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Professional Data Engineer: Professional Data Engineer on Google Cloud Platform Certification Video Training Course

The complete solution to prepare for for your exam with Professional Data Engineer: Professional Data Engineer on Google Cloud Platform certification video training course. The Professional Data Engineer: Professional Data Engineer on Google Cloud Platform certification video training course contains a complete set of videos that will provide you with thorough knowledge to understand the key concepts. Top notch prep including Google Professional Data Engineer exam dumps, study guide & practice test questions and answers.

115 Students Enrolled
201 Lectures
03:47:10 Hours

Professional Data Engineer: Professional Data Engineer on Google Cloud Platform Certification Video Training Course Exam Curriculum

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1

You, This Course and Us

1 Lectures
Time 00:02:01
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2

Introduction

3 Lectures
Time 00:23:26
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3

Compute

13 Lectures
Time 01:32:14
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4

Storage

12 Lectures
Time 01:32:13
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5

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

7 Lectures
Time 00:54:46
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6

BigTable ~ HBase = Columnar Store

6 Lectures
Time 00:54:18
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7

Datastore ~ Document Database

2 Lectures
Time 00:20:52
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8

BigQuery ~ Hive ~ OLAP

11 Lectures
Time 01:30:56
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9

Dataflow ~ Apache Beam

10 Lectures
Time 01:35:33
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10

Dataproc ~ Managed Hadoop

7 Lectures
Time 00:51:56
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11

Pub/Sub for Streaming

9 Lectures
Time 01:03:22
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12

Datalab ~ Jupyter

4 Lectures
Time 00:25:58
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13

TensorFlow and Machine Learning

27 Lectures
Time 03:49:01
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14

Regression in TensorFlow

16 Lectures
Time 02:25:49
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15

Vision, Translate, NLP and Speech: Trained ML APIs

4 Lectures
Time 00:43:52
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16

Virtual Machines and Images

7 Lectures
Time 00:46:24
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17

VPCs and Interconnecting Networks

19 Lectures
Time 03:07:31
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18

Managed Instance Groups and Load Balancing

14 Lectures
Time 01:56:23
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19

Ops and Security

12 Lectures
Time 01:39:51
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Appendix: Hadoop Ecosystem

17 Lectures
Time 02:10:44

You, This Course and Us

  • play 1. You, This Course and Us
    02:01

Introduction

  • play 1. Theory, Practice and Tests
    10:26
  • play 2. Lab: Setting Up A GCP Account
    07:00
  • play 3. Lab: Using The Cloud Shell
    06:01

Compute

  • play 1. Compute Options
    09:16
  • play 2. Google Compute Engine (GCE)
    07:38
  • play 3. Lab: Creating a VM Instance
    05:59
  • play 4. More GCE
    08:12
  • play 5. Lab: Editing a VM Instance
    04:45
  • play 6. Lab: Creating a VM Instance Using The Command Line
    04:43
  • play 7. Lab: Creating And Attaching A Persistent Disk
    04:00
  • play 8. Google Container Engine - Kubernetes (GKE)
    10:33
  • play 9. More GKE
    09:54
  • play 10. Lab: Creating A Kubernetes Cluster And Deploying A Wordpress Container
    06:55
  • play 11. App Engine
    06:48
  • play 12. Contrasting App Engine, Compute Engine and Container Engine
    06:03
  • play 13. Lab: Deploy And Run An App Engine App
    07:29

Storage

  • play 1. Storage Options
    09:48
  • play 2. Quick Take
    13:41
  • play 3. Cloud Storage
    10:37
  • play 4. Lab: Working With Cloud Storage Buckets
    05:25
  • play 5. Lab: Bucket And Object Permissions
    03:52
  • play 6. Lab: Life cycle Management On Buckets
    03:12
  • play 7. Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage
    07:09
  • play 8. Transfer Service
    05:07
  • play 9. Lab: Migrating Data Using The Transfer Service
    05:32
  • play 10. Lab: Cloud Storage ACLs and API access with Service Account
    07:50
  • play 11. Lab: Cloud Storage Customer-Supplied Encryption Keys and Life-Cycle Management
    09:28
  • play 12. Lab: Cloud Storage Versioning, Directory Sync
    08:42

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

  • play 1. Cloud SQL
    07:40
  • play 2. Lab: Creating A Cloud SQL Instance
    07:55
  • play 3. Lab: Running Commands On Cloud SQL Instance
    06:31
  • play 4. Lab: Bulk Loading Data Into Cloud SQL Tables
    09:09
  • play 5. Cloud Spanner
    07:25
  • play 6. More Cloud Spanner
    09:18
  • play 7. Lab: Working With Cloud Spanner
    06:49

BigTable ~ HBase = Columnar Store

  • play 1. BigTable Intro
    07:57
  • play 2. Columnar Store
    08:12
  • play 3. Denormalised
    09:02
  • play 4. Column Families
    08:10
  • play 5. BigTable Performance
    13:19
  • play 6. Lab: BigTable demo
    07:39

Datastore ~ Document Database

  • play 1. Datastore
    14:10
  • play 2. Lab: Datastore demo
    06:42

BigQuery ~ Hive ~ OLAP

  • play 1. BigQuery Intro
    11:03
  • play 2. BigQuery Advanced
    09:59
  • play 3. Lab: Loading CSV Data Into Big Query
    09:04
  • play 4. Lab: Running Queries On Big Query
    05:26
  • play 5. Lab: Loading JSON Data With Nested Tables
    07:28
  • play 6. Lab: Public Datasets In Big Query
    08:16
  • play 7. Lab: Using Big Query Via The Command Line
    07:45
  • play 8. Lab: Aggregations And Conditionals In Aggregations
    09:51
  • play 9. Lab: Subqueries And Joins
    05:44
  • play 10. Lab: Regular Expressions In Legacy SQL
    05:36
  • play 11. Lab: Using The With Statement For SubQueries
    10:45

Dataflow ~ Apache Beam

  • play 1. Data Flow Intro
    11:04
  • play 2. Apache Beam
    03:42
  • play 3. Lab: Running A Python Data flow Program
    12:56
  • play 4. Lab: Running A Java Data flow Program
    13:42
  • play 5. Lab: Implementing Word Count In Dataflow Java
    11:17
  • play 6. Lab: Executing The Word Count Dataflow
    04:37
  • play 7. Lab: Executing MapReduce In Dataflow In Python
    09:50
  • play 8. Lab: Executing MapReduce In Dataflow In Java
    06:08
  • play 9. Lab: Dataflow With Big Query As Source And Side Inputs
    15:50
  • play 10. Lab: Dataflow With Big Query As Source And Side Inputs 2
    06:28

Dataproc ~ Managed Hadoop

  • play 1. Data Proc
    08:28
  • play 2. Lab: Creating And Managing A Dataproc Cluster
    08:11
  • play 3. Lab: Creating A Firewall Rule To Access Dataproc
    08:25
  • play 4. Lab: Running A PySpark Job On Dataproc
    07:39
  • play 5. Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc
    08:44
  • play 6. Lab: Submitting A Spark Jar To Dataproc
    02:10
  • play 7. Lab: Working With Dataproc Using The GCloud CLI
    08:19

Pub/Sub for Streaming

  • play 1. Pub Sub
    08:23
  • play 2. Lab: Working With Pubsub On The Command Line
    05:35
  • play 3. Lab: Working With PubSub Using The Web Console
    04:40
  • play 4. Lab: Setting Up A Pubsub Publisher Using The Python Library
    05:52
  • play 5. Lab: Setting Up A Pubsub Subscriber Using The Python Library
    04:08
  • play 6. Lab: Publishing Streaming Data Into Pubsub
    08:18
  • play 7. Lab: Reading Streaming Data From PubSub And Writing To BigQuery
    10:14
  • play 8. Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery
    05:54
  • play 9. Lab: Pubsub Source BigQuery Sink
    10:20

Datalab ~ Jupyter

  • play 1. Data Lab
    03:00
  • play 2. Lab: Creating And Working On A Datalab Instance
    04:01
  • play 3. Lab: Importing And Exporting Data Using Datalab
    12:14
  • play 4. Lab: Using The Charting API In Datalab
    06:43

TensorFlow and Machine Learning

  • play 1. Introducing Machine Learning
    08:04
  • play 2. Representation Learning
    10:27
  • play 3. NN Introduced
    07:35
  • play 4. Introducing TF
    07:16
  • play 5. Lab: Simple Math Operations
    08:46
  • play 6. Computation Graph
    10:17
  • play 7. Tensors
    09:02
  • play 8. Lab: Tensors
    05:03
  • play 9. Linear Regression Intro
    09:57
  • play 10. Placeholders and Variables
    08:44
  • play 11. Lab: Placeholders
    06:36
  • play 12. Lab: Variables
    07:49
  • play 13. Lab: Linear Regression with Made-up Data
    04:52
  • play 14. Image Processing
    08:05
  • play 15. Images As Tensors
    08:16
  • play 16. Lab: Reading and Working with Images
    08:06
  • play 17. Lab: Image Transformations
    06:37
  • play 18. Introducing MNIST
    04:13
  • play 19. K-Nearest Neigbors
    07:42
  • play 20. One-hot Notation and L1 Distance
    07:31
  • play 21. Steps in the K-Nearest-Neighbors Implementation
    09:32
  • play 22. Lab: K-Nearest-Neighbors
    14:14
  • play 23. Learning Algorithm
    10:58
  • play 24. Individual Neuron
    09:52
  • play 25. Learning Regression
    07:51
  • play 26. Learning XOR
    10:27
  • play 27. XOR Trained
    11:11

Regression in TensorFlow

  • play 1. Lab: Access Data from Yahoo Finance
    02:49
  • play 2. Non TensorFlow Regression
    05:53
  • play 3. Lab: Linear Regression - Setting Up a Baseline
    11:19
  • play 4. Gradient Descent
    09:56
  • play 5. Lab: Linear Regression
    14:42
  • play 6. Lab: Multiple Regression in TensorFlow
    09:15
  • play 7. Logistic Regression Introduced
    10:16
  • play 8. Linear Classification
    05:25
  • play 9. Lab: Logistic Regression - Setting Up a Baseline
    07:33
  • play 10. Logit
    08:33
  • play 11. Softmax
    11:55
  • play 12. Argmax
    12:13
  • play 13. Lab: Logistic Regression
    16:56
  • play 14. Estimators
    04:10
  • play 15. Lab: Linear Regression using Estimators
    07:49
  • play 16. Lab: Logistic Regression using Estimators
    04:54

Vision, Translate, NLP and Speech: Trained ML APIs

  • play 1. Lab: Taxicab Prediction - Setting up the dataset
    14:38
  • play 2. Lab: Taxicab Prediction - Training and Running the model
    11:22
  • play 3. Lab: The Vision, Translate, NLP and Speech API
    10:54
  • play 4. Lab: The Vision API for Label and Landmark Detection
    07:00

Virtual Machines and Images

  • play 1. Live Migration
    10:17
  • play 2. Machine Types and Billing
    09:21
  • play 3. Sustained Use and Committed Use Discounts
    07:03
  • play 4. Rightsizing Recommendations
    02:22
  • play 5. RAM Disk
    02:07
  • play 6. Images
    07:45
  • play 7. Startup Scripts And Baked Images
    07:31

VPCs and Interconnecting Networks

  • play 1. VPCs And Subnets
    11:14
  • play 2. Global VPCs, Regional Subnets
    11:19
  • play 3. IP Addresses
    11:39
  • play 4. Lab: Working with Static IP Addresses
    05:46
  • play 5. Routes
    07:36
  • play 6. Firewall Rules
    15:33
  • play 7. Lab: Working with Firewalls
    07:05
  • play 8. Lab: Working with Auto Mode and Custom Mode Networks
    19:32
  • play 9. Lab: Bastion Host
    07:10
  • play 10. Cloud VPN
    07:27
  • play 11. Lab: Working with Cloud VPN
    11:11
  • play 12. Cloud Router
    10:31
  • play 13. Lab: Using Cloud Routers for Dynamic Routing
    14:07
  • play 14. Dedicated Interconnect Direct and Carrier Peering
    08:10
  • play 15. Shared VPCs
    10:11
  • play 16. Lab: Shared VPCs
    06:17
  • play 17. VPC Network Peering
    10:10
  • play 18. Lab: VPC Peering
    07:17
  • play 19. Cloud DNS And Legacy Networks
    05:19

Managed Instance Groups and Load Balancing

  • play 1. Managed and Unmanaged Instance Groups
    10:53
  • play 2. Types of Load Balancing
    05:46
  • play 3. Overview of HTTP(S) Load Balancing
    09:20
  • play 4. Forwarding Rules Target Proxy and Url Maps
    08:31
  • play 5. Backend Service and Backends
    09:28
  • play 6. Load Distribution and Firewall Rules
    04:28
  • play 7. Lab: HTTP(S) Load Balancing
    11:21
  • play 8. Lab: Content Based Load Balancing
    07:06
  • play 9. SSL Proxy and TCP Proxy Load Balancing
    05:06
  • play 10. Lab: SSL Proxy Load Balancing
    07:49
  • play 11. Network Load Balancing
    05:08
  • play 12. Internal Load Balancing
    07:16
  • play 13. Autoscalers
    11:52
  • play 14. Lab: Autoscaling with Managed Instance Groups
    12:22

Ops and Security

  • play 1. StackDriver
    12:08
  • play 2. StackDriver Logging
    07:39
  • play 3. Lab: Stackdriver Resource Monitoring
    08:12
  • play 4. Lab: Stackdriver Error Reporting and Debugging
    05:52
  • play 5. Cloud Deployment Manager
    06:05
  • play 6. Lab: Using Deployment Manager
    05:10
  • play 7. Lab: Deployment Manager and Stackdriver
    08:27
  • play 8. Cloud Endpoints
    03:48
  • play 9. Cloud IAM: User accounts, Service accounts, API Credentials
    08:53
  • play 10. Cloud IAM: Roles, Identity-Aware Proxy, Best Practices
    09:31
  • play 11. Lab: Cloud IAM
    11:57
  • play 12. Data Protection
    12:02

Appendix: Hadoop Ecosystem

  • play 1. Introducing the Hadoop Ecosystem
    01:34
  • play 2. Hadoop
    09:43
  • play 3. HDFS
    10:55
  • play 4. MapReduce
    10:34
  • play 5. Yarn
    05:29
  • play 6. Hive
    07:19
  • play 7. Hive vs. RDBMS
    07:10
  • play 8. HQL vs. SQL
    07:36
  • play 9. OLAP in Hive
    07:34
  • play 10. Windowing Hive
    08:22
  • play 11. Pig
    08:04
  • play 12. More Pig
    06:38
  • play 13. Spark
    08:54
  • play 14. More Spark
    11:45
  • play 15. Streams Intro
    07:44
  • play 16. Microbatches
    05:40
  • play 17. Window Types
    05:46
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Comments * The most recent comment are at the top

Anthony
United States
Astounding course with extraordinary inclinations. With the utilization of the course by the jewel of instructors for association level courses, I could clear the request course in the essential endeavor with fantastic numbers. The teacher has given his whole data in the course effectively with different uncovered gadgets to make it essential for reviewing. Along these lines, the training is ready to engage you to completely clear the certification exams.
Jaxon
Netherlands
I joined this course for a starting to GCP and was enjoyably shocked at the profundity and detail of the educational programs exhibited. Teachers were clear in their video introduction and straight to the point. Regions of enhancements would be: 1. A different segments material for Data Engineer and Cloud Architect. 2. More exam rehearses for every point. Generally speaking, highly prescribed course. Worth taking.
Hazel
Bulgaria
The course is efficient and most likely the main course, which covers the parts of the two Data designing and engineering. Despite the fact that this training is definitely not an extensive asset for clearing the exams, it sets up a decent construct for one to work in light of. I like the conveyance and enthusiasm of the educators in instructing. Lovely course to work with.
Luis
Rwanda
I earned my Google certification just by finishing this course. Its best part is Labs for every module. Bar Sub, Data Flow, and Big Query Integration Lab are such models where you become more acquainted with how unique bits of riddle fit together. I exceedingly suggest this course. Thanks for it!
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