Professional Data Engineer: Professional Data Engineer on Google Cloud Platform
Your Google Professional Data Engineer exam is just around the corner, right? So, it's high time to find an effective preparation tool! Our training course is what you really need! This is a series of videos led by the experienced IT instructors who will provide you with a detailed overview of the Professional Data Engineer certification test. Ace your Google Professional Data Engineer at the first attempt and obtain the Professional Data Engineer on Google Cloud Platform credential with ease.
Curriculum for Professional Data Engineer Video Course
You, This Course and Us
Video Name | Time | |
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1. You, This Course and Us | 02:01 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | Time | |
---|---|---|
1. Datastore | 14:10 | |
2. Lab: Datastore demo | 06:42 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
Video Name | 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 |
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