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Professional Data Engineer: Professional Data Engineer on Google Cloud Platform

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Rating

4.41

Students

110

Duration

03:47:10 h

$16.49

$14.99

Curriculum for Professional Data Engineer Certification Video Course

You, This Course and Us

1 Lectures

Time 00:02:01

Introduction

3 Lectures

Time 00:23:26

Compute

13 Lectures

Time 01:32:14

Storage

12 Lectures

Time 01:32:13

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

7 Lectures

Time 00:54:46

BigTable ~ HBase = Columnar Store

6 Lectures

Time 00:54:18

Datastore ~ Document Database

2 Lectures

Time 00:20:52

BigQuery ~ Hive ~ OLAP

11 Lectures

Time 01:30:56

Dataflow ~ Apache Beam

10 Lectures

Time 01:35:33

Dataproc ~ Managed Hadoop

7 Lectures

Time 00:51:56

Pub/Sub for Streaming

9 Lectures

Time 01:03:22

Datalab ~ Jupyter

4 Lectures

Time 00:25:58

TensorFlow and Machine Learning

27 Lectures

Time 03:49:01

Regression in TensorFlow

16 Lectures

Time 02:25:49

Vision, Translate, NLP and Speech: Trained ML APIs

4 Lectures

Time 00:43:52

Virtual Machines and Images

7 Lectures

Time 00:46:24

VPCs and Interconnecting Networks

19 Lectures

Time 03:07:31

Managed Instance Groups and Load Balancing

14 Lectures

Time 01:56:23

Ops and Security

12 Lectures

Time 01:39:51

Appendix: Hadoop Ecosystem

17 Lectures

Time 02:10:44

Name of Video	Time
1. You, This Course and Us	02:01

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

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

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

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

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

Name of Video	Time
1. Datastore	14:10
2. Lab: Datastore demo	06:42

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Professional Data Engineer Exam Dumps, Practice Test Questions

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Professional Data Engineer Premium Bundle

Premium File: 319 Questions & Answers. Last update: Apr 23, 2025
Training Course: 201 Video Lectures
Study Guide: 543 Pages

Latest Questions
100% Accurate Answers
Fast Exam Updates

Professional Data Engineer Premium Bundle

Premium File: 319 Questions & Answers. Last update: Apr 23, 2025
Training Course: 201 Video Lectures
Study Guide: 543 Pages

Latest Questions
100% Accurate Answers
Fast Exam Updates

$69.97

$49.99

Google Professional Data Engineer Training Course

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