Course Overview
This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.
Who should attend
This class is intended for experienced developers who are responsible for managing big data transformations including:
- Extracting, Loading, Transforming, cleaning, and validating data
- Designing pipelines and architectures for data processing
- Creating and maintaining machine learning and statistical models
- Querying datasets, visualizing query results and creating reports
Prerequisites
To get the most of out of this course, participants should have:
- Completed Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM) course OR have equivalent experience
- Basic proficiency with common query language such as SQL
- Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python
- Familiarity with Machine Learning and/or statistics
Course Objectives
This course teaches participants the following skills:
- Design and build data processing systems on Google Cloud Platform
- Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
- Derive business insights from extremely large datasets using Google BigQuery
- Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
- Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
- Enable instant insights from streaming data
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