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

GCP: Complete Google Data Engineer and Cloud Architect Guide

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
About this Course

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 is the target audience?

  • 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
Basic knowledge
  • Basic understanding of technology - superficial exposure to Hadoop is enough.
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
Curriculum
Lectures quantity: 227
Common duration: 28:37:32
You, This Course and Us
  • You, This Course and Us  
  • Course Materials  
Introduction
  • Theory, Practice and Tests  
  • Lab: Setting Up A GCP Account  
  • Why Cloud?  
  • Hadoop and Distributed Computing  
  • On-premise, Colocation or Cloud?  
  • Introducing the Google Cloud Platform  
  • Lab: Using The Cloud Shell  
  • Important! Delete unused GCP projects/instances  
Compute
  • About this section  
  • Compute Options  
  • Google Compute Engine (GCE)  
  • Lab: Creating a VM Instance  
  • More GCE  
  • Lab: Editing a VM Instance  
  • Lab: Creating a VM Instance Using The Command Line  
  • Lab: Creating And Attaching A Persistent Disk  
  • Google Container Engine - Kubernetes (GKE)  
  • More GKE  
  • Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container  
  • App Engine  
  • Contrasting App Engine, Compute Engine and Container Engine  
  • Lab: Deploy And Run An App Engine App  
Storage
  • Session 4 - About this section  
  • Storage Options  
  • Quick Take  
  • Cloud Storage  
  • Lab: Working With Cloud Storage Buckets  
  • Lab: Bucket And Object Permissions  
  • Lab: Life cycle Management On Buckets  
  • Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage  
  • Transfer Service  
  • Lab: Migrating Data Using The Transfer Service  
  • Lab: Cloud Storage ACLs and API access with Service Account  
  • Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management  
  • Lab: Cloud Storage Versioning, Directory Sync  
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
  • Session 5 - About this section  
  • Cloud SQL  
  • Lab: Creating A Cloud SQL Instance  
  • Lab: Running Commands On Cloud SQL Instance  
  • Lab: Bulk Loading Data Into Cloud SQL Tables  
  • Cloud Spanner  
  • More Cloud Spanner  
  • Lab: Working With Cloud Spanner  
  • Important! Delete unused GCP projects/instances - Cloud  

    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
  • Hadoop Pre-reqs and Context  
BigTable ~ HBase = Columnar Store
  • Session 7 - About this section  
  • BigTable Intro  
  • Columnar Store  
  • Denormalised  
  • Column Families  
  • BigTable Performance  
  • Lab: BigTable demo  
  • Important! Delete unused GCP projects/instances - BigTable  

    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
  • Session 8 - About this section  
  • Datastore  
  • Lab: Datastore demo  
BigQuery ~ Hive ~ OLAP
  • Session 9 - About this section  
  • BigQuery Intro  
  • BigQuery Advanced  
  • Lab: Loading CSV Data Into Big Query  
  • Lab: Running Queries On Big Query  
  • Lab: Loading JSON Data With Nested Tables  
  • Lab: Public Datasets In Big Query  
  • Lab: Using Big Query Via The Command Line  
  • Lab: Aggregations And Conditionals In Aggregations  
  • Lab: Subqueries And Joins  
  • Lab: Regular Expressions In Legacy SQL  
  • Lab: Using The With Statement For SubQueries  
Dataflow ~ Apache Beam
  • Session 10 - About this section  
  • Data Flow Intro  
  • Apache Beam  
  • Lab: Running A Python Data flow Program  
  • Lab: Running A Java Data flow Program  
  • Lab: Implementing Word Count In Dataflow Java  
  • Lab: Executing The Word Count Dataflow  
  • Lab: Executing MapReduce In Dataflow In Python  
  • Lab: Executing MapReduce In Dataflow In Java  
  • Lab: Dataflow With Big Query As Source And Side Inputs  
  • Lab: Dataflow With Big Query As Source And Side Inputs 2  
Dataproc ~ Managed Hadoop
  • Session 11 - About this section  
  • Data Proc  
  • Lab: Creating And Managing A Dataproc Cluster  
  • Lab: Creating A Firewall Rule To Access Dataproc  
  • Lab: Running A PySpark Job On Dataproc  
  • Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc  
  • Lab: Submitting A Spark Jar To Dataproc  
  • Lab: Working With Dataproc Using The GCloud CLI  
Pub/Sub for Streaming
  • Session 12 - About this section  
  • Pub Sub  
  • Lab: Working With Pubsub On The Command Line  
  • Lab: Working With PubSub Using The Web Console  
  • Lab: Setting Up A Pubsub Publisher Using The Python Library  
  • Lab: Setting Up A Pubsub Subscriber Using The Python Library  
  • Lab: Publishing Streaming Data Into Pubsub  
  • Lab: Reading Streaming Data From PubSub And Writing To BigQuery  
  • Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery  
  • Lab: Pubsub Source BigQuery Sink  
Datalab ~ Jupyter
  • Session 13 - About this section  
  • Data Lab  
  • Lab: Creating And Working On A Datalab Instance  
  • Lab: Importing And Exporting Data Using Datalab  
  • Lab: Using The Charting API In Datalab  
TensorFlow and Machine Learning
  • Session 14 - About this section  
  • Introducing Machine Learning  
  • Representation Learning  
  • NN Introduced  
  • Introducing TF  
  • Lab: Simple Math Operations  
  • Computation Graph  
  • Tensors  
  • Lab: Tensors  
  • Linear Regression Intro  
  • Placeholders and Variables  
  • Lab: Placeholders  
  • Lab: Variables  
  • Lab: Linear Regression with Made-up Data  
  • Image Processing  
  • Images As Tensors  
  • Lab: Reading and Working with Images  
  • Lab: Image Transformations  
  • Introducing MNIST  
  • K-Nearest Neigbors as Unsupervised Learning  
  • One-hot Notation and L1 Distance  
  • Steps in the K-Nearest-Neighbors Implementation  
  • Lab: K-Nearest-Neighbors  
  • Learning Algorithm  
  • Individual Neuron  
  • Learning Regression  
  • Learning XOR  
  • XOR Trained  
Regression in TensorFlow
  • Session 15 - About this section  
  • Lab: Access Data from Yahoo Finance  
  • Non TensorFlow Regression  
  • Lab: Linear Regression - Setting Up a Baseline  
  • Gradient Descent  
  • Lab: Linear Regression  
  • Lab: Multiple Regression in TensorFlow  
  • Logistic Regression Introduced  
  • Linear Classification  
  • Lab: Logistic Regression - Setting Up a Baseline  
  • Logit  
  • Softmax  
  • Argmax  
  • Lab: Logistic Regression  
  • Estimators  
  • Lab: Linear Regression using Estimators  
  • Lab: Logistic Regression using Estimators  
Vision, Translate, NLP and Speech: Trained ML APIs
  • Session 16 - About this section  
  • Lab: Taxicab Prediction - Setting up the dataset  
  • Lab: Taxicab Prediction - Training and Running the model  
  • Lab: The Vision, Translate, NLP and Speech API  
  • Lab: The Vision API for Label and Landmark Detection  
Virtual Machines and Images
  • Session 17 - About this section  
  • Live Migration  
  • Machine Types and Billing  
  • Sustained Use and Committed Use Discounts  
  • Rightsizing Recommendations  
  • RAM Disk  
  • Images  
  • Startup Scripts And Baked Images  
VPCs and Interconnecting Networks
  • Session 18 - About this section  
  • VPCs And Subnets  
  • Global VPCs, Regional Subnets  
  • IP Addresses  
  • Lab: Working with Static IP Addresses  
  • Routes  
  • Firewall Rules  
  • Lab: Working with Firewalls  
  • Lab: Working with Auto Mode and Custom Mode Networks  
  • Lab: Bastion Host  
  • Cloud VPN  
  • Lab: Working with Cloud VPN  
  • Cloud Router  
  • Lab: Using Cloud Routers for Dynamic Routing  
  • Dedicated Interconnect Direct and Carrier Peering  
  • Shared VPCs  
  • Lab: Shared VPCs  
  • VPC Network Peering  
  • Lab: VPC Peering  
  • Cloud DNS And Legacy Networks  
Managed Instance Groups and Load Balancing
  • Session 19 - About this section  
  • Managed and Unmanaged Instance Groups  
  • Types of Load Balancing  
  • Overview of HTTP(S) Load Balancing  
  • Forwarding Rules Target Proxy and Url Maps  
  • Backend Service and Backends  
  • Load Distribution and Firewall Rules  
  • Lab: HTTP(S) Load Balancing  
  • Lab: Content Based Load Balancing  
  • SSL Proxy and TCP Proxy Load Balancing  
  • Lab: SSL Proxy Load Balancing  
  • Network Load Balancing  
  • Autoscalers  
  • Lab: Autoscaling with Managed Instance Groups  
  • Internal Load Balancing  
Ops and Security
  • Session 20 - About this section  
  • StackDriver  
  • StackDriver Logging  
  • Lab: Stackdriver Resource Monitoring  
  • Lab: Stackdriver Error Reporting and Debugging  
  • Cloud Deployment Manager  
  • Lab: Using Deployment Manager  
  • Lab: Deployment Manager and Stackdriver  
  • Cloud Endpoints  
  • Cloud IAM: User accounts, Service accounts, API Credentials  
  • Cloud IAM: Roles, Identity-Aware Proxy, Best Practices  
  • Lab: Cloud IAM  
  • Data Protection  
Appendix: Hadoop Ecosystem
  • Introducing the Hadoop Ecosystem  
  • Hadoop  
  • HDFS  
  • MapReduce  
  • Yarn  
  • Hive  
  • Hive vs. RDBMS  
  • HQL vs. SQL  
  • OLAP in Hive  
  • Windowing Hive  
  • Pig  
  • More Pig  
  • Spark  
  • More Spark  
  • Streams Intro  
  • Microbatches  
  • Window Types  
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