Google Cloud Data Engineer
Google data engineers need to know how to correctly manage data, including collecting, transforming, and visualizing data. They’re also responsible for designing, building, and maintaining processing systems while also managing their security and stability. Prepare for the exam by following the Professional Data Engineer learning path.
About This Course
In this course, you’ll discover the big data capabilities of Google Cloud Platform (GCP), including its data processing and machine learning operations. This course covers serverless data analysis and machine learning models that are provisioned in Google Cloud Platform, and provides skills and knowledge that are valuable to any student learning to complete the Google Data Engineer certificate.
Exam Structure
- Length: 2 hours
- Registration fee: $200 (plus tax where applicable)
- Languages: English, Japanese.
- Exam format: Multiple choice
- Prerequisites: None
- Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.
Salary Trend
As per Indeed, the average income of these engineers is about US$127,053 per year with an annual bonus of US$5,000.
Learning Objectives
Target Audience
- Data professionals who are responsible for provisioning and optimizing big data solutions, and data enthusiasts getting started with Google Cloud Platform.
- Cloud engineers and architects who want to pass the Professional Data Engineer exam
- Data engineers who want to learn about Google's advanced tools and services for data engineering
- Data scientists and data engineers who want to understand machine learning concepts
- Cloud application developers who want to use machine learning to build applications
- Devops engineers who need to support data engineering pipelines and machine learning models
Curriculum
Fundamentals
Google Cloud Platform Concepts
Navigating Google Cloud Platform Services
Benefits of Google Cloud Platform
Comparing GCP and Other Models
Creating a GCP Account
Creating a Project
GCP BigQuery
Practical Exercise
Storage & Analytics
Analytics and Scaling
Network Data Processing Models
DataProc
Dataproc Architecture
Dataproc Operations
Implementations with BigQuery for Big Data
Fundamentals of Big Query
APIs and Machine Learning
Dataflow Autoscaling Pipelines
Machine Learning with TensorFlow and Cloud ML
Engineering and Streaming Architecture
Streaming Pipelines and Analytics
Big Data and Security
