Preparing for Google Cloud Certification: Cloud Data Engineer

Google Cloud via Coursera Professional Certificate

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Details

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Provider

Coursera Professional Certificate
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

22 weeks, 3 hours a week
Level

Intermediate

Found in

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: Google Cloud Big Data and Machine Learning Fundamentals
- Offered by Google Cloud. This course introduces the Google Cloud big data and machine learning products and services that support the ... Enroll for free.

Course 2: Modernizing Data Lakes and Data Warehouses with Google Cloud
- Offered by Google Cloud. The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for ... Enroll for free.

Course 3: Building Batch Data Pipelines on Google Cloud
- Offered by Google Cloud. Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, ... Enroll for free.

Course 4: Building Resilient Streaming Analytics Systems on Google Cloud
- Offered by Google Cloud. Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics ... Enroll for free.

Course 5: Smart Analytics, Machine Learning, and AI on Google Cloud
- Offered by Google Cloud. Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course ... Enroll for free.

Course 6: Preparing for your Professional Data Engineer Journey
- Offered by Google Cloud. This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. ... Enroll for free.

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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1 hour 56 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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7 hours 16 minutes
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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. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using Google Cloud Skills Boost.
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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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8 hours 58 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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17 hours 3 minutes
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.