Data Engineering on Google Cloud Platform

Data Engineering on Google Cloud Platform Certification Training Course Overview

Enroll for the 4-day Data Engineering on Google Cloud Platform Training course from Koenig Solutions.  In this course you will learn about introduction to designing and building data processing systems on Google Cloud Platform. 

Through a blend of hands-on labs and interactive lectures, you 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.

Target Audience:

This class is intended for experienced developers who are responsible for managing big data transformations.

Learning Objectives:

After completing this course, you will be able 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

 

 

 

 

Data Engineering on Google Cloud Platform (32 Hours) Download Course Contents

Live Virtual Classroom Fee For Both Group Training & 1-on-1 Training On Request
Group Training
02 - 05 Aug 09:00 AM - 05:00 PM CST
(8 Hours/Day)

06 - 09 Sep 09:00 AM - 05:00 PM CST
(8 Hours/Day)

1-on-1 Training (GTR)
4 Hours
8 Hours
Week Days
Week End

Start Time : At any time

12 AM
12 PM

GTR=Guaranteed to Run
Classroom Training (Available: London, Dubai, India, Sydney, Vancouver)
Duration : On Request
Fee : On Request
On Request
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Course Prerequisites
  • 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
 

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