Data Engineering, Big Data, and Machine Learning on GCP Specialization
Data Engineering on Google Cloud Platform. Launch your career in Data Engineering. Deliver business value with big data and machine learning.
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What you will learn
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.
Skills you will gain
About this Specialization
Applied Learning Project
This Specialization incorporates hands-on labs using our Qwiklabs platform.
These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google BigQuery, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.
Some related experience required.
Some related experience required.
There are 5 Courses in this Specialization
There are 5 Courses in this Specialization
Google Cloud Platform Big Data and Machine Learning Fundamentals
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.
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 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.
Building Batch Data Pipelines on GCP
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.
Building Resilient Streaming Analytics Systems on GCP
*Note: this is a new course with updated content from what you may have seen in the previous version of this Specialization.
Offered by

Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Frequently Asked Questions
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Is the certificate received after completing a Coursera course or specialization the same as a Google Cloud certification?
I’ve passed all the Coursera courses for the Data Engineering on Google Cloud Platform Specialization. Will I be able to pass the Google Certified Professional Data Engineer certification exam?
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