BigQuery

Introduction

BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. Using Meltano, an open source ELT DataOps framework, and target-miso, an open source Singer component, Miso can help you automate the process of sending your data from BigQuery to Miso's Search and Recommendation engines.

Integration Steps

Build your Meltano project

1. Follow Meltano’s guide to install the framework (Note: You can either install Python and the Meltano package to your local machine or use their official docker image for the environment).
2. Setup your project skeleton using our examples.
3. Configure the BigQuery extractor following the instructions here.
4. Follow this guide to configure the loader (target-miso) to pipe your data to Miso’s engine.
5. Optional: You can configure an orchestrator, like Airflow, to automate and schedule the data flow.

Tips and Tricks

• In Meltano’s CLI, you can use --dry-run flag to test the configuration.
• Use the discover capability of your tap to confirm stream IDs.
• Use meltano invoke tap-* --test to emit a single record for testing purposes.
• Use meltano invoke target-* --input to test a loader.

Additional Resources

For more information on Miso’s API, check out the official API Documentation.

For more Integrations like this, visit the Integrations page on our Docs site.

For more information on getting started with Meltano, see their official documentation.

Published Date: June 27th, 2022