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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
Development Software Testing Google Cloud Professional Data Engineer

GCP: Complete Google Data Engineer and Cloud Architect Guide

The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop
Bestseller
Rating: 4.2 out of 54.2 (5,747 ratings)
39,147 students
Created by Loony Corn
Last updated 7/2018
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

21 sections • 226 lectures • 27h 51m total length

  • Preview02:01
  • Preview00:11

  • Preview10:26
  • Preview06:59
  • Lab: Using The Cloud Shell
    06:01
  • Important! Delete unused GCP projects/instances
    00:20

  • About this section
    00:15
  • Compute Options
    09:16
  • Google Compute Engine (GCE)
    07:38
  • Preview05:59
  • 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

  • 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
  • Preview05:06
  • 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

  • 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
  • Important! Delete unused GCP projects/instances
    00:21

  • Hadoop Pre-reqs and Context
    00:18

  • About this section
    00:15
  • Preview07:57
  • 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
  • Important! Delete unused GCP projects/instances
    00:21

  • About this section
    00:15
  • Datastore
    14:10
  • Lab: Datastore demo
    06:42
  • Datastore
    3 questions

  • About this section
    00:15
  • BigQuery Intro
    11:03
  • BigQuery Advanced
    09:59
  • Preview09:03
  • 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

  • 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
Joy B
730 courses
33 reviews
Rating: 4.5 out of 5a year 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.

Instructor

Loony Corn
An ex-Google, Stanford and Flipkart team
Loony Corn
  • 4.2 Instructor Rating
  • 21,351 Reviews
  • 127,081 Students
  • 73 Courses

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 :-)

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