GCP Professional DataEngineer Certification-A Complete Guide

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Vignesh Sekar Sujatha

16:22:40

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1. Overview of Cloud and Google Cloud Platform

1.1 PDE.pdf

1. Course Introduction - Google Cloud Professional Data Engineer Certification.mp4

03:35

2.1 2.Brief Introduction On Cloud Computing.pdf

2. Introduction On Cloud Computing.mp4

06:18

3.1 3.Benefits of Cloud Computing.pdf

3. Benefits of Cloud Computing.mp4

09:18

4.1 4.Why Google Cloud Platform.pdf

4. Why Google Cloud Platform.mp4

05:24

5.1 5.GCP Vs AWS Vs Azure - A Brief Comparison.pdf

5. GCP vs Azure vs AWS - A Brief Comparison.mp4

00:39

6.1 Creating a GCP Account - free trial.pdf

6. Creating a GCP Account - free trial.html

7. GCP account activation troubleshooting.html

8. Important Message.html

9. About Instructor.html

2. Overview on Google Cloud Fundamentals

1.1 Section 2 - Getting Started With Google Cloud Platform .pdf

1. Google Cloud - Regions and Zones.mp4

08:19

2. Google Cloud - Resource Hierarchy.mp4

04:29

3. Lab On GCP Console and Services Offered.mp4

05:02

4. Lab on Google Cloud Project ID And Project Name.mp4

07:51

5. Google Cloud - Cloud Shell And Cloud Editor.mp4

02:58

6. Lab on Cloud Shell And Cloud Editor.mp4

03:20

7.1 gcloudshell- Latest-2.pdf

7. Lab on Cloud Shell And Cloud Editor[Contd].mp4

06:37

8. Google Cloud - Identity And Access Management (IAM).mp4

10:24

9. Lab on Google Cloud - IAM And Roles.mp4

10:50

10. Google Cloud - Service Account And Types.mp4

05:44

11. Lab on Google Cloud - Service Account.mp4

05:04

3. Getting Started With Google Cloud Platform

1.1 Section 3 - Overview on Google Cloud Fundamentals.pdf

1. Managed Services in Google Cloud.mp4

20:28

2. Google Cloud - Compute Engine(IAAS).mp4

11:58

3. Lab on Google Cloud - Compute Engine.mp4

06:53

4. Google Cloud - App Engine(PAAS).mp4

07:53

5.1 Appengine.pdf

5.2 AppEngine.zip

5. Lab on Google Cloud App Engine - Python.mp4

06:08

6. Google Cloud - Kuberentes Engine(IAAS).mp4

08:06

7.1 gke-cli.pdf

7. Lab on Google Cloud - Kubernetes Engine (CLI).mp4

07:13

8. Google Cloud - Cloud Run(CAAS).mp4

11:49

9.1 Cloud run - Python.zip

9. Lab on Google Cloud Cloud Run - Python.mp4

06:29

10.1 cloudFunctions-python.pdf

10. Google Cloud - Cloud Function(FAAS).mp4

12:28

11.1 CloudFunctions-console.pdf

11. Lab on Google Cloud Cloud Function - Python.mp4

06:52

4. Data Engineering - Overview

1.1 Overview on Data Engineering.pdf

1. Overview on Data Engineering.mp4

07:41

2. Basic Terminologies in Data Engineering.mp4

08:56

3. Scope of Data Engineering.mp4

04:31

4. Types Of Data.mp4

04:24

5. What is a data pipeline.mp4

07:14

6. What is ETL.mp4

05:22

7. What is ELT.mp4

03:45

8. Types Of ETL Tools.mp4

02:42

5. Google Cloud Storage

1.1 Overview on Cloud Storage.pdf

1. Overview on Google Cloud Storage.mp4

05:08

2. Google Cloud Storage Hierarchy.mp4

06:08

3. Features of Cloud Storage.mp4

06:37

4. Lab on Creating Cloud Storage Bucket - Console.mp4

10:41

5. Cloud Storage Classes - Types.mp4

05:30

6.1 Lab on Creating Cloud Storage Bucket - gsutil(CLI) Commands.pdf

6. Lab on Creating Cloud Storage Bucket - gsutil (CLI) Commands.mp4

16:31

7. Lab on Object Life Cycle Management - Cloud Storage Bucket.mp4

08:05

8.1 Lab+on+Object+Versioning+-+Cloud+Storage+Bucket.pdf

8. Lab on Object Versioning - Cloud Storage Bucket.mp4

13:20

9.1 Lab on Signed Temporary URL access - Cloud Storage Bucket.pdf

9. Lab on Signed Temporary URL access - Cloud Storage Bucket.mp4

09:34

10. Overview on Cloud IAM roles - Cloud Storage bucket.mp4

08:33

11. Lab on on Cloud IAM roles - Cloud Storage bucket.mp4

08:33

12. Lab on Bucket Retention policy - Cloud Storage Bucket.mp4

06:00

13.1 storage-api.zip

13. Lab on Creating Cloud Storage Bucket - Client library(Python).mp4

11:33

14. Pricing on Cloud Storage buckets.mp4

01:41

15. Popular Use Cases.mp4

01:47

6. Google Cloud SQL

1. Overview on Google Cloud SQL.mp4

08:24

2. Features of Google Cloud SQL.mp4

03:42

3. Lab on Creating SQL Instance(PostgreSQL) - Console.mp4

07:12

4.1 Lab on Connecting to Cloud SQL Instance.pdf

4. Lab on Connecting to Cloud SQL Instance.mp4

10:00

5.1 Lab on Allow Whitelisting in Cloud SQL Instance.pdf

5. Lab on Allow Whitelisting in Cloud SQL Instance.mp4

03:33

6.1 Lab on Creating SQL Instance(PostgreSQL) - CLI.pdf

6. Lab on Creating SQL Instance(PostgreSQL) - CLI.mp4

09:50

7.1 import-table demo.pdf

7. Lab on Import and Export Data files from Cloud SQL to Cloud Storage.mp4

05:28

8. Lab on Cloud SQL Failover.mp4

03:27

9. Cloud SQL Pricing.mp4

01:42

10. Benefits of Cloud SQL.mp4

02:22

7. Google Cloud Spanner

1.1 Overview on Google Cloud Spanner.pdf

1. Overview on Google Cloud Spanner.mp4

05:55

2. What is CAP Theorem.mp4

07:08

3. Features of Cloud Spanner.mp4

04:46

4. Lab on Cloud Spanner - E2E.mp4

18:38

5. Cloud Spanner Pricing.mp4

02:21

6. Use cases And Drawbacks of Cloud Spanner.mp4

02:47

7.1 Cloud SQL vs Spanner.pdf

7. Google Cloud SQL Vs Cloud Spanner - Which one to use and When.mp4

03:17

8. Google Cloud BigTable

1. History of BigTable.mp4

02:59

2. Overview on Cloud BigTable.mp4

03:19

3. Cloud BigTable Storage Model.mp4

04:18

4. Overview on BigTable Architecture.mp4

03:19

5. Features Of Cloud BigTable.mp4

06:32

6.1 BigTable Instance - HBase Shell.pdf

6. Lab on Cloud BigTable - HBase shell.mp4

14:08

7.1 Bigtable Qwik Start - Command Line.pdf

7. Lab on Cloud BigTable - cbt (CLI) Commands.mp4

14:10

8. Cloud BigTable Pricing.mp4

01:27

9. Pros And Cons of Cloud BigTable.mp4

02:34

10. Popular Use cases.mp4

01:15

11.1 Cloud BigTable Vs Apache HBase.pdf

11. Cloud BigTable Vs Apache HBase - A Brief Comparison.mp4

03:21

9. Google Cloud Datastore and Firestore

1.1 Overview on Google Cloud Datastore.pdf

1. Overview on Cloud Datastore.mp4

06:33

2. Features Of Cloud Datastore.mp4

03:08

3. Cloud Datastore Pricing.mp4

01:53

4. Best Practices for Cloud Datastore.mp4

02:06

5.1 Overview on Google Cloud Firestore .pdf

5. Overview on Cloud Firestore (Native mode).mp4

06:08

6. Features Of Cloud Firestore.mp4

03:07

7. Cloud Firestore Pricing.mp4

01:07

8. Best Practices for Cloud Firestore.mp4

02:01

9.1 Brief Comparison on Cloud Datastore and Cloud Firestore.pdf

9. Datastore Mode Vs Native Mode - Which one to use and When.mp4

04:38

10. Google Cloud MemoryStore

1.1 Overview on Google Cloud MemoryStore.pdf

1. Overview on Cloud Memory Store.mp4

03:42

2. What is Memorystore for Redis.mp4

03:54

3. Features of Cloud Memorystore - Redis.mp4

03:51

4. Overview on GCP Memorystore - Redis in console.mp4

04:54

5. Lab on Cloud Memory store - Redis Instance.mp4

17:51

6. Pricing of Memorystore for Redis.mp4

01:16

7. What is Cloud Memcached.mp4

03:23

8. Features of Cloud Memorystore - Memcached.mp4

02:47

9. Overview on GCP Memorystore - Memcached in console.mp4

04:38

10. Pricing of Memorystore for Memcached.mp4

02:48

11. Difference between Redis and Memcached.mp4

02:27

11. Google Cloud BigQuery

1. Overview on Cloud BigQuery.mp4

07:43

2. Cloud BigQuery Hierarchy.mp4

05:29

3. Features of Cloud Bigquery.mp4

04:38

4. Lab on Cloud Bigquery - Console I.mp4

09:03

5. Lab on Cloud Bigquery - Console II.mp4

07:55

6.1 Lab on Cloud Bigquery - bq (CLI)Commands.pdf

6. Lab on Cloud Bigquery - bq (CLI) Commands.mp4

15:13

7. Lab on Cloud Bigquery - Client library(Python).mp4

11:58

8. Export BigQuery Table to Cloud Storage Bucket.mp4

06:10

9. Pricing in Cloud BigQuery.mp4

06:05

10. Use Cases of Google BigQuery.mp4

03:11

11. BigQuery Benefits of Use.mp4

02:24

12. Other services in Cloud BigQuery.mp4

07:26

12. Google Cloud Data Fusion

1. Overview on Cloud Data Fusion.mp4

06:12

2. Features of Cloud Data Fusion.mp4

03:18

3. Lab on Cloud Data Fusion - I.mp4

04:18

4. Lab on Cloud Data Fusion - II.mp4

13:37

5. Cloud Data Fusion pricing.mp4

02:32

13. Google Cloud PubSub

1. Overview on Cloud PubSub.mp4

05:29

2. Cloud PubSub Architecture.mp4

05:29

3. Features on Cloud PubSub.mp4

03:07

4. Types Of Cloud PubSub Services.mp4

02:36

5. Lab on Cloud PubSub - Console.mp4

12:14

6. Lab on Cloud PubSub - gcloud CLI.mp4

13:37

7. Cloud PubSub Pricing.mp4

02:16

8. Pros and Cons of Cloud PubSub.mp4

01:58

9. Popular Use cases.mp4

01:55

14. Google Cloud Dataproc

1.1 Overview on Google Cloud Dataproc .pdf

1. Overview on Cloud Dataproc.mp4

07:48

2. Features of Cloud Dataproc.mp4

04:19

3. Why use Dataproc.mp4

03:03

4. Lab on Cloud Dataproc Console - Standard Cluster.mp4

16:00

5. Lab on Cloud Dataproc CLI - Single Node Cluster.mp4

07:12

6. Lab on Submitting Pyspark job in Notebook Instance.mp4

02:17

7. Cloud Dataproc Pricing.mp4

01:14

8. Best Practices and Limitations of Cloud Dataproc.mp4

02:59

15. Google Cloud Dataflow

1.1 Overview on Cloud Dataflow.pdf

1. Overview on Cloud Dataflow.mp4

07:01

2. Features of Cloud Dataflow.mp4

06:55

3. Why use GCP Dataflow.mp4

07:28

4. Overview on Dataflow Concepts.mp4

15:05

5. Lab on Cloud Dataflow - Batch Predefined template.mp4

05:59

6. Lab on Creating Dataflow Pipeline Custom template - Apache Beam(Python SDK).mp4

07:05

7. Cloud Dataflow Pricing.mp4

01:55

8. Cloud Dataflow Use Cases.mp4

03:47

9. Limitations of Cloud Dataflow.mp4

03:03

10. Cloud Dataflow vs Cloud Dataproc - Which one to use when .mp4

05:22

16. Google Cloud Composer

1.1 Overview on Cloud Composer - Final.pdf

1. Overview on Cloud Composer.mp4

07:21

2. Features of Cloud Composer.mp4

03:11

3. Difference between Composer 1 and Composer 2.mp4

04:10

4.1 Lab on Composer-Dataflow.zip

4. Lab on Cloud Composer Using Dataflow.mp4

23:37

5. Architecture on Cloud Composer.mp4

07:04

6. Cloud Composer Pricing.mp4

02:27

7. Pros and Cons of Cloud Composer.mp4

03:50

8. Popular Use cases.mp4

02:15

17. Google Cloud Data Catalog

1. Overview on Cloud Data Catalog.mp4

01:50

2. Why do you need Data Catalog.mp4

05:36

3. How Data Catalog works.mp4

02:09

4. Overview on Dataplex.mp4

03:08

18. Google Cloud Dataprep

1. Overview on Cloud Dataprep.mp4

01:57

2. What is Dataprep by Trifacta.mp4

05:04

3. Pricing of Cloud Dataprep.mp4

00:57

4. Advantages and Limitations of Cloud Dataprep.mp4

01:39

19. Google Cloud Looker Studio

1. Overview on Looker Studio.mp4

02:56

20. Labs based on Real Time Scenarios

1. Streaming - Serverless Data Processing with Cloud Dataflow.mp4

22:33

21. Professional Data Engineer - Preparing for Exam + Practice Exam

1. Exam Tips.html

2. Additional Resources.html

3.1 PDE-AnswersAndExplanations.pdf

3. Sample Question 1.html

4. Sample Question 2.html

5. Sample Question 3.html

6. Sample Question 4.html

7. Sample Question 5.html

8. Sample Question 6.html

9. Sample Question 7.html

10. Sample Question 8.html

11. Sample Question 9.html

12. Sample Question 10.html

13. Sample Question 11.html

14. Sample Question 12.html

15. Sample Question 13.html

16. Sample Question 14.html

17. Sample Question 15.html

18. Sample Question 16.html

19. Sample Question 17.html

20. Sample Question 18.html

21. Sample Question 19.html

22. Sample Question 20.html

22. Congratulations - Google Cloud Professional Certified Data Engineer

1. Congratulations - Google Cloud Professional Certified Data Engineer.html

2. Thank You.html

3. Bonus Lecture.html

Description

A Complete end to end knowledge on Google Cloud and Data Engineering stacks in GCP for aspiring data professionals!

What You'll Learn?

Start your journey on Google Cloud to become a Cloud Certified Professional Data Engineer
Knowledge on all the essential concepts related to data engineers like Database(SQL & NoSQL), BigData, Apache Beam, Apache Airflow and Apache Spark.
Complete Hands-on on GCP Core services like Compute engine, Kubernetes engine, App engine, Cloud Run and Cloud Functions.
Understand the best practices for building available, scalable, resilient and secure data pipelines on the Google Cloud Platform
You will acquire professional level data engineering skills in google cloud like Cloud SQL,Cloud DataStore/Firestore,BigQuery, Memorystore and Cloud Storage.
You will acquire professional level data engineering skills in google cloud like Cloud PubSub, Cloud Dataproc, Cloud Dataflow,DataFusion, BigTable and Composer.
Hands on Practical Lab Sessions in Services like Cloud Data Prep, Data Catalog and Looker Studio
Learn how to create big data ETL/ELT pipelines using Google Cloud platform
You will learn how to monitor pipelines using Cloud Monitor, Cloud Logging and Error Reporting with a real-world project.
Data-sets, Script files and command file used in lectures are available in resources tab. This will save your typing efforts.
Implement real life end-to-end solutions by combining latest available services in Google Cloud Platform.
Hands on Practical Lab Sessions
Ideal for candidates aspiring to clear Professional Data Engineer exam

Who is this for?

You want to start your Cloud Journey with Google Cloud Platform

You want to Clear Google Cloud Professional Data Engineer certification

You want to learn Deep Internals of Data Engineering components in Google Cloud.

What You Need to Know?

Google Cloud account Free trial - (valid debit or credit card)

Basic understanding of Python, PySpark and SQL

You have an attitude to learn while having fun

More details

Description
ONLY COURSE YOU NEED TO GET READY for Google Cloud (GCP) Certified Professional Cloud Data Engineer Exam!

Immerse in a classroom style coaching of GCP cloud data engineering certification topics. This course has 17+ Hours of insanely great video contents with 90+ hands-on Lab (Most Practical Course)

Cloud is the future and GCP is Fastest growing Public cloud. 87 percentage of Google Cloud certified individuals are more confident about their cloud skills.

Google Cloud Platform is one of the fastest-growing cloud service platforms offered today that lets you run your applications and data workflows at a 'Google-sized' scale.

Google Cloud Certified Professional Cloud Data Engineer certification is one of the most highly desired IT certifications out today. It is also one of the most challenging exams offered by any cloud vendor today. Passing this exam will take many hours of study, hands-on experience, and an understanding of a very wide range of GCP services.

Have a look at course curriculum, to see depth of Course coverage.
This course also comes with:
Lifetime access to all course material & updates
Q&A Section
Udemy Certificate of Completion
So, What are you waiting for, Enroll NOW and I will see you inside course.

Best Wishes,
Vignesh Sekar.

Who this course is for:
You want to start your Cloud Journey with Google Cloud Platform
You want to Clear Google Cloud Professional Data Engineer certification
You want to learn Deep Internals of Data Engineering components in Google Cloud.

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Overall 6 years of IT experience in designing and implementing various machine learning models, ETL data pipelines, Data Analysis, Statistical analysis, Development, Testing and Productizing ml models and data pipelines.● Strong in problem solving and solutioning business problems by breaking down into structured deliverables.● Experience in collaborating with multiple stakeholder teams (Business, product, tech, finance, delivery teams) to prioritize and align on cross-functional processes.● Experience working in a fast-paced environment and make pragmatic engineering decisions in a short amount of time.● Experience in Cloud platforms like Google Cloud Platform and Microsoft Azure Services.● Experience in crafting ML Models implementing feature engineering, inferencing pipelines and real time model predictions for high performance and scalability.● Experience in ML Ops to measure and track model performance● Experience in writing complex SQL queries based on business requirement.● Experience in Handling Big Data using different Hadoop eco system components such as HDFS, YARN, MapReduce, Spark, Pig, Sqoop, Hive, Hbase and Kafka.● Proficiency in programming languages like Python, PySpark and SQL. ● Hands on experience in working with Continuous Integration and Deployment (CI/CD) using Jenkins.● Experience with creating scripts for data modeling and data import and export and automating various activities.● Experienced in Agile Methodologies, Scrum stories and sprints experience in a Python based environment, along with data analytics, data wrangling. ● Team Player with good interpersonal skills, strong understanding of fundamental business processes ● Excellent communication and problem-solving skills. ● Capable of rapidly learning new technologies and processes and successfully applying them to projects and operations

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