Welcome to the Advanced Analytics for Web accelerator. Once finished, you will be able to build a deeper understanding of customer behaviour on your website and use your data to influence business decisions.
Here you will learn to:
- Model and Visualise Snowplow data
- using the snowplow-web dbt package and Streamlit
- using our sample data for Snowflake or Databricks (no need to have a working pipeline)
- Set-up Snowplow Tracking and Enrichment
- Apply what you have learned on your own pipeline to gain insights
Who is this guide for?
- Data practitioners who would like to get familiar with Snowplow data.
- Data practitioners who want to learn how to use the snowplow-web dbt package and set-up tracking using their companies website or single page application, to gain insight from their customers’ behavioural data as quickly as possible.
What you will learn
In approximately 8 working hours you can achieve the following:
- Upload data - Upload a sample Snowplow events dataset to your warehouse
- Model - Configure and run the snowplow-web data model
- Visualise - Visualise the modeled data with Streamlit
- Track - Set-up and deploy tracking to your website or single page application
- Enrich - Add enrichments to your data
- Next steps - Gain value from your own pipeline data through modeling and visualisation
section 1. Upload
1h :upload, 00-00, 1m
section 2. Model
1h :model, after upload, 1m
section 3. Visualise
1h :visualise, after model, 1m
section 4. Track
2h :track, after visualise, 2m
section 5. Enrich
1h :enrich, after track, 1m
section 6. Next steps
2h :next steps, after enrich, 2m
Modeling and Visualisation
- dbt CLI installed or dbt Cloud account available
- New dbt project created and configured
- Python 3 Installed
- Snowflake or Databricks account and a user with access to create schemas and tables
Tracking and Enrichment
- Snowplow pipeline
- Web app to implement tracking
Please note that Snowflake and Databricks will be used for illustration but the snowplow-web dbt package also supports BigQuery, Postgres and Redshift. Further adapter support for this accelerator coming soon!
What you will build
Advanced Analytics for Web Dashboard - with Streamlit or Databricks