Bigquery

Ever since it was released that server-side tagging in Google Tag Manager would run on the Google Cloud Platform stack, my imagination has been running wild.

By running on GCP, the potential for integrations with other GCP components is limitless. The output to Cloud Logging already introduces interesting pipeline opportunities, but now it gets even better.

It’s finally possible to write directly to Google BigQuery from a Client or tag template!

This means that instead of treating server-side tagging as a “simple” proxy, whose purpose is mainly to replace data streams that would otherwise connect a client directly with an endpoint, it’s now possible to use a Server container as a data collection machine itself.

On New Year’s Eve 2018, I published an article which instructed how to scrape pages of a site and write the results into Google BigQuery. I considered it to be a cool way to build your own web scraper, as it utilized the power and scale of the Google Cloud platform combined with the flexibility of a headless crawler built on top of Puppeteer.

In today’s article, I’m revisiting this solution in order to share with you its latest version, which includes a feature that you might find extremely useful when auditing the cookies that are dropped on your site.

Recently I published an article on how to set up an impact test for the “flicker effect” omnipresent in client-side A/B-testing tools. Be sure to check out that article first to get some context to what we’re going to be talking about here.

In this short follow-up, I’ll show you how to measure the average time of the anti-flicker snippet delaying page visibility, if you choose to deploy the snippet. The methodology is very similar.

I’ve thoroughly enjoyed writing short (and sometimes a bit longer) bite-sized tips for my #GTMTips topic. With the advent of Google Analytics: App + Web and particularly the opportunity to access raw data through BigQuery, I thought it was a good time to get started on a new tip topic: #BigQueryTips.

For Universal Analytics, getting access to the BigQuery export with Google Analytics 360 has been one of the major selling points for the expensive platform. The hybrid approach of getting access to raw data that has nevertheless been annotated with Google Analytics’ sessionization schema and any integrations (e.g. Google Ads) the user might have enabled is a powerful thing indeed.

Update 7 October 2020: BigQuery Export can now be configured via the property settings of Google Analytics: App + Web, so you don’t need to follow the steps in this article. Check out Charles Farina’s guide for how to do this.

Here’s yet another article inspired by the fairly recent release of Google Analytics: App + Web properties. This new property type surfaces Firebase’s analytics capabilities for websites as well, when before they were restricted to mobile apps only (see my guides for iOS and Android).

Google Tag Manager is strictly a tag delivery system, and it’s very careful not to collect any analytics data on its own. This is most likely a deliberate choice, because if GTM was to start collecting data, it would introduce additional barriers to adoption.

Nevertheless, being a tool that consolidates the design, development, deployment, and testing of all the marketing and analytics pixels, code snippets, and utilities running on a website or a mobile app, lacking the necessary features for auditing and monitoring has always seemed like an oversight.

In my intense love affair with the Google Cloud Platform, I’ve never felt more inspired to write content and try things out. After starting with a Snowplow Analytics setup guide, and continuing with a Lighthouse audit automation tutorial, I’m going to show you yet another cool thing you can do with GCP.

In this guide, I’ll show you how to use an open-source web crawler running in a Google Compute Engine virtual machine (VM) instance to scrape all the internal and external links of a given domain, and write the results into a BigQuery table. With this setup, you can audit and monitor the links in any website, looking for bad status codes or missing titles, and fix them to improve your site’s logical architecture.

Google Cloud Platform is very, very cool. It’s a fully capable, enterprise-grade, scalable cloud ecosystem which lets even total novices get started with building their first cloud applications. I wrote a long guide for installing Snowplow on the GCP, and you might want to read that if you want to see how you can build your own analytics tool using some nifty open-source modules.

But this guide will not be about Snowplow. Rather, it will tackle Google’s own open-source performance audit tool: Lighthouse.