Google Certified Professional Data Engineer

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Chris Behrens

12:47:16

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1. Introduction

1. Welcome.mp4

02:50

2. About This Course.mp4

04:55

3. Course Audience and Prerequisites.mp4

03:03

2. Data Processing Fundamentals

1. Data Processing Concepts.mp4

08:32

2. Data Processing Pipelines.mp4

06:48

3. Storage and Databases

1. Introduction to Data Storage in GCP.mp4

07:25

2. Working with Data.mp4

04:49

3. Cloud Storage.mp4

08:38

4. Service Accounts.mp4

06:06

5. Data Transfer Services.mp4

03:51

6. Cloud SQL.mp4

09:16

7. LAB- Creating a Cloud SQL Instance and Loading Data.mp4

08:17

8. Cloud Firestore.mp4

08:30

9. Cloud Spanner.mp4

11:44

10. LAB- Working with Cloud Spanner.mp4

05:59

11. Cloud Memorystore.mp4

03:39

12. LAB- Working with Cloud Memorystore.mp4

10:29

13. Comparing Storage Options.mp4

05:13

14. Exam Tips.mp4

01:50

4. Big Data Ecosystem

1. MapReduce.mp4

06:21

2. Hadoop & HDFS.mp4

07:36

3. Apache Pig -.mp4

03:08

4. Apache Spark.mp4

09:45

5. Apache Kafka.mp4

07:10

5. Real Time Messaging with PubSub

1. Pub Sub Concepts.mp4

06:34

2. Pub Sub Basics.mp4

03:58

3. LAB- Working with Cloud Pub Sub.mp4

10:37

4. LAB- Cloud Pub Sub Client Libraries.mp4

09:53

5. Advanced Pub Sub.mp4

07:38

6. LAB- Loosely Coupled Services with Cloud Pub Sub.mp4

10:22

7. LAB- Stream Data through Cloud Pub Sub to BigQuery.mp4

16:03

8. Exam Tips.mp4

02:40

6. Pipelines with Cloud Dataflow

1. Dataflow Introduction.mp4

05:35

2. Pipeline Lifecycle.mp4

05:38

3. Dataflow Pipeline Concepts.mp4

05:47

4. Advanced Dataflow Concepts.mp4

05:40

5. Dataflow Security and Access.mp4

06:13

6. Using Dataflow.mp4

07:27

7. Exam Tips.mp4

02:34

8. LAB- Working with Cloud Dataflow.mp4

07:59

9. LAB- Streaming Pipelines with Cloud Dataflow.mp4

17:26

7. Managed Spark with Cloud Dataproc

1. Dataproc Overview.mp4

03:21

2. Dataproc Basics.mp4

03:41

3. LAB- Working with Cloud Dataproc.mp4

05:51

4. Advanced Dataproc.mp4

06:31

5. LAB- Cloud Dataproc with the GCS Connector.mp4

08:03

6. Exam Tips.mp4

02:14

8. NoSQL Data with Cloud Bigtable

1. Bigtable Concepts.mp4

06:05

2. Bigtable Architecture.mp4

06:44

3. Bigtable Data Model.mp4

04:37

4. LAB- Working with Cloud Bigtable.mp4

12:59

5. Bigtable Schema Design.mp4

07:26

6. Bigtable Advanced Concepts.mp4

06:41

7. LAB- Loading and Querying Data with Cloud Bigtable.mp4

10:53

8. Exam Tips.mp4

02:18

9. Data Analytics with BigQuery

1. BigQuery Basics.mp4

05:30

2. Using BigQuery.mp4

13:09

3. Partitioning and Clustering.mp4

07:37

4. Best Practices.mp4

08:04

5. Securing BigQuery.mp4

04:31

6. BigQuery Monitoring and Logging.mp4

03:00

7. Machine Learning with BigQuery ML.mp4

03:50

8. Exam Tips.mp4

02:20

9. LAB- Working with BigQuery.mp4

10:02

10. LAB- Advanced BigQuery Features.mp4

14:44

10. Exploration with Cloud Datalab

1. Datalab Concepts.mp4

03:36

2. LAB- Working with Cloud Datalab.mp4

11:20

3. LAB- Jupyter Notebooks in GCP.mp4

09:35

11. Visualization with Cloud Data Studio

1. Reporting and Business Intelligence.mp4

02:54

2. Data Distributions.mp4

08:12

3. Introduction to Data Studio.mp4

02:45

4. Charts and Filters.mp4

05:05

12. Orchestration with Cloud Composer

1. Cloud Composer Overview.mp4

05:49

2. Cloud Composer Architecture.mp4

03:11

3. Lab- Working with Cloud Composer.mp4

13:51

4. Advanced Cloud Composer.mp4

03:50

13. Introduction to Machine Learning

1. Machine Learning Introduction.mp4

09:27

2. Machine Learning Basics.mp4

15:07

3. Machine Learning Types and Models.mp4

08:36

4. Overfitting.mp4

08:05

5. Hyperparameters.mp4

04:01

6. Feature Engineering.mp4

10:05

14. Machine Learning with TensorFlow

1. Deep Learning with TensorFlow.mp4

06:24

2. Introduction to Artificial Neural Networks.mp4

14:40

3. Neural Network Architectures.mp4

06:03

4. Building a Neural Network.mp4

03:56

15. Using Pre-Trained ML Cloud APIs

1. Cloud AI Cloud APIs.mp4

04:19

2. Vision.mp4

06:48

3. Video Intelligence.mp4

05:00

4. Language.mp4

08:03

5. Conversation.mp4

09:03

6. LAB- Working with Cloud ML APIs.mp4

14:08

16. Leveraging Auto ML Platform

1. Introduction to AutoML.mp4

03:11

2. Sight with AutoML.mp4

04:27

3. Language with AutoML.mp4

04:08

4. Structured Data with AutoML.mp4

03:17

17. Operationalizing Machine Learning Models

1. Introduction to Operationalizing ML Models.mp4

04:23

2. Kubeflow.mp4

02:49

3. AI Platform.mp4

04:05

18. Data Security and Industry Regulation

1. Security and Regulation Overview.mp4

05:36

2. IAM Best Practices.mp4

06:11

3. Data Security.mp4

04:16

4. Data Privacy.mp4

03:57

5. Industry Regulation.mp4

05:32

19. Dataprep

1. Dataprep Overview.mp4

03:31

2. LAB- Working with Cloud Dataprep.mp4

13:37

20. Preparing for the Professional Data Engineer Exam

1. Reference Architectures- Big Data.mp4

08:32

2. Reference Architectures- Artificial Intelligence and Machine Learning.mp4

03:40

3. Reference Architectures- Internet of Things.mp4

03:56

4. Reference Architectures- Mobile & Gaming.mp4

04:02

5. External Resources and Tutorials.mp4

03:19

6. Exam Guide Breakdown.mp4

18:50

7. What to Expect From the Exam.mp4

02:57

8. Thank You and Good Luck!.mp4

00:58

Description

Learn how to design, build and operate powerful big data and machine learning solutions using Google Cloud Platform

What You'll Learn?

Hello Cloud Gurus! Data management, data analytics, machine learning and artificial intelligence are all hot topics. And who does these better than Google? Our Google Certified Professional Data Engineer course will help prepare you for the certification exam so you can take that next step in your Cloud career and demonstrate your proficiency in one of the most in-demand disciplines in the industry today. The primary focus of this course is to prepare you for the GCP Professional Data Engineer certification exam. Along the way you’ll solidify your foundations in data engineering and machine learning, ensuring that by the end of the course you will be able to design and build data processing solutions, operationalize machine learning models and gain a working knowledge of relevant GCP data processing tools and technologies. This course will teach you how to: * Design, build and operationalize data solutions * Process data streams in real-time * Efficiently store and access data in the cloud * Use the GCP pre-trained AI APIs (vision, speech and text) * Train and operationalize ML models. The Google Cloud Professional Data Engineer is for data scientists, solution architects, devops engineers and anyone wanting to move into machine learning and data engineering in the context of Google. Students will need to have some familiarity with the basics of GCP, such as: storage, compute and security; some basic coding skills (like Python); and a good understanding of databases. You do not need to have a background in data engineering or machine learning, but some experience with GCP is essential. This is an advanced certification and we strongly recommend that students take the Google Certified Associate Cloud Engineer exam before embarking on this course. However, anyone who is motivated and wants to understand how big data and machine learning is done on GCP will still find value with this course. Keep being awesome, Cloud Gurus!

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Data Science

Google Cloud

Chris Behrens

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