Preparing for Google Cloud Certification: Cloud Data Engineer Certificate Online
Move toward data engineering certification with these Google Cloud preparation courses that will familiarize you with designing, deploying, and operationalizing data science analytics.
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LEARN MOREWhat You Will Learn
- Learn about the purpose and power of Google Cloud's big data and ML solutions
- Examine data lakes and data warehouse use cases to understand why data engineering is best performed within a cloud environment
- Explore batch and streaming data pipelines on GCP using BigQuery, Cloud Data Fusion and serverless data processing with Dataflow
- Plan and prepare for the Google Cloud Professional Data Engineer certification exam
About the Google Cloud Professional Data Engineer Certification
The Google Cloud Professional Data Engineer Certification is a premier certification designed to validate the cloud capabilities of some of today's top data engineers. It’s an industry-recognized certification that, when earned, can help give you the confidence – and pay increase or promotion – deserving of a data engineer with advanced cloud skills.
So, if you’re looking to move forward in your career by readying yourself for the cloud data engineer certification exam, then the Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate is right for you.
Through a series of six, self-paced courses you can complete online on your own schedule, you’ll learn about Big Data and machine learning products in Google Cloud, how to apply aggregations and transformations to streaming data using Dataflow, and why and how data engineering is best performed in a cloud environment.
This Professional Certificate program also features hands-on labs with the Qwiklabs platform, so you can practice what you learn as you progress through each course. This means you're building transferrable, real-world skills that benefit both your career and your employer.
Required Google Cloud Data Engineer Certificate Courses
BEGINNER | Data Science | Self-paced | 9 hours
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.
INTERMEDIATE | Information Technology | Self-paced | 9 hours
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.INTERMEDIATE | Information Technology | Self-paced | 17 hours
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.INTERMEDIATE | Information Technology | Self-paced | 7 hours
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.INTERMEDIATE | Information Technology | Self-paced | 7 hours
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.
ADVANCED | Information Technology | Self-paced | 5 hours
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.Complete This Certificate. Get College Credit.
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Frequently Asked Questions
When you enroll in this self-paced certificate program, you decide how quickly you want to complete each of the courses in the specialization. To access the courses, you pay a small monthly cost of $35, so the total cost of your Professional Certificate depends on you. Plus, you can take a break or cancel your subscription anytime.
You can expect to spend 2-3 months complete the courses and hands-on projects to earn your certificate.
This intermediate-level course is best for those with a year of experience in either SQL, data modeling, Python programming, machine learning, or ETL.
Share this top-ranked certificate with your professional network to showcase your cloud skills and readiness to advance in your career. It also means you've prepared for the industry-recognized Google Cloud Professional Data Engineer certification exam.
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