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Ever since Server-side tagging was publicly announced at SUPERWEEK 2020, Google and the trusted tester community have been hard at work, building something that just might change the landscape of digital analytics for good. Google Tag Manager has now released Server-side tagging into public beta. In this lengthy article, we’ll take a look at what Server-side tagging is, how it should (and should not) be used, and what its implications are on the broader digital analytics community.

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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.

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Until recently, I had a feature on GTM Tools that polled the user’s Google Tag Manager container(s) for a recently published version. If one was found, a notification was sent to a Slack app, which forwarded it to a workspace and channel of the user’s choice. This was fine, except for the fact that polling the GTM and Slack APIs for dozens upon dozens of containers is a total resource hog, and the only way I can maintain GTM Tools is it doesn’t have API leaks like that.

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Last updated 11 September 2020: Added important note about how the custom domain should be mapped with A/AAAA DNS records rather than a CNAME record. Ah, Safari’s Intelligent Tracking Prevention - the gift that keeps on giving. Having almost milked this cow for all it’s worth, I was sure there would be little need to revisit the topic. Maybe, I thought, it would be better to just sit back and watch the world burn.

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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.

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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.

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Last updated 18 Jan 2019: Added details about the free tier limitations, and showed how to avoid the Dataflow jobs auto-scaling out of control. I’m (still) a huge fan of Snowplow Analytics. Their open-source, modular approach to DIY analytics pipelines has inspired me two write articles about them, and to host a meetup in Helsinki. In my previous Snowplow with Amazon Web Services guide, I walked you through setting up a Snowplow pipeline using Amazon Web Services.

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Simo Ahava

Husband | Father | Analytics developer
simo (at) simoahava.com

Senior Data Advocate at Reaktor

Finland