Professional Data Engineer: Professional Data Engineer on Google Cloud Platform

Professional Data Engineer: Professional Data Engineer on Google Cloud Platform

Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.

$13.74 / $24.99

You, This Course and Us

  • 1. You, This Course and Us
    2m 1s

Introduction

  • 1. Theory, Practice and Tests
    10m 26s
  • 2. Lab: Setting Up A GCP Account
    7m
  • 3. Lab: Using The Cloud Shell
    6m 1s

Compute

  • 1. Compute Options
    9m 16s
  • 2. Google Compute Engine (GCE)
    7m 38s
  • 3. Lab: Creating a VM Instance
    5m 59s
  • 4. More GCE
    8m 12s
  • 5. Lab: Editing a VM Instance
    4m 45s
  • 6. Lab: Creating a VM Instance Using The Command Line
    4m 43s
  • 7. Lab: Creating And Attaching A Persistent Disk
    4m
  • 8. Google Container Engine - Kubernetes (GKE)
    10m 33s
  • 9. More GKE
    9m 54s
  • 10. Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container
    6m 55s
  • 11. App Engine
    6m 48s
  • 12. Contrasting App Engine, Compute Engine and Container Engine
    6m 3s
  • 13. Lab: Deploy And Run An App Engine App
    7m 29s

Storage

  • 1. Storage Options
    9m 48s
  • 2. Quick Take
    13m 41s
  • 3. Cloud Storage
    10m 37s
  • 4. Lab: Working With Cloud Storage Buckets
    5m 25s
  • 5. Lab: Bucket And Object Permissions
    3m 52s
  • 6. Lab: Life cycle Management On Buckets
    3m 12s
  • 7. Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage
    7m 9s
  • 8. Transfer Service
    5m 7s
  • 9. Lab: Migrating Data Using The Transfer Service
    5m 32s
  • 10. Lab: Cloud Storage ACLs and API access with Service Account
    7m 50s
  • 11. Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management
    9m 28s
  • 12. Lab: Cloud Storage Versioning, Directory Sync
    8m 42s

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

  • 1. Cloud SQL
    7m 40s
  • 2. Lab: Creating A Cloud SQL Instance
    7m 55s
  • 3. Lab: Running Commands On Cloud SQL Instance
    6m 31s
  • 4. Lab: Bulk Loading Data Into Cloud SQL Tables
    9m 9s
  • 5. Cloud Spanner
    7m 25s
  • 6. More Cloud Spanner
    9m 18s
  • 7. Lab: Working With Cloud Spanner
    6m 49s

BigTable ~ HBase = Columnar Store

  • 1. BigTable Intro
    7m 57s
  • 2. Columnar Store
    8m 12s
  • 3. Denormalised
    9m 2s
  • 4. Column Families
    8m 10s
  • 5. BigTable Performance
    13m 19s
  • 6. Lab: BigTable demo
    7m 39s

Datastore ~ Document Database

  • 1. Datastore
    14m 10s
  • 2. Lab: Datastore demo
    6m 42s

BigQuery ~ Hive ~ OLAP

  • 1. BigQuery Intro
    11m 3s
  • 2. BigQuery Advanced
    9m 59s
  • 3. Lab: Loading CSV Data Into Big Query
    9m 4s
  • 4. Lab: Running Queries On Big Query
    5m 26s
  • 5. Lab: Loading JSON Data With Nested Tables
    7m 28s
  • 6. Lab: Public Datasets In Big Query
    8m 16s
  • 7. Lab: Using Big Query Via The Command Line
    7m 45s
  • 8. Lab: Aggregations And Conditionals In Aggregations
    9m 51s
  • 9. Lab: Subqueries And Joins
    5m 44s
  • 10. Lab: Regular Expressions In Legacy SQL
    5m 36s
  • 11. Lab: Using The With Statement For SubQueries
    10m 45s

Dataflow ~ Apache Beam

  • 1. Data Flow Intro
    11m 4s
  • 2. Apache Beam
    3m 42s
  • 3. Lab: Running A Python Data flow Program
    12m 56s
  • 4. Lab: Running A Java Data flow Program
    13m 42s
  • 5. Lab: Implementing Word Count In Dataflow Java
    11m 17s
  • 6. Lab: Executing The Word Count Dataflow
    4m 37s
  • 7. Lab: Executing MapReduce In Dataflow In Python
    9m 50s
  • 8. Lab: Executing MapReduce In Dataflow In Java
    6m 8s
  • 9. Lab: Dataflow With Big Query As Source And Side Inputs
    15m 50s
  • 10. Lab: Dataflow With Big Query As Source And Side Inputs 2
    6m 28s

Dataproc ~ Managed Hadoop

  • 1. Data Proc
    8m 28s
  • 2. Lab: Creating And Managing A Dataproc Cluster
    8m 11s
  • 3. Lab: Creating A Firewall Rule To Access Dataproc
    8m 25s
  • 4. Lab: Running A PySpark Job On Dataproc
    7m 39s
  • 5. Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc
    8m 44s
  • 6. Lab: Submitting A Spark Jar To Dataproc
    2m 10s
  • 7. Lab: Working With Dataproc Using The GCloud CLI
    8m 19s

Pub/Sub for Streaming

  • 1. Pub Sub
    8m 23s
  • 2. Lab: Working With Pubsub On The Command Line
    5m 35s
  • 3. Lab: Working With PubSub Using The Web Console
    4m 40s
  • 4. Lab: Setting Up A Pubsub Publisher Using The Python Library
    5m 52s
  • 5. Lab: Setting Up A Pubsub Subscriber Using The Python Library
    4m 8s
  • 6. Lab: Publishing Streaming Data Into Pubsub
    8m 18s
  • 7. Lab: Reading Streaming Data From PubSub And Writing To BigQuery
    10m 14s
  • 8. Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery
    5m 54s
  • 9. Lab: Pubsub Source BigQuery Sink
    10m 20s

Datalab ~ Jupyter

  • 1. Data Lab
    3m
  • 2. Lab: Creating And Working On A Datalab Instance
    4m 1s
  • 3. Lab: Importing And Exporting Data Using Datalab
    12m 14s
  • 4. Lab: Using The Charting API In Datalab
    6m 43s

TensorFlow and Machine Learning

  • 1. Introducing Machine Learning
    8m 4s
  • 2. Representation Learning
    10m 27s
  • 3. NN Introduced
    7m 35s
  • 4. Introducing TF
    7m 16s
  • 5. Lab: Simple Math Operations
    8m 46s
  • 6. Computation Graph
    10m 17s
  • 7. Tensors
    9m 2s
  • 8. Lab: Tensors
    5m 3s
  • 9. Linear Regression Intro
    9m 57s
  • 10. Placeholders and Variables
    8m 44s
  • 11. Lab: Placeholders
    6m 36s
  • 12. Lab: Variables
    7m 49s
  • 13. Lab: Linear Regression with Made-up Data
    4m 52s
  • 14. Image Processing
    8m 5s
  • 15. Images As Tensors
    8m 16s
  • 16. Lab: Reading and Working with Images
    8m 6s
  • 17. Lab: Image Transformations
    6m 37s
  • 18. Introducing MNIST
    4m 13s
  • 19. K-Nearest Neigbors
    7m 42s
  • 20. One-hot Notation and L1 Distance
    7m 31s
  • 21. Steps in the K-Nearest-Neighbors Implementation
    9m 32s
  • 22. Lab: K-Nearest-Neighbors
    14m 14s
  • 23. Learning Algorithm
    10m 58s
  • 24. Individual Neuron
    9m 52s
  • 25. Learning Regression
    7m 51s
  • 26. Learning XOR
    10m 27s
  • 27. XOR Trained
    11m 11s

Regression in TensorFlow

  • 1. Lab: Access Data from Yahoo Finance
    2m 49s
  • 2. Non TensorFlow Regression
    5m 53s
  • 3. Lab: Linear Regression - Setting Up a Baseline
    11m 19s
  • 4. Gradient Descent
    9m 56s
  • 5. Lab: Linear Regression
    14m 42s
  • 6. Lab: Multiple Regression in TensorFlow
    9m 15s
  • 7. Logistic Regression Introduced
    10m 16s
  • 8. Linear Classification
    5m 25s
  • 9. Lab: Logistic Regression - Setting Up a Baseline
    7m 33s
  • 10. Logit
    8m 33s
  • 11. Softmax
    11m 55s
  • 12. Argmax
    12m 13s
  • 13. Lab: Logistic Regression
    16m 56s
  • 14. Estimators
    4m 10s
  • 15. Lab: Linear Regression using Estimators
    7m 49s
  • 16. Lab: Logistic Regression using Estimators
    4m 54s

Vision, Translate, NLP and Speech: Trained ML APIs

  • 1. Lab: Taxicab Prediction - Setting up the dataset
    14m 38s
  • 2. Lab: Taxicab Prediction - Training and Running the model
    11m 22s
  • 3. Lab: The Vision, Translate, NLP and Speech API
    10m 54s
  • 4. Lab: The Vision API for Label and Landmark Detection
    7m

Virtual Machines and Images

  • 1. Live Migration
    10m 17s
  • 2. Machine Types and Billing
    9m 21s
  • 3. Sustained Use and Committed Use Discounts
    7m 3s
  • 4. Rightsizing Recommendations
    2m 22s
  • 5. RAM Disk
    2m 7s
  • 6. Images
    7m 45s
  • 7. Startup Scripts And Baked Images
    7m 31s

VPCs and Interconnecting Networks

  • 1. VPCs And Subnets
    11m 14s
  • 2. Global VPCs, Regional Subnets
    11m 19s
  • 3. IP Addresses
    11m 39s
  • 4. Lab: Working with Static IP Addresses
    5m 46s
  • 5. Routes
    7m 36s
  • 6. Firewall Rules
    15m 33s
  • 7. Lab: Working with Firewalls
    7m 5s
  • 8. Lab: Working with Auto Mode and Custom Mode Networks
    19m 32s
  • 9. Lab: Bastion Host
    7m 10s
  • 10. Cloud VPN
    7m 27s
  • 11. Lab: Working with Cloud VPN
    11m 11s
  • 12. Cloud Router
    10m 31s
  • 13. Lab: Using Cloud Routers for Dynamic Routing
    14m 7s
  • 14. Dedicated Interconnect Direct and Carrier Peering
    8m 10s
  • 15. Shared VPCs
    10m 11s
  • 16. Lab: Shared VPCs
    6m 17s
  • 17. VPC Network Peering
    10m 10s
  • 18. Lab: VPC Peering
    7m 17s
  • 19. Cloud DNS And Legacy Networks
    5m 19s

Managed Instance Groups and Load Balancing

  • 1. Managed and Unmanaged Instance Groups
    10m 53s
  • 2. Types of Load Balancing
    5m 46s
  • 3. Overview of HTTP(S) Load Balancing
    9m 20s
  • 4. Forwarding Rules Target Proxy and Url Maps
    8m 31s
  • 5. Backend Service and Backends
    9m 28s
  • 6. Load Distribution and Firewall Rules
    4m 28s
  • 7. Lab: HTTP(S) Load Balancing
    11m 21s
  • 8. Lab: Content Based Load Balancing
    7m 6s
  • 9. SSL Proxy and TCP Proxy Load Balancing
    5m 6s
  • 10. Lab: SSL Proxy Load Balancing
    7m 49s
  • 11. Network Load Balancing
    5m 8s
  • 12. Internal Load Balancing
    7m 16s
  • 13. Autoscalers
    11m 52s
  • 14. Lab: Autoscaling with Managed Instance Groups
    12m 22s

Ops and Security

  • 1. StackDriver
    12m 8s
  • 2. StackDriver Logging
    7m 39s
  • 3. Lab: Stackdriver Resource Monitoring
    8m 12s
  • 4. Lab: Stackdriver Error Reporting and Debugging
    5m 52s
  • 5. Cloud Deployment Manager
    6m 5s
  • 6. Lab: Using Deployment Manager
    5m 10s
  • 7. Lab: Deployment Manager and Stackdriver
    8m 27s
  • 8. Cloud Endpoints
    3m 48s
  • 9. Cloud IAM: User accounts, Service accounts, API Credentials
    8m 53s
  • 10. Cloud IAM: Roles, Identity-Aware Proxy, Best Practices
    9m 31s
  • 11. Lab: Cloud IAM
    11m 57s
  • 12. Data Protection
    12m 2s

Appendix: Hadoop Ecosystem

  • 1. Introducing the Hadoop Ecosystem
    1m 34s
  • 2. Hadoop
    9m 43s
  • 3. HDFS
    10m 55s
  • 4. MapReduce
    10m 34s
  • 5. Yarn
    5m 29s
  • 6. Hive
    7m 19s
  • 7. Hive vs
    7m 10s
  • 8. HQL vs
    7m 36s
  • 9. OLAP in Hive
    7m 34s
  • 10. Windowing Hive
    8m 22s
  • 11. Pig
    8m 4s
  • 12. More Pig
    6m 38s
  • 13. Spark
    8m 54s
  • 14. More Spark
    11m 45s
  • 15. Streams Intro
    7m 44s
  • 16. Microbatches
    5m 40s
  • 17. Window Types
    5m 46s