The Google Certified Data Engineer

This course is a complete end-to-end solution for preparing for and passing the Google Certified Data Engineer.

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Course curriculum

1. What Do I Need to Take for The Data Engineering Certification?
2. An Introduction to Machine Learning
3. Modeling in Python
4. Data Cleansing
5. Basic Algorithm (Model) Types
6. The Perceptron
7. Simple Neural Network
1. Introduction
2. Is this Course for You?
3. Instructor Q & A
4. Cloud Platform Resource Hierarchy
5. The Exam Case Study
6. Big Data Services
7. Section Summary
8. Data Engineering Cheat Sheet
1. Creating an Account on GCP
2. Navigating the GCP Console
3. Creating a Project
4. Billing at a High Level
5. IAMS
6. Cloud Shell
7. API Mangement
8. Installing GCP SDK
9. Section Summary
1. Compute Services High Level
2. Working with Compute Engine
3. Cloud Launcher
4. Compute Engine Resources
5. What's Docker?
6. App Engine
7. Deploy Docker Container
8. Summary
1. Key Storage Terms
2. Storage Classes
3. Buckets
4. Working with Objects in Our Buckets
5. An Introduction to gsutil
6. Summary
1. Cloud SQL Introduction
2. MySQL Client in Cloudshell
3. Creating and Exporting Schema
4. Cloud SQL Backups
5. Creating a Cloud SQL Instance
6. Summary
1. What is BigQuery?
2. What is BigTable?
3. Cloud Datastore
4. The Pub/Sub Demo
5. What is Cloud Datastore?
6. What is Pub/Sub?
7. BigQuery Demo
8. Cloud Dataproc Demo
9. BigTable Demo
10. What is Tensorflow?
11. What is a Datalab?
12. Cloud Datalab Demo
13. Section Summary
1. Introduction
2. Is This Course Right for You?
3. The 4 Types of Data
4. Structured Versus Unstructured Data
5. Instructor Q&A
6. Section Summary
1. Why Use Cloud Dataproc?
2. On Premise Hadoop Buildout
3. Scaling Up or Out
4. Decouple Storage and Compute
5. Regions and Zones
6. Cloud Dataproc Architecture
7. Section Summary
1. The Cluster Creation Screen
2. Create Cluster Using Console
3. Create Cluster with Command Line
4. The 3 Cluster Options
5. Preemptible Worker Nodes
6. How Preemption Works
7. Image Versions
8. Custom Image
9. Custom Dataproc Cluster
10. Install Software on Dataproc Clusters
11. Add Initialization Actions
12. Cluster High Availability
13. Scaling Clusters
14. Section Summary
1. The Submit Jobs Screen
2. Submit Spark Job to Cluster
3. Submit PySpark Job vis SSH
4. Hadoop Jobs to GCP in 3 Steps
5. Scala and Python Jobs to GCP
1. WebHD_720p (4)
2. WebHD_720p (3)
3. WebHD_720p (5)
4. WebHD_720p (2)
5. WebHD_720p
6. WebHD_720p (6)
7. WebHD_720p (1)
1. Whiteboarding: On-Preminse to Cloud Dataproc
2. Whiteboarding: Moving Jobs to GCP
3. White Boarding: Data and Compute in the Same Zones
4. Whiteboarding: Defining Preemption
5. White Boarding: On-Premise Jobs to GCP Architecture
6. White Boarding: Adding Custom Software to Nodes
7. Section Summary
1. Introduction
2. Is this Course Right for You?
3. What is Streaming?
4. The Three Vs of Data
5. The Beam Pipeline
6. Section Summary
1. Definition and History
2. Beam Object Model
3. Pipeline Object Review
4. Pipeline Object Review Answer Key
5. Event Time and Processing Time
6. Windowing
7. Use Case: The Mobil App
8. Handling Data Tensions
9. MapReduce
10. FlumeJava and Batch Patterns
11. MillWheel
12. Event Skew
13. Section Summary
1. Cloud Dataflow: The SDK and the Runner
2. The 4 Core Questions of Dataflow
3. Lab: Building a Dataflow Pipeline
4. Dataflow Job Monitoring UI
5. Stack Driver and Dataflow
6. Simple Dashboard
7. Lab: Monitoring Dataflow
8. Section Summary
1. Course Introduction
2. Exam Preparation Tip
3. Is This Course Right for You?
4. Exam Tip
5. What's an Array?
6. What's a Tensor?
7. The "FLOW" in Tensorflow
8. Number Moving Through Graph
9. Hello World in Tensorflow
10. Section Summary
1. Creating a Jupyter Notebook on GCP
2. Reconnect Datalab to Virtual Machine
3. Download and Upload Notebooks to Datalab
4. Up and Running with Cloud Datalab
5. Summary
1. The Tensorflow Code Base
2. Forward Feeding Graphs
3. Handling Iteration in Tensorflow Graphs
4. Steps in Every Tensorflow Program
5. Modeling Larger Computational Graphs
6. Resizing After High Utilization Warning
7. Simple End-to-End Example
8. Tensor Dimensions
9. Placeholders
10. Sessions
11. Node Life Cycle
12. Properties of a Tensor
13. Convert to Tensors
14. Enabling Logging with Tensorflow
15. Lab: hello World in Tensorflow
16. Section Summary
1. Numpy vs Tensorflow
2. Data Scrubbing
3. Data Import and Exploration
4. Linear Regression in Tensorflow
5. The Mandelbrot Set
6. Overfitting and How to Correct it.
7. Packaging Up Our Model
8. Creating a Server Input Function
9. Lab: Linear Regression in TensorFlow
10. Linear Regression Lab Walk Through
11. Cloud Machine Learning at Scale
12. Section Summary

About this course

Free
173 lessons
7 hours of video content

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The Google Certified Data Engineer

Course curriculum

Introduction to Machine Learning for Data Engineers

The Google Cloud Platform for Data Engineers - Section 1

The Google Cloud Platform for Data Engineers - Section 2

The Google Cloud Platform for Data Engineers - Section 3

The Google Cloud Platform for Data Engineers - Section 4

The Google Cloud Platform for Data Engineers - Section 5

The Google Cloud Platform for Data Engineers - Section 6

Managing Big Data on Google's Cloud Platform - Section 1

Managing Big Data on Google's Cloud Platform - Section 2

Managing Big Data on Google's Cloud Platform - Section 3

Managing Big Data on Google's Cloud Platform - Section 4

Managing Big Data on Google's Cloud Platform - Section 5

Managing Big Data on Google's Cloud Platform - Section 6

Streaming Analytics on Google's Cloud Platform - Section 1

Streaming Analytics on Google's Cloud Platform - Section 2

Streaming Analytics on Google's Cloud Platform Section - 3

Tensorflow on Google's Cloud Platform for Data Engineers - Section 1

Tensorflow on Google's Cloud Platform for Data Engineers - Section 2

Tensorflow on Google's Cloud Platform for Data Engineers - Section 3

Tensorflow on Google's Cloud Platform for Data Engineers - Section 4

About this course