Platform Architecture · Snowplow

Snowplow validates every event against a schema. Someone still has to decide what the schemas say.

Snowplow consulting starts where the pipeline stops helping. The pipeline validates each event against its schema and routes failures to a separate stream; it cannot tell you whether the schema describes the behavior your business needs to measure. We design and govern that layer: the event and entity definitions, the versioning rules, and the models that turn validated events into numbers finance and marketing can reconcile.

Start with an Assessment →Not sure where to start?
01Where it breaks

Where Snowplow estates break.

These are the patterns that show up across Snowplow implementations where the pipeline is validating correctly but nobody is governing what gets validated.

02How we engage

What we do on a Snowplow engagement.

Tracking design

One event dictionary, one entity model, owned and versioned. Sentence-level definitions, not just field types.

Iglu governance

Approval path, naming, versioning, and deprecation rules that engineers can follow without a meeting.

Failed-event operations

A named owner, a review rhythm, and a reprocessing runbook.

Warehouse modeling

Sessions, identity, and revenue defined once, in the warehouse, reconciled to the system of record. See how we approach the BigQuery and Snowflake warehouse truth layer.

Server-side collection

Where a first-party collector fits alongside server-side GTM or RudderStack.

What does Snowplow schema governance involve?

An event dictionary that defines each event and entity in plain sentences, an approval path for changes to the Iglu registry, a versioning and deprecation rule, and a named owner for every schema.

03Who works on this

The team behind this work.

Valentina Borisovna, Lead Experimentation and Data Visualization Analyst at Analytico
Valentina Chivikova
Analytics Data Engineer · Calgary, Alberta

Valentina builds the pipelines behind Analytico's measurement work, moving event and business data into the warehouse in a form teams can model, test, and trust.

04Questions

Snowplow — frequently asked questions

It depends on whether you need to own the event schema and the raw data. If your reporting questions fit GA4's model, GA4 may be enough; if you need to define your own events and entities and govern them in a registry, that is the case for Snowplow. Either way, the schema and the definitions behind it are the architecture work we do.
An event dictionary that defines each event and entity in plain sentences, an approval path for changes to the Iglu registry, a versioning and deprecation rule, and a named owner for every schema.
They are routed to a bad-data stream and can be reprocessed once the tracker or schema is fixed. (Source: Snowplow docs, architecture overview.)
Yes; it loads to Redshift, Snowflake, BigQuery, and Databricks, and to lakes on S3, GCS, and ADLS. (Source: Snowplow docs, architecture overview.)
Start with the Measurement Architecture Assessment: a scoped diagnostic conducted by a senior principal. It begins with a 30-minute conversation, runs three to five weeks, and ends with a document you own.

Send us your Iglu registry list and the last 30 days of failed-event counts.

We will tell you which three schemas to fix first.

Start here
The Assessment is calibrated to your Snowplow schema and pipeline — not a generic tracking review.
Start with an Assessment →Not sure where to start?

Talk to someone who has fixed this before.

A signal audit takes two weeks and tells you which numbers to trust. Book a call or send a note.

Prefer to talk live?

Pick a time that works for you. You will get a calendar invite right away.