Snowplow Analytics Setup explained by someone who builds it – With Paul Boocock from Snowplow

Dear Data-Traveller, please note that this is a Linkedin-Remix.

I posted this content already on Linkedin in June 2022, but I want to make sure it doesn’t get lost in the social network abyss.

For your accessibility-experience and also for our own content backup, we repost the original text here.

Have a look, leave a like if you like it, and join the conversation in the comments if this sparks a thought!

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Do you want to learn more about a Snowplow setup from the people who build it?

Snowplow gets a lot of attention at the moment as an analytics alternative.

For good reasons – But for whom is it an alternative?

Here are some points to understand your requirements:

– When you already work with raw analytics data. For example, from GA UA 360. You have a model that calculates your important reports from this data. Changing the data collection part to Snowplow is relatively easy in this case.
– When behavioral data becomes an essential part of your application setup. For example, your app communication is a core feature heavily driven by user interaction. You make programmatic decisions on buying ads based on content performance intra-day.
– You have the resources and will to provide a streamlined self-service analytics setup. Emphasis on resources.
– Handling the consent state of a user as transparent as possible provides you with additional data you are capable of handling.

One point should shine through: You need more understanding of how you process the data once it lands in your database.

And it would help if you had some understanding of how the data arrives in your database.

To give you a kickstarter for that, I sat down with Paul Boocock, Head of Engineering at Snowplow, to talk about how the Snowplow pipeline works. We do this based on the Google Cloud quickstarter terraform setup that Snowplow released some time ago.

Join us for ~1h talking about setting up Snowplow—link to the video: