Course Overview
Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.
Who should attend
This class is intended for developers who are responsible for:
- Extracting, loading, transforming, cleaning, and validating data.
- Designing pipelines and architectures for data processing.
- Integrating analytics and machine learning capabilities into data pipelines.
- Querying datasets, visualizing query results, and creating reports.
Certifications
This course is part of the following Certifications:
Prerequisites
To benefit from this course, participants should have completed “Google Cloud Big Data and Machine Learning Fundamentals” or have equivalent experience.
Participants should also have:
- Basic proficiency with a common query language such as SQL.
- Experience with data modeling and ETL (extract, transform, load) activities.
- Experience with developing applications using a common programming language such as Python.
- Familiarity with machine learning and/or statistics.
Course Objectives
- Design and build data processing systems on Google Cloud.
- Process batch and streaming data by implementing autoscaling data pipelines on Dataflow.
- Derive business insights from extremely large datasets using BigQuery.
- Leverage unstructured data using Spark and ML APIs on Dataproc.
- Enable instant insights from streaming data.
- Understand ML APIs and BigQuery ML, and learn to use AutoML to create powerful models without coding.
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