Preparing for Google Cloud Certification: Cloud Data Engineer
Earn a certificate upon completion.
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Subtitles: English, Spanish, French, Portuguese (European), Russian
About this Professional Certificate
Google Cloud Professional Data Engineer certification was ranked #1 on Global Knowledge’s list of 15 top-paying certifications in 2021! Enroll now to prepare!
More than 86% of Google Cloud certified users feel more confident in their cloud skills*. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Data Engineer certification.
Here’s what you have to do:
1) Complete the Coursera Data Engineering Professional Certificate
2) Review other recommended resources for the Google Cloud Professional Data Engineer certification exam
3) Review the Professional Data Engineer exam guide
4) Complete Professional Data Engineer sample questions
5) Register for the Google Cloud certification exam (remotely or at a test center)
Applied Learning Project
This professional certificate incorporates hands-on labs using the Qwiklabs platform. These hands-on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.
* Percentages indicate those who strongly or somewhat agree with the statement. Findings from a survey conducted with Google Cloud certified individuals in May 2019 by an independent third-party research organization.
Cambridge College Course Equivalencies
This professional certificate may be applied as prior learning credit to a Cambridge College degree or certificate program that includes the following courses:
- MIS 205W Management Information Systems
Courses in this Professional Certificate
This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud.
The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud Platform in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. Learners will get hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs.
Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud Platform for data transformation including BigQuery, executing Spark on Cloud Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Cloud Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud Platform using Qwiklabs.
Note: this is a new course with updated content from what you may have seen in the previous version of this Specialization.
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud Platform. Cloud Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Cloud Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners will get hands-on experience building streaming data pipeline components on Google Cloud Platform using QwikLabs.
Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud Platform depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces AI Platform Notebooks and BigQuery Machine Learning. Also, this course covers how to productionalize machine learning solutions using Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud Platform using QwikLabs.
The purpose of this course is to help those who are qualified develop confidence to attempt the exam, and to help those not yet qualified to develop their own plan for preparation.