Section 1 : Introduction

 		Lecture 1 Welcome 2:12
Lecture 2 Preparing for the Google Cloud Professional Data Engineer Exam 3:27

Section 2 : Cloud Storage for Data Engineering

 		Lecture 3 Introduction to Object Storage 7:13
Lecture 4 Options for Loading Data 3:45
Lecture 5 Access Controls for Cloud Storage 2:15
Lecture 6 Lifecycle Policy Management 2:26
Lecture 7 Using Cloud Storage Console 5:16
Lecture 8 Exercise Cloud Storage 0:13
Lecture 9 Solution Cloud Storage 1:40

Section 3 : Relational Databases - Cloud SQL

 		Lecture 10 Introduction to Relational Databases 7:56
Lecture 11 When to use Cloud SQL 2:31
Lecture 12 Creating a Cloud SQL Database 5:0
Lecture 13 Monitoring Cloud SQL 2:13
Lecture 14 Exercise Create a Cloud SQL Database 0:31
Lecture 15 Solution Create a Cloud SQL Database 2:32

Section 4 : Relational Databases - Cloud Spanner

 		Lecture 16 When to use Cloud Spanner 1:36
Lecture 17 Creating a Cloud Spanner Database 3:57
Lecture 18 Cloud Spanner Performance Considerations 3:31

Section 5 : NoSQL Databases Cloud Firestore

 		Lecture 19 Introduction to Cloud Firestore & Document Databases 3:2
Lecture 20 Entities and Kinds 2:1
Lecture 21 Indexing in Cloud Firestore 1:48
Lecture 22 Creating Entities 3:56
Lecture 23 Querying Entities 2:49
Lecture 24 Creating Kinds and Namespaces 2:26
Lecture 25 Working with Transactions 1:59
Lecture 26 Exercise Create a Kind and Entities 0:28
Lecture 27 Solution Creating Kinds and Entities 1:22

Section 6 : NoSQL Databases Bigtable

 		Lecture 28 Introduction to Bigtable and Wide-Column Databases 4:33
Lecture 29 Creating a Bigtable Instance
Lecture 30 Designing Row-keys for Bigtable 5:33
Lecture 31 Query Patterns and Denormalization 4:1
Lecture 32 Designing for Time Series Data 4:49

Section 7 : Analytical Databases BigQuery Data Management

 		Lecture 33 Introduction to BigQuery and Analytical Databases 5:18
Lecture 34 BigQuery Scalar Datatypes 1:10
Lecture 35 BigQuery Nested and Repeated Fields 1:29
Lecture 36 Querying Scalars, Nested and Repeated Fields 4:31
Lecture 37 Exercise Querying BigQuery Public Datasets 0:28
Lecture 38 Solution Querying BigQuery Public Datasets 0:59
Lecture 39 Access Controls in BigQuery 1:15
Lecture 40 Partitioning Tables 4:43
Lecture 41 Clustering Partitioned Tables 1:6
Lecture 42 Loading Data into BigQuery 2:49

Section 8 : Migrating a Data Warehouse

 		Lecture 43 Assessing the Current State of a Data Warehouse 3:20
Lecture 44 Schema and Data Transfer 3:7
Lecture 45 Data Pipelines 1:58
Lecture 46 Reporting and Analysis 1:45
Lecture 47 Data Governance 2:20

Section 9 : Caching Data and Cloud Memorystore

 		Lecture 48 Using Caching to Improve Performance 4:28
Lecture 49 Cloud Memorystore Data Structures 2:2
Lecture 50 When to use Cloud Memorystore 4:1

Section 10 : Workflows and ETLELT

 		Lecture 51 Introduction to Cloud Composer 3:24
Lecture 52 Cloud Composer Architecture 3:18
Lecture 53 Introduction to Cloud Data Fusion 3:21

Section 11 : Cloud PubSub and Data Pipelines

 		Lecture 54 Introduction to Cloud PubSub 0:58
Lecture 55 Creating Topics and Subscriptions 2:51
Lecture 56 Creating and Reading Messages 2:5
Lecture 57 Exercise Create a Topic, Publish Messages, Read Messages 0:40
Lecture 58 Solution Create a Topic Publish Message 2:6

Section 12 : Cloud Dataflow and Data Pipelines

 		Lecture 59 Stream and Batch Processing with Cloud Dataflow 3:53
Lecture 60 Running a Job in Cloud Dataflow 4:20
Lecture 61 Analyzing a Failed Job in Cloud Dataflow 3:4
Lecture 62 Monitoring Cloud Dataflow 2:55
Lecture 63 Troubleshooting a Cloud Dataflow Pipeline 2:9

Section 13 : Cloud Dataproc and Data Pipelines

 		Lecture 64 Introduction to Cloud Dataproc 3:30
Lecture 65 Creating a Cloud Dataproc Cluster 5:52
Lecture 66 Monitoring a Cloud Dataproc Cluster 1:39
Lecture 67 Using Cloud Storage with Cloud Dataproc

Section 14 : Special Considerations in Distributed Systems

 		Lecture 68 Hybrid and multi-cloud computing 3:59
Lecture 69 Asychronous Messaging 4:54
Lecture 70 Stream Processing 7:45
Lecture 71 Data Consistency Models 3:14

Section 15 : Monitoring and Logging

 		Lecture 72 Monitoring and Alerting with Cloud Monitoring 4:24
Lecture 73 Logging with Cloud Logging 3:23
Lecture 74 Creating an Alert 2:54
Lecture 75 Install the Monitoring Agent on a Virtual Machine 4:8

Section 16 : Security and Compliance

 		Lecture 76 Introduction to Identity Access Management
Lecture 77 Resource Hierarchy 1:44
Lecture 78 Predefined Roles
Lecture 79 Custom Roles 3:57
Lecture 80 Primitive Roles 1:28
Lecture 81 IAM Best Practices 2:11
Lecture 82 Ensuring Privacy with Data Loss Prevention API 4:4
Lecture 83 Legal Compliance 2:54
Lecture 84 Encryption At Rest and In Motion 6:16
Lecture 85 Key Management 2:58
Lecture 86 Exercise Grant roles to users 0:20
Lecture 87 Solution Grant roles to users 0:49

Section 17 : Introduction to Machine Learning

 		Lecture 88 3 Categories of Machine Learning Problems 3:16
Lecture 89 2 Approaches to Building ML Models 1:5
Lecture 90 Symbolic Machine Learning 5:44
Lecture 91 Neural Networks and Deep Learning 4:20

Section 18 : Building ML Models

 		Lecture 92 Features and Labels 2:30
Lecture 93 Feature Engineering 5:18
Lecture 94 Model Building 3:49
Lecture 95 Model Evaluation 5:18
Lecture 96 Gradient Descent and Backpropagation 7:23
Lecture 97 Model Troubleshooting 5:11
Lecture 98 Building Models in GCP
Lecture 99 Using Pre-built ML Models 2:39

Section 19 : Deploying and Monitoring ML Models

 		Lecture 100 Options for Deploying ML Models 2:48
Lecture 101 Using GPUs and TPUs 1:53
Lecture 102 Monitoring ML Models 3:9
Lecture 103 Bias and Unfairness in ML Models 2:9

Section 20 : Analytical Databases BigQuery ML

 		Lecture 104 Introduction to Machine Learning in BigQuery 3:6
Lecture 105 Creating a Regression Model in BigQuery 2:31
Lecture 106 Evaluating a Regression Model in BigQuery 1:36
Lecture 107 Using Model for Predictions in BigQuery 1:36
Lecture 108 Exercise Creating a Model in BigQuery 0:18
Lecture 109 Solution Creating an ML Model in BigQuery 0:26

Section 21 : Conclusion

 		Lecture 110 Conclusion and Next Steps 2:55