Storage

Lecture 1: About this section

Lecture 2: Storage Options

Lecture 3: Quick Take

Lecture 4: Cloud Storage

Lecture 5: Lab: Working With Cloud Storage Buckets

Lecture 6: Lab: Bucket And Object Permissions

Lecture 7: Lab: Life cycle Management On Buckets

Lecture 8: Fix for AccessDeniedException: 403 Insufficient Permission

Lecture 9: Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage

Lecture 10: Transfer Service

Lecture 11: Lab: Migrating Data Using The Transfer Service

Lecture 12: gcloud init

Lecture 13: Lab: Cloud Storage ACLs and API access with Service Account

Lecture 14: Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management

Lecture 15: Lab: Cloud Storage Versioning, Directory Sync

Chapter 5: Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

Lecture 1: About this section

Lecture 2: Cloud SQL

Lecture 3: Lab: Creating A Cloud SQL Instance

Lecture 4: Lab: Running Commands On Cloud SQL Instance

Lecture 5: Lab: Bulk Loading Data Into Cloud SQL Tables

Lecture 6: Cloud Spanner

Lecture 7: More Cloud Spanner

Lecture 8: Lab: Working With Cloud Spanner

Lecture 9: Important! Delete unused GCP projects/instances

Chapter 6: Hadoop Pre-reqs and Context

Lecture 1: Hadoop Pre-reqs and Context

Chapter 7: BigTable ~ HBase = Columnar Store

Lecture 1: About this section

Lecture 2: BigTable Intro

Lecture 3: Columnar Store

Lecture 4: Denormalised

Lecture 5: Column Families

Lecture 6: BigTable Performance

Lecture 7: Getting the HBase Prompt

Lecture 8: Lab: BigTable demo

Lecture 9: Important! Delete unused GCP projects/instances

Chapter 8: Datastore ~ Document Database

Lecture 1: About this section

Lecture 2: Datastore

Lecture 3: Lab: Datastore demo

Chapter 9: BigQuery ~ Hive ~ OLAP

Lecture 1: About this section

Lecture 2: BigQuery Intro

Lecture 3: BigQuery Advanced

Lecture 4: Lab: Loading CSV Data Into Big Query

Lecture 5: Lab: Running Queries On Big Query

Lecture 6: Lab: Loading JSON Data With Nested Tables

Lecture 7: Lab: Public Datasets In Big Query

Lecture 8: Lab: Using Big Query Via The Command Line

Lecture 9: Lab: Aggregations And Conditionals In Aggregations

Lecture 10: Lab: Subqueries And Joins

Lecture 11: Lab: Regular Expressions In Legacy SQL

Lecture 12: Lab: Using The With Statement For SubQueries

Chapter 10: Dataflow ~ Apache Beam

Lecture 1: About this section

Lecture 2: Data Flow Intro

Lecture 3: Apache Beam

Lecture 4: Lab: Running A Python Data flow Program

Lecture 5: Lab: Running A Java Data flow Program

Lecture 6: Lab: Implementing Word Count In Dataflow Java

Lecture 7: Lab: Executing The Word Count Dataflow

Lecture 8: Lab: Executing MapReduce In Dataflow In Python

Lecture 9: Lab: Executing MapReduce In Dataflow In Java

Lecture 10: Lab: Dataflow With Big Query As Source And Side Inputs

Lecture 11: Lab: Dataflow With Big Query As Source And Side Inputs 2

Chapter 11: Dataproc ~ Managed Hadoop

Lecture 1: About this section

Lecture 2: Data Proc

Lecture 3: Lab: Creating And Managing A Dataproc Cluster

Lecture 4: Lab: Creating A Firewall Rule To Access Dataproc

Lecture 5: Lab: Running A PySpark Job On Dataproc

Lecture 6: Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc

Lecture 7: Lab: Submitting A Spark Jar To Dataproc

Instructors

  • Loony Corn
    Loony Corn
    An ex-Google, Stanford and Flipkart team

Rating Distribution

  • 1 stars: 170 votes
  • 2 stars: 210 votes
  • 3 stars: 1047 votes
  • 4 stars: 2821 votes
  • 5 stars: 3103 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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