Google Professional Data Engineer (GCP) Certification

Course details

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.

You, This Course and Us (1)

• You, This Course and Us

Introduction (7)

• 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

Compute Choices (13)

• 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
• Storage (9)
• 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

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS (7)

• 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

BigTable ~ HBase = Columnar Store. (6)

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

Datastore ~ Document Database (2)

• Datastore
• Lab: Datastore demo

BigQuery ~ Hive ~ OLAP (11)

• 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 (10)

• 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 (7)

• 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

Pub/Sub for Streaming (9)

• 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

Datalab ~ Jupyter (4)

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

TensorFlow and Machine Learning (27)

• 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 (16)

• 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 (4)

• 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

Networking (7)

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

Ops and Security (8)

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

Appendix: Hadoop Ecosystem (17)

• 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

Updated on 05 September, 2020