Overview

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! --- 87% 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 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.

Syllabus

Course 1: Build Data Lakes and Data Warehouses on Google Cloud
Course 2: Build Batch Data Pipelines on Google Cloud
Course 3: Build Streaming Data Pipelines on Google Cloud
Course 4: Smart Analytics, Machine Learning, and AI on Google Cloud
Course 5: Preparing for your Professional Data Engineer Journey

Courses

5 reviews
9 hours 29 minutes
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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4 hours 40 minutes
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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8 hours 11 minutes
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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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6 hours 36 minutes
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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10 hours 3 minutes
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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 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. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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10 hours 53 minutes
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.