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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

40 minutos 7 créditos

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.

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Puntuación

—/100

Run the simulation script

Ejecutar paso

/ 30

Deploy the Google Dataflow Job to Process Stream Data

Ejecutar paso

/ 20

Inspect the data in BiqQuery

Ejecutar paso

/ 20

Create a BiqQuery view for Data Studio visualization

Ejecutar paso

/ 30