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Setting Up a Data Streaming Pipeline with Dataflow

One of the primary benefits of Dataflow is that it can handle both streaming and batch data processing in a serverless, fast, and cost-effective manner. In this hands-on lab, you’ll establish the necessary infrastructure — including a Cloud Storage bucket, a Pub/Sub topic, and a BiqQuery dataset — to execute a Dataflow template on real-time streaming data from New York City’s ever-busy taxi service.

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Path Info

Level
Clock icon Intermediate
Duration
Clock icon 45m
Published
Clock icon Jun 23, 2022

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Table of Contents

  1. Challenge

    Enable the Necessary APIs

    Enable the Dataflow, Google Cloud Storage JSON, BigQuery, Cloud Pub/Sub, and Cloud Resource Manager APIs, either through the user interface or the Cloud Shell.

  2. Challenge

    Create a Storage Bucket

    Create a Cloud Storage bucket to hold the temporary Dataflow data.

  3. Challenge

    Create a Dataset and Table

    Create a BigQuery dataset and table with the proper schema to hold the dataset-generated data.

  4. Challenge

    Run a Pub/Sub to BigQuery Dataflow Job

    Use the Pub/Sub to BigQuery Dataflow template to process the data.

  5. Challenge

    Query the Resulting Dataset

    Input and run the desired queries.

A Cloud Guru - Labs - Setting Up a Data Streaming Pipeline with Dataflow
Author
A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

What's a lab?

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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