Google Professional Data Engineer (GCP) Online Course

Google Professional Data Engineer (GCP) Online Course

This in-depth course is your complete guide to mastering the Google Cloud Platform (GCP), specifically tailored to help you succeed in the Google Professional Data Engineer certification exam. With over 20 hours of content and around 60 hands-on demos, this course offers a practical, real-world approach to learning GCP.

While AWS remains the most widely used cloud platform, GCP stands out as a top choice for advanced machine learning and data engineering applications—especially given its seamless integration with TensorFlow, Google's powerful deep learning framework.

Key Features

• Certification-Focused: Covers all major topics required for the Google Data Engineer and Cloud Architect certifications
• Compute & Storage: Deep dives into App Engine, Kubernetes (Container Engine), and Compute Engine
• Big Data & Analytics: Learn how to use GCP tools like Dataproc, Dataflow, BigQuery, BigTable, and Pub/Sub
• Machine Learning with TensorFlow: Understand deep learning fundamentals, how neural networks work, and how they’re trained in the cloud
• DevOps Tools: Explore Stackdriver for logging and monitoring, and use Cloud Deployment Manager for infrastructure as code
• Security Fundamentals: Master Identity & Access Management (IAM), OAuth, API keys, service accounts, and Identity-Aware Proxy
• Networking Essentials: Learn about VPCs, shared VPCs, load balancing across layers, VPN, Cloud Interconnect, and CDN Interconnect
• Hadoop Ecosystem Overview: Brief introduction to open-source tools like Hadoop, Spark, Pig, Hive, and HBase

What You’ll Learn

• Deploy managed Hadoop applications on GCP
• Build and train deep learning models using TensorFlow in the cloud
• Choose the right compute option between Containers, VMs, and App Engine
• Use modern big data tools like BigTable, Dataflow, Apache Beam, and Pub/Sub
• Prepare thoroughly for the GCP Data Engineer certification with real-world scenarios and demos
   

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