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    <description>The latest articles on DEV Community by Mark Kiehl (@markwkiehl).</description>
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      <title>How to build a modern data platform on the free tier of Google Cloud Platform</title>
      <dc:creator>Mark Kiehl</dc:creator>
      <pubDate>Sat, 05 Oct 2024 13:46:05 +0000</pubDate>
      <link>https://dev.to/markwkiehl/how-to-build-a-modern-data-platform-on-the-free-tier-of-google-cloud-platform-4162</link>
      <guid>https://dev.to/markwkiehl/how-to-build-a-modern-data-platform-on-the-free-tier-of-google-cloud-platform-4162</guid>
      <description>&lt;p&gt;I released a series of seven free public articles on Medium.com “How to build a modern data platform on the free tier of Google Cloud Platform”.  The lead article is available at: &lt;a href="https://medium.com/@markwkiehl/building-a-data-platform-on-gcp-0427500f62e8" rel="noopener noreferrer"&gt;https://medium.com/@markwkiehl/building-a-data-platform-on-gcp-0427500f62e8&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Part One “Building a Data Platform on GCP” defined the functional requirements, and detailed how to install the required software. &lt;/p&gt;

&lt;p&gt;Part Two “GCP Infrastructure &amp;amp; Authentication” explained how to use Google application default credentials (ADC) to authenticate a user-managed service account. &lt;/p&gt;

&lt;p&gt;Part Three “Google Cloud Pub/Sub Messaging” showed how to use a Python script to generate and subscribe to the Google Pub/Sub Messaging service. &lt;/p&gt;

&lt;p&gt;Part Four “Containerization using Docker” covered how to build a local Docker image for a Python script, run it locally, and then push it to Google Artifact Registry (repository). &lt;/p&gt;

&lt;p&gt;Part Five “Google Cloud Run Jobs &amp;amp; Scheduler” demonstrated how to configure Google Cloud Run Jobs and Cloud Scheduler Jobs using Google CLI to execute a Python script stored in Google Artifact Registry on a specified interval from any Google region. &lt;/p&gt;

&lt;p&gt;Part Six “Google BigQuery Cloud Database” set up a Google BigQuery dataset and table using the Google CLI, and then a Python script was used to write and query data with SQL.  &lt;/p&gt;

&lt;p&gt;Part Seven “Google Cloud Analytics” explored how to extract data from a Google BigQuery table, load it into a Pandas DataFrame, and effortlessly perform analysis and visualizations — all from a Python script.&lt;/p&gt;

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      <category>python</category>
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      <category>cloudcomputing</category>
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