5975 | Data Engineering on Google Cloud Platform

 

Course Overview

This four-day instructor-led class provides you with 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, 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.

 

Prerequisites

  •  Completed Google Cloud Fundamentals- Big Data and Machine Learning course #8325 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


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


What You'll Learn

  •  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

 

Outline

  • 1. Serverless Data Analysis with BigQuery
  • 2. Serverless, Autoscaling Data Pipelines with Dataflow
  • 3. Getting Started with Machine Learning
  • 4. Building ML Models with Tensorflow
  • 5. Scaling ML Models with CloudML
  • 6. Feature Engineering
  • 7. ML Architectures
  • 8. Google Cloud Dataproc Overview
  • 9. Running Dataproc Jobs
  • 10. Integrating Dataproc with Google Cloud Platform
  • 11. Making Sense of Unstructured Data with Google’s Machine Learning APIs
  • 12. Need for Real-Time Streaming Analytics
  • 13. Architecture of Streaming Pipelines
  • 14. Stream Data and Events into PubSub
  • 15. Build a Stream Processing Pipeline
  • 16. High Throughput and Low-Latency with Bigtable
  • 17. High Throughput and Low-Latency with Bigtable

5975 | Data Engineering on Google Cloud Platform

SKU: GCP-5975
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