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Data Pipeline: Process Stream Data and Visualize Real Time Geospatial Data

Data Pipeline: Process Stream Data and Visualize Real Time Geospatial Data

40분 크레딧 7개

GSP439

Google Cloud Self-Paced Labs

Overview

In this lab you will learn how to use Google Dataflow to process real-time streaming data from a real-time real world historical data set, store the results in Google BigQuery, then use Google Data Studio to visualize real-time geospatial data.

Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes via Java and Python APIs with the Apache Beam SDK. Cloud Dataflow provides a serverless architecture that can be used to shard and process very large batch data sets, or high volume live streams of data, in parallel.

Google BigQuery is a RESTful web service that enables interactive analysis of massively large datasets working in conjunction with Google Storage.

The data set that is used provides historic information about internal flights in the United States retrieved from the US Bureau of Transport Statistics website.

Objectives

  • Create a Google Dataflow processing job for streaming data.

  • Generate real-time streaming data using Python.

  • Analyze streaming data in Google BigQuery.

  • Create a real-time geospatial dashboard for streaming data.

이 실습의 나머지 부분과 기타 사항에 대해 알아보려면 Qwiklabs에 가입하세요.

  • Google Cloud Console에 대한 임시 액세스 권한을 얻습니다.
  • 초급부터 고급 수준까지 200여 개의 실습이 준비되어 있습니다.
  • 자신의 학습 속도에 맞춰 학습할 수 있도록 적은 분량으로 나누어져 있습니다.
이 실습을 시작하려면 가입하세요
점수

—/100

Run the simulation script

단계 진행

/ 30

Deploy the Google Dataflow Job to Process Stream Data

단계 진행

/ 20

Inspect the data in BiqQuery

단계 진행

/ 20

Create a BiqQuery view for Data Studio visualization

단계 진행

/ 30