Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate

Participants will be introduced to Google Cloud's big-data capabilities in this course. Through a combination of demonstrations and hands-on labs, participants will get a broad overview of Google Cloud and a more in-depth look at data processing capabilities and machine intelligence capabilities. This course shows the power, flexibility, and ease that big data solutions can provide on Google Cloud.

Data lakes and warehouses are the two core components of any data pipeline. This course outlines the use cases for each storage type and dives into technical details about the Google Cloud data lake and warehouse options. This course also explains the role of the data engineer and the benefits of a well-designed data pipeline for business operations. It also examines the reasons why cloud environments are a good place to do data engineering.

This is the first course in the Data Engineering on Google Cloud series. This course will prepare you for the Building Batch Data Pipelines course on Google Cloud.

Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course will explain which paradigm is best for batch data and when to use it. This course also covers many technologies in Google Cloud Data Transformation including BigQuery, executing Spark with Dataproc, pipeline graphs, Cloud Data Fusion, and serverless data processing using Dataflow. Qwiklabs will allow learners to build data pipeline components in Google Cloud.

As streaming allows businesses to obtain real-time data on their business operations, processing streaming data is increasingly popular. This course will teach you how to create streaming data pipelines using Google Cloud. For handling streaming data, Pub/Sub will be described. This course will also cover how to apply aggregations to streaming data using Dataflow and how to store processed records in BigQuery and Cloud Bigtable for analysis. The course will give learners hands-on experience with QwikLabs, which allows them to build streaming data pipeline components for Google Cloud.

Machine learning can help businesses extract more insight from their data. This course will demonstrate how machine learning can be integrated in data pipelines on Google Cloud Platform depending on the customization level. This course will cover AutoML. It can be modified with very little to no modification. This course introduces AI Platform Notebooks and BigQuery Machine Learning. These enable you to create more personalized machine learning capabilities. Kubeflow is also covered in this course. This course will provide hands-on experience in building machine learning models with QwikLabs, Google Cloud Platform.

This course is designed to give qualified candidates confidence in taking the exam and help others who are not yet qualified to create their own preparation plan.