Curriculum For This Course

You, This Course and Us

1Lectures

Time 00:02:01

Introduction

3Lectures

Time 00:23:26

Compute

13Lectures

Time 01:32:14

Storage

12Lectures

Time 01:32:13

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

7Lectures

Time 00:54:46

BigTable ~ HBase = Columnar Store

6Lectures

Time 00:54:18

Datastore ~ Document Database

2Lectures

Time 00:20:52

BigQuery ~ Hive ~ OLAP

11Lectures

Time 01:30:56

Dataflow ~ Apache Beam

10Lectures

Time 01:35:33

Dataproc ~ Managed Hadoop

7Lectures

Time 00:51:56

Pub/Sub for Streaming

9Lectures

Time 01:03:22

Datalab ~ Jupyter

4Lectures

Time 00:25:58

TensorFlow and Machine Learning

27Lectures

Time 03:49:01

Regression in TensorFlow

16Lectures

Time 02:25:49

Vision, Translate, NLP and Speech: Trained ML APIs

4Lectures

Time 00:43:52

Virtual Machines and Images

7Lectures

Time 00:46:24

VPCs and Interconnecting Networks

19Lectures

Time 03:07:31

Managed Instance Groups and Load Balancing

14Lectures

Time 01:56:23

Ops and Security

12Lectures

Time 01:39:51

Appendix: Hadoop Ecosystem

17Lectures

Time 02:10:44

You, This Course and Us
	1. You, This Course and Us	02:01

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

Compute
	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

Storage
	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

Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
	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

BigTable ~ HBase = Columnar Store
	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

Datastore ~ Document Database
	1. Datastore	14:10
	2. Lab: Datastore demo	06:42

BigQuery ~ Hive ~ OLAP
	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

Dataflow ~ Apache Beam
	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

Dataproc ~ Managed Hadoop
	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

Pub/Sub for Streaming
	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

Datalab ~ Jupyter
	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

TensorFlow and Machine Learning
	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

Regression in TensorFlow
	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

Vision, Translate, NLP and Speech: Trained ML APIs
	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

Virtual Machines and Images
	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

VPCs and Interconnecting Networks
	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

Managed Instance Groups and Load Balancing
	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

Ops and Security
	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

Appendix: Hadoop Ecosystem
	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

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Professional Data Engineer: Professional Data Engineer on Google Cloud Platform

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Professional Data Engineer: Professional Data Engineer on Google Cloud Platform

Curriculum For This Course

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