GCP: Complete Google Data Engineer and Cloud Architect Guide

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166 Lessons (22h)

  • You, This Course and Us
    You, This Course and Us2:02
    Course Materials
  • Introduction
    Theory, Practice and Tests10:28
    Why Cloud?9:45
    Hadoop and Distributed Computing9:03
    On-premise, Colocation or Cloud?10:07
    Introducing the Google Cloud Platform13:22
    Lab: Setting Up A GCP Account6:59
    Lab: Using The Cloud Shell6:01
  • Compute Choices
    Compute Options9:18
    Google Compute Engine (GCE)7:40
    More GCE8:14
    Lab: Creating a VM Instance5:59
    Lab: Editing a VM Instance4:45
    Lab: Creating a VM Instance Using The Command Line4:43
    Lab: Creating And Attaching A Persistent Disk4:00
    Google Container Engine - Kubernetes (GKE)10:35
    More GKE9:56
    Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container6:55
    App Engine6:50
    Contrasting App Engine, Compute Engine and Container Engine6:05
    Lab: Deploy And Run An App Engine App7:29
  • Storage
    Storage Options9:50
    Quick Take13:43
    Cloud Storage10:39
    Lab: Working With Cloud Storage Buckets5:25
    Lab: Bucket And Object Permissions3:52
    Lab: Life cycle Management On Buckets5:06
    Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage7:09
    Transfer Service5:09
    Lab: Migrating Data Using The Transfer Service5:32
  • Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
    Cloud SQL7:42
    Lab: Creating A Cloud SQL Instance7:54
    Lab: Running Commands On Cloud SQL Instance6:31
    Lab: Bulk Loading Data Into Cloud SQL Tables9:09
    Cloud Spanner7:27
    More Cloud Spanner9:20
    Lab: Working With Cloud Spanner6:49
  • The Hadoop Ecosystem
    Introducing the Hadoop Ecosystem1:35
    Hadoop9:45
    HDFS10:55
    MapReduce10:34
    Yarn5:29
    Hive7:19
    Hive v RDBMS7:10
    HQL SQL7:38
    OLAP in Hive7:36
    Windowing Hive8:22
    Pig8:04
    More Pig6:38
    Spark8:56
    More Spark11:45
    Streams Intro7:44
    Microbatches5:42
    Window Types5:48
  • BigTable ~ HBase = Columnar Store
    BigTable Intro7:59
    Columnar Store8:14
    Denormalised9:04
    Column Families8:12
    BigTable Performance13:21
    Lab: BigTable demo7:39
  • Datastore ~ Document Database
    Datastore14:12
    Lab: Datastore demo6:42
  • BigQuery ~ Hive ~ OLAP
    BigQuery Intro11:03
    BigQuery Advanced9:59
    Lab: Loading CSV Data Into Big Query9:03
    Lab: Running Queries On Big Query5:26
    Lab: Loading JSON Data With Nested Tables7:28
    Lab: Public Datasets In Big Query8:16
    Lab: Using Big Query Via The Command Line7:45
    Lab: Aggregations And Conditionals In Aggregations9:51
    Lab: Subqueries And Joins5:44
    Lab: Regular Expressions In Legacy SQL5:36
    Lab: Using The With Statement For SubQueries10:45
  • Dataflow ~ Apache Beam
    Data Flow Intro11:06
    Apache Beam3:42
    Lab: Running A Python Data flow Program12:56
    Lab: Running A Java Data flow Program13:42
    Lab: Implementing Word Count In Dataflow Java11:17
    Lab: Executing The Word Count Dataflow4:37
    Lab: Executing MapReduce In Dataflow In Python9:50
    Lab: Executing MapReduce In Dataflow In Java6:08
    Lab: Dataflow With Big Query As Source And Side Inputs15:50
    Lab: Dataflow With Big Query As Source And Side Inputs 26:28
  • Dataproc ~ Managed Hadoop
    Data Proc8:30
    Lab: Creating And Managing A Dataproc Cluster8:11
    Lab: Creating A Firewall Rule To Access Dataproc8:25
    Lab: Running A PySpark Job On Dataproc7:39
    Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc8:44
    Lab: Submitting A Spark Jar To Dataproc2:10
    Lab: Working With Dataproc Using The Gcloud CLI8:19
  • Pub/Sub for Streaming
    Pub Sub8:25
    Lab: Working With Pubsub On The Command Line5:35
    Lab: Working With PubSub Using The Web Console4:39
    Lab: Setting Up A Pubsub Publisher Using The Python Library5:52
    Lab: Setting Up A Pubsub Subscriber Using The Python Library4:08
    Lab: Publishing Streaming Data Into Pubsub8:18
    Lab: Reading Streaming Data From PubSub And Writing To BigQuery10:14
    Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery5:54
    Lab: Pubsub Source BigQuery Sink10:20
  • Datalab ~ Jupyter
    Data Lab3:01
    Lab: Creating And Working On A Datalab Instance10:29
    Lab: Importing And Exporting Data Using Datalab12:14
    Lab: Using The Charting API In Datalab6:43
  • TensorFlow and Machine Learning
    Introducing Machine Learning8:06
    Representation Learning10:29
    NN Introduced7:37
    Introducing TF7:18
    Lab: Simple Math Operations8:46
    Computation Graph10:19
    Tensors9:04
    Lab: Tensors5:03
    Linear Regression Intro9:59
    Placeholders and Variables8:46
    Lab: Placeholders6:36
    Lab: Variables7:49
    Lab: Linear Regression with Made-up Data4:52
    Image Processing8:07
    Images As Tensors8:18
    Lab: Reading and Working with Images8:05
    Lab: Image Transformations6:37
    Introducing MNIST4:15
    K-Nearest Neigbors as Unsupervised Learning7:44
    One-hot Notation and L1 Distance7:31
    Steps in the K-Nearest-Neighbors Implementation9:34
    Lab: K-Nearest-Neighbors14:14
    Learning Algorithm11:00
    Individual Neuron9:54
    Learning Regression7:53
    Learning XOR10:29
    XOR Trained11:13
  • Regression in TensorFlow
    Lab: Access Data from Yahoo Finance2:49
    Non TensorFlow Regression8:07
    Lab: Linear Regression - Setting Up a Baseline11:18
    Gradient Descent9:58
    Lab: Linear Regression14:42
    Lab: Multiple Regression in TensorFlow9:15
    Logistic Regression Introduced10:18
    Linear Classification5:27
    Lab: Logistic Regression - Setting Up a Baseline7:33
    Logit8:35
    Softmax11:57
    Argmax12:15
    Lab: Logistic Regression16:56
    Estimators4:12
    Lab: Linear Regression using Estimators7:49
    Lab: Logistic Regression using Estimators4:54
  • Vision, Translate, NLP and Speech: Trained ML APIs
    Lab: Taxicab Prediction - Setting up the dataset14:38
    Lab: Taxicab Prediction - Training and Running the model11:22
    Lab: The Vision, Translate, NLP and Speech API10:53
    Lab: The Vision API for Label and Landmark Detection7:00
  • Networking
    Virtual Private Clouds7:05
    VPC and Firewalls9:27
    XPC or Shared VPC7:41
    VPN8:51
    Types of Load Balancing6:46
    Proxy and Pass-through load balancing9:51
    Internal load balancing6:03
  • Ops and Security
    StackDriver12:10
    StackDriver Logging7:41
    Cloud Deployment Manager6:07
    Cloud Endpoints3:49
    Security and Service Accounts7:46
    OAuth and End-user accounts8:33
    Identity and Access Management8:33
    Data Protection12:04

Discuss the Google Cloud for ML with TensorFlow & Big Data with Managed Hadoop

L
Loonycorn

Instructor

Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Description

The Google Cloud Platform is not the most popular cloud offering out there (hello AWS!) but it may be the best cloud offering for high-end machine learning applications. That's because TensorFlow, the extremely popular deep learning technology is also from Google. This comprehensive guide to TensorFlow and the Google Cloud Platform will help put you on certification track to become a Google Data Engineer or Cloud Architect.

  • Access 166 lectures & 22 hours of content 24/7
  • Cover the material you need to pass Google Data Engineer & Cloud Architect certification exams
  • Explore AppEngine, Kubernetes, & Compute Engine
  • Discuss Big Data & Managed Hadoop w/ Dataproc, Dataflow, BigTable, BigQuery, & more
  • Learn what neural networks & deep learning are, how neurons work, & how neural networks are trained
  • Understand DevOps principles like StackDrive logging, monitoring, & cloud deployment management
  • Discover security, networking, & Hadoop foundations

Specs

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.