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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.


Path Info

Clock icon Intermediate
Clock icon 45m
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.

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