Google Professional Data Engineer Training & Certification

Google Cloud Certified Professional Data Engineer is a globally valued certification that hones your skills to make data-driven decisions by optimizing the captured data. The certification training expertly demonstrate how to efficiently collect, transform, and visualize data to generate useful insights. This comprehensive training aims to render practical understanding to design, build, maintain, and troubleshoot the data processing systems while emphasizing on the crucial aspects of the system including reliability, scalability, fault-tolerance, fidelity, security, and efficiency.

Course Description

Overview

A Data Engineer is accountable to perform data analysis to anticipate prospected business outcomes, develop statistical models for supporting the decision-making, and builds machine learning models for simplifying and automating primary processes of the business.

Note: The validity of all Google certifications is 2 years from the date certified. Candidates are required to recertify in order for maintaining the status of an earned certification.

Why Professional Data Engineer?

This data engineering certification training advances your skills to:

Define and manage data structures
Work with data processes
Analyzing data and integrating pre-built ML services
Build process to optimize and analyze data
Understand and work with data visualizations
Understand and implement security and compliance

Target Audience

Data architects
Data engineers
Developers managing big data transformations
Data analysts

Pre-requisites

Basic understanding of cloud,databases and data analysis

Exam Information

To acquire the credentials of mentioned certification, you have to pass the following exam:

Certification Name	Professional Data Engineer
Test Format	Multiple choice and multiple select
Test Duration	2 hours
Passing Score	Confidential

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Course Objectives

The Data Engineer training and certification course provides in-depth understanding of:

Creating and maintaining databases and data structures
Designing effective data processing systems
Performing data analysis and enabling machine learning
Devising business processes for optimization and analysis
Designing models to induce reliability
Creating data visualizations and policy advocacy
Designing compliance and data security

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