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Snowplow R99 Carnac released with Google Analytics support

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We are pleased to announce the release of Snowplow R99 Carnac. This batch pipeline release debuts the much anticipated Google Analytics integration in Snowplow.

We are initially adding support for Google Analytics to the Snowplow batch pipeline; extending this support to the real-time pipeline will follow in due course.

Read on for more information on R99 Carnac, named after [the collection of megalithic sites around the village of Carnac in Britanny, France][carnac]:

  1. Why integrate Google Analytics into Snowplow?
  2. Overall architecture
  3. The Snowplow Google Analytics plugin
  4. New Iglu Central schemas
  5. Upgrading
  6. Roadmap
  7. Help

1. Why integrate Google Analytics into Snowplow?

Back in May, we put out an RFC on sending Google Analytics events into Snowplow. The central idea is to allow data sent using the Google Analytics JavaScript tag or the Measurement Protocol to be seamlessly integrated into the Snowplow pipeline as events and contexts.

To quote the RFC on why we went through the trouble of integrating Google Analytics events in Snowplow:

Google Analytics is the most widely used digital analytics platform in the world. And for good reason: it’s a great product – and it’s free!

However, as all Snowplow users will be aware, there are significant limitations with Google Analytics – especially with the free product:

  • Access to your own data is mediated via Google. You can access your data via the Google Analytics UI and APIs, but there are many restrictions on what data you can fetch, the volume of data you can fetch and the granularity of data you can fetch. In addition, only a subset of data is available in real-time
  • Google Analytics applies a standard set of data processing (modeling) steps on the data that are standard across it’s enormous user base; this data modeling includes sessionization and marketing attribution. These steps are not necessarily appropriate for all users
  • Google Analytics data is sampled. You can understand why Google would want to fall back to sampling: this has significant implications when you’re providing a product like Google Analytics, with such an enormous user base, for free. But it is a pain if you want to perform very particular analyses on very particular subsets of users, for example, because the data becomes unreliable as the sample size drops

Many of the above reasons are motivations for Google Analytics users to setup Snowplow alongside Google Analytics. However there is some overhead to doing this, particularly on the tracking side: for every Google tag that you create, you need to integrate a comparable Snowplow tracking tag.

By adding native support for Google Analytics and the Measurement Protocol to Snowplow, it should be straightforward for any GA user to add a single small snippet of JavaScript to their setup to push their data to Snowplow as well as GA, and thus benefit from all the opportunities that Snowplow opens up for them.

Since the release of the RFC, we’ve been hard at work on making this a reality.

2. Overall architecture

To make things as plug-and-play as possible, we’ve chosen to write a Google Analytics plugin which will intercept requests made to Google Analytics’ endpoint and duplicate them to a Snowplow collector.

On the Snowplow side of things, we’ve extended our pipeline to handle Google Analytics events through a new adapter (a component translating specific third-party events into Snowplow ones), plus a set of Iglu schemas describing Google Analytics events.

The following diagram sums up our approach:


We’ll be detailing each subsystem in turn.

3. The Snowplow Google Analytics plugin

As mentioned above, we have written a small open-source Google Analytics plugin that will mirror the requests made to Google Analytics to your Snowplow collector. It is available on GitHub, and hosted on our CloudFront.

It is fairly straightforward to setup, integrating nicely into your existing Google Analytics setup:

<script> (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r<span class="p">;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date<span class="p">();a=s.createElement(o), m=s.getElementsByTagName(o)[0<span class="p">];a.async=1<span class="p">;a.src=g<span class="p">;m.parentNode.insertBefore(a,m) })(window,document,'script','https://www.google-analytics.com/analytics.js','ga'<span class="p">); ga('create', 'UA-12345-1', 'auto'<span class="p">); ga('require', 'spGaPlugin', { endpoint: 'https://events.acme.com' <span class="p">}); ga('send', 'pageview'<span class="p">); </script> <script async src="https://d1fc8wv8zag5ca.cloudfront.net/sp-ga-plugin/0.1.0/sp-ga-plugin.js"></script>

Note that only two lines differ from the usual Google Analytics setup:

  • ga('require', 'spGaPlugin', { endpoint: 'https://events.acme.com' }); which will instantiate our plugin configured to mirror requests to the https://events.acme.com Snowplow collector endpoint
  • <script async src="https://d1fc8wv8zag5ca.cloudfront.net/sp-ga-plugin/0.1.0/sp-ga-plugin.js"></script> which will load our plugin code.

4. New Iglu Central schemas

Google Analytics support comes with a host of new schemas which are available as:

As the multitude of schemas indicates, the approach we took involved breaking down the Google Analytics payloads into a large set of closely-defined entities.

As a result, a Google Analytics payload will result in a single enriched event consisting of:

  1. A self-describing JSON, populated into the unstruct_event field and determined by the Google Analytics’ event hitType
  2. Zero or more self-describing JSONs, populated into the contexts field and determined by the rest of the contextual information in the Google Analytics payload

As an example, let’s take a pageview event from the Measurement Protocol definition. Processing this in Snowplow will result in an enriched event with:

  • A page_view entity as unstruct_event
  • A list of additional user, hit, system_info and similar entities stored in the contexts field

5. Upgrading

On the client-side you will need to make use of the plugin as described in section 3.

To benefit from the new Google Analytics integration on the batch pipeline side, you’ll need to bump the Spark Enrich version used in the EmrEtlRunner configuration file:

enrich: version: spark_enrich: 1.12.0

6. Roadmap

Upcoming Snowplow releases will include:

  • R100 [BAT] PII Enrichment phase 1, the first wave of GDPR features being added to Snowplow, centred on a new enrichment which can pseudonymize sensitive personally identifiable information
  • R10x [STR] GCP support, which will let you run the Snowplow realtime pipeline on Google Cloud Platform
  • R10x [BAT] Priority fixes, various stability, security and data quality improvements for the batch pipeline

7. Getting help

For more details on this release, please check out the release notes on GitHub.

If you have any questions or run into any problems, please visit our Discourse forum.

Learn more about our unique approach to data delivery with a Snowplow demo.

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