Data Engineering on Google Cloud Platform

Name: Data Engineering on Google Cloud Platform
Price: 1700.00 EUR

Gain a hands-on introduction to designing and building data processing systems on the Google Cloud Platform with this four-day instructor led course.

This course is Guaranteed to Run September 14-17 2020

If you have a Google voucher code you can redeem it against this course and date in the basket.

4 day course

Supporting material

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 offered as a Virtual Classroom course, which will be hosted from the UK on British Summer Time (BST). It is also available as a private training session and can be delivered at our own training venue in the World Trade Center, Barcelona, or any location of your choice.

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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