Google Professional Data Engineer (GCP) Online Course

Google Professional Data Engineer (GCP) Online Course

This course is a really comprehensive guide to the Google Cloud Platform - it has ~20 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.

Course Silent Features

• Certification stuff - Covers pretty much all of the material you ought to need to get past the Google Data Engineer and Cloud Architect certification tests
• 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)

In this course, you will Learn and understand the following concepts deeply:-

• Deploy Managed Hadoop apps on the Google Cloud
• Build deep learning models in 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

Course Curriculum

1. Introduction

• Theory, Practice and Tests
• Why Cloud?
  
• Hadoop and Distributed Computing
  
• On-premise, Colocation or Cloud?
  
• Introducing the Google Cloud Platform
  
• Lab: Setting Up A GCP Account
  
• Lab: Using The Cloud Shell

2. Compute Choices

• Compute Options
  
• Google Compute Engine (GCE)
  
• More GCE
  
• Lab: Creating a VM Instance
  
• 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

3. Storage

• 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

4. Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

• 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

5. BigTable ~ HBase = Columnar Store.

• BigTable Intro
  
• Columnar Store
  
• Denormalised
  
• Column Families
  
• BigTable Performance
  
• Lab: BigTable demo

6. Datastore ~ Document Database

• Datastore
  
• Lab: Datastore demo

7. BigQuery ~ Hive ~ OLAP

• 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

8. Dataflow ~ Apache Beam

• 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

9. Dataproc ~ Managed Hadoop

• Data Proc
• Lab: Creating And Managing A Dataproc Cluster
  
• Lab: Creating A Firewall Rule To Access Dataproc
  
• Lab: Running A PySpark Job OnDataproc
  
• Lab: Running ThePySpark REPL Shell And Pig Scripts On Dataproc
  
• Lab: Submitting A Spark Jar ToDataproc
  
• Lab: Working With Dataproc Using TheGCloud CLI

10. Pub/Sub for Streaming.

• Pub Sub
  
• Lab: Working With Pubsub On The Command Line
  
• Lab: Working WithPubSub 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 IntoPubsub
  
• Lab: Reading Streaming Data FromPubSub And Writing To BigQuery
  
• Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery
  
• Lab: Pubsub Source BigQuery Sink

11. Datalab ~ Jupyter

• Data Lab
  
• Lab: Creating And Working On A Datalab Instance
  
• Lab: Importing And Exporting Data Using Datalab
  
• Lab: Using the Charting API InDatalab

12. TensorFlow and Machine Learning

• 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

13. Regression in TensorFlow

• 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

14. Vision, Translate, NLP and Speech: Trained ML APIs

• 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

15. Networking

• Virtual Private Clouds
  
• VPC and Firewalls
  
• XPC or Shared VPC
  
• VPN
  
• Types of Load Balancing
  
• Proxy and Pass-through load balancing
  
• Internal load balancing

16. Ops and Security

• StackDriver
  
• StackDriver Logging
  
• Cloud Deployment Manager
  
• Cloud Endpoints
  
• Security and Service Accounts
  
• Auth and End-user accounts
  
• Identity and Access Management
  
• Data Protection

17. 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