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Data Engineering on Google Cloud Platform

Gain a hands-on introduction to designing and building data processing systems on the Google Cloud Platform with this four-day instructor led course.
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4 day course
Supporting material
Classroom, Virtual, Private
Classroom
Face to face, interactive classroom training run from our global training centres.
Virtual Classroom
A convenient and interactive learning experience, that enables you to attend on of our courses from the comfort of your own home or anywhere you can log on. We offer Virtual Classroom on selected live classroom courses where this will appear as an option under the location drop down if available. These can also be booked as Private Virtual Classrooms for exclusive business sessions.
Private
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

As a Google Cloud Partner, we’ll share our years of industry experience to help you accelerate your use of the Google Cloud Platform and get you on the path to acquiring the Professional Data Engineer Certification.

Jellyfish has been selected by Google to facilitate the delivery of this four-day course. All of our trainers are experienced practitioners, so you can learn with total confidence.

Through a combination of presentations, demos, and hands-on labs, you will learn how to design data processing systems, build end-to-end data pipelines, analyse data and carry out machine learning.

The course covers structured, unstructured, and streaming data.

This Data Engineering on Google Cloud Platform course is part of the Professional Data Engineer track and is available at our training centre in The Shard, London. This course will be run over four consecutive days. We also offer private training at a location of your choice or via Virtual Classroom.

Course overview
Who should attend:
This course 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, visualising query results and creating reports
Walk away with the ability to:
  • 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
Prerequisites
To get the most of out of this course, you should have:
  • Completed Google Cloud Fundamentals: Big Data & Machine Learning 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 as Python
  • Familiarity with Machine Learning and/or statistics
 
Course agenda
Day 1: Making Sense of Unstructured Data with Google’s Machine Learning APIs
  • Module 1: Google Cloud Dataproc Overview
  • Module 2: Running Dataproc Jobs
  • Module 3: Integrating Dataproc with Google Cloud Platform
  • Module 4: Making Sense of Unstructured Data with Google’s Machine Learning APIs
Day 2: Serverless Data Analysis with Google BigQuery and Cloud Dataflow
  • Module 5: Serverless data analysis with BigQuery
  • Module 6: Serverless, autoscaling data pipelines with Dataflow
Day 3: Serverless Machine Learning with TensorFlow on Google Cloud Platform
  • Module 7: Getting started with Machine Learning
  • Module 8: Building ML models with Tensorflow
  • Module 9: Scaling ML models with CloudML
  • Module 10: Feature Engineering
Day 4: Building Resilient Streaming Systems on Google Cloud Platform
  • Module 11: Architecture of streaming analytics pipelines
  • Module 12: Ingesting Variable Volumes
  • Module 13: Implementing streaming pipelines
  • Module 14: Streaming analytics and dashboards
  • Module 15: High throughput and low-latency with Bigtable
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