Course details

GCP Engineering and Streaming Architecture

GCP Engineering and Streaming Architecture


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Feature engineering can be an essential tool in applied machine learning when enhancing a dataset. In this course, you will learn about concepts of feature engineering, including areas of streaming architecture and implementations.

Target Audience
Data professionals who are responsible for provisioning and optimizing big data solutions, and data enthusiasts getting started with Google Cloud Platform

Prerequisites
None

Expected Duration (hours)
1.3

Lesson Objectives

GCP Engineering and Streaming Architecture

  • start the course
  • describe the concepts of feature engineering
  • recall the benefits of quality features with feature engineering and feature selection
  • describe the process of input selection in feature engineering
  • demonstrate feature engineering in use cases
  • recall the concepts of streaming data and real-time stream processing
  • describe Dataflow triggers and late data
  • install Java JDK on Windows 10
  • demonstrate how to install Apache Maven on Windows 10
  • install Google Cloud SDK and initialize SDK Shell on Windows 10
  • demonstrate the process of streaming pipelines using Dataflow SDK 2.x and Java in Cloud SDK Shell
  • demonstrate the process of streaming pipelines using Dataflow SDK 2.x and Python in Google Cloud Shell
  • describe feature engineering concepts and streaming data architecture
  • Course Number:
    cl_gcde_a13_it_enus

    Expertise Level
    Intermediate