Instant BigQuery Usage Insights with Malloy
The Easiest, Fastest Way to Query BigQuery with Malloy
Malloy is an innovative way to analyze and explore data. The Malloy team’s maniacal focus on user experience removes much of the toil of data analysis, and turns it into a joyful experience. With some recent advances on GCP, it’s now easier than ever to run Malloy against your data in BigQuery. This blog post will show you exactly how, and provide you with some sample analysis that you can take off the shelf and use to explore your BigQuery usage patterns.
Google Cloud Shell is a browser-based environment that greatly simplifies developing and operating a GCP environment. It comes preloaded with all the necessary libraries and credentials for accessing resources in a GCP project, including BigQuery. It also comes with a VS Code-based editing environment called Cloud Code, with full access to the VS Code Extension Marketplace. Navigate to ide.cloud.google.com and you’ll see it open up. Make sure you are running the ‘Preview’ as it has a more recent version of VS Code.
To start analyzing your BigQuery usage data with Malloy, you need to do a few things: install the Malloy VS Code extension, connect Cloud Code to your GCP Project, and clone the bigquery_jobs Github repository. Let's walk through it, step-by-step:
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