Preparing for Google Cloud Certification: Cloud Data Engineer

Advance your career and prepare for the industry-recognized Google Cloud Professional Data Engineer Certification. Get a competitive edge as a Google Professional Data Engineer.

The Google Data Engineer Certification incorporates hands-on lab topics like BigQuery, using the Qwiklabs platform.

Big Data

Identify the purpose and value of key Big Data and Machine Learning products in Google Cloud

Cloud SQL

Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud

BigQuery

Employ BigQuery to carry out interactive data analysis and choose between different data processing products on Google Cloud

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Dana Baker

Dana Baker, Executive Director of Regional Campuses

"We are committed to developing current and relevant coursework to help transform our next generation of leaders."

Preparing for Google Cloud Certification: Cloud Data Engineer

100% Online

Learn on your own schedule

Flexible Schedule

Set and maintain flexible deadlines

Entry Level

No previous experience required

6-Months to Complete

Suggested pace of 10 hours/week; 6 Courses

Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate Courses

Google Cloud Platform: Big Data and Machine Learning Fundamentals

This course introduces you to the big data capabilities of Google Cloud. Through a combination of presentations, demonstrations and hands-on labs, 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.

By the end of this course, you will be able to:

  • Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
  • Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
  • Employ BigQuery to carry out interactive data analysis.
  • Choose between different data processing products on Google Cloud.

Modernizing Data Lakes and Data Warehouses with GCP

The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and examines, in technical detail, the available data lake and warehouse solutions on Google Cloud Platform.

By the end of this course, you will be able to:

  • Define the role of a data engineer.
  • Outline the benefits a successful data pipeline brings to business operations.
  • Discuss why data engineering should be done in a cloud environment.
  • Get hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs.

Building Batch Data Pipelines on GCP

Data pipelines fall under one of these paradigms: Extra-Load, Extract-Load-Transform or Extract-Transform-Load. This course describes which paradigm should be used and when for batch data.

By the end of this course, you will be able to:

  • Use the Google Cloud Platform for data transformation including BigQuery.
  • Execute Spark on Cloud Dataproc.
  • Produce pipeline graphs in Cloud Data Fusion.
  • Perform serverless data processing with Cloud Dataflow.
  • Build data pipeline components on the Google Cloud Platform using Qwiklabs.

Building Resilient Streaming Analytics Systems on GCP

Processing streaming data has gained popularity since streaming enables businesses to get real-time metrics on operations. This course reviews the process of building streaming data pipelines on the Google Cloud Platform and describes Cloud Pub/Sub for handling incoming streaming data.

By the end of this course, you will be able to:

  • Apply aggregations and transformations to streaming data using Cloud Dataflow.
  • Store processed records to BigQuery or Cloud Bigtable for analysis.
  • Build streaming data pipeline components on Google Cloud Platform using QwikLabs.

Smart Analytics, Machine Learning and AI on GCP

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.

This course covers:

  • For little to no customization, AutoML.
  • For more tailored machine learning capabilities, AI Platform Notebooks and BigQuery Machine Learning.
  • How to put into production machine learning solutions using Kubeflow.
  • Building machine learning models on Google Cloud Platform using QwikLabs.

Preparing for the Google Cloud Professional Data Engineer Exam

If you are qualified , this course will help you to confidently take the exam.  If you do not yet feel qualified, it will help you develop a plan for preparation.

Skills you will gain: