Professional Data Engineer: Professional Data Engineer on Google Cloud Platform

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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.

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Video Courses

You, This Course and Us

Lectures	Duration
1. You, This Course and Us	2m 1s

1. You, This Course and Us

2m 1s

Introduction

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

1. Theory, Practice and Tests

10m 26s

2. Lab: Setting Up A GCP Account

3. Lab: Using The Cloud Shell

6m 1s

Compute

Lectures	Duration
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

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

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

Lectures	Duration
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

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

Lectures	Duration
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

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

Lectures	Duration
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

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

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

1. Datastore

14m 10s

2. Lab: Datastore demo

6m 42s

BigQuery ~ Hive ~ OLAP

Lectures	Duration
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

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

Lectures	Duration
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

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

Lectures	Duration
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

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

Lectures	Duration
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

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

Lectures	Duration
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

1. Data Lab

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

Lectures	Duration
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

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

Lectures	Duration
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

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

Lectures	Duration
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

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

Virtual Machines and Images

Lectures	Duration
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

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

Lectures	Duration
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

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

Lectures	Duration
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

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

Lectures	Duration
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

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

Lectures	Duration
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

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