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Welcome to the Advanced Analytics for Mobile accelerator. Once finished, you will be able to build a deeper understanding of customer behavior on your mobile apps and use your data to influence business decisions.

Here you will learn to:

  • Model and Visualize Snowplow data
    • using the snowplow-mobile dbt package and Streamlit
    • using our sample data (no need to have a working pipeline)
  • Set-up Snowplow Tracking in a mobile app
    • track events both from an native iOS/Android, React Native, or Flutter app
  • 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 would like to set up tracking in a mobile app and learn how to use the Snowplow mobile data model to gain insight from their customers’ behavioral data as quickly as possible.

What you will learn

In approximately 9 working hours you can achieve the following:

  • Upload data - Upload a sample Snowplow events dataset to your warehouse
  • Model - Configure and run the snowplow-mobile data model
  • Visualize - Visualize the modeled data with Streamlit
  • Track - Set-up and deploy tracking to your mobile app
  • Next steps - Gain value from your own pipeline data through modeling and visualization
gantt dateFormat HH-mm axisFormat %M section 1. Upload 1h :upload, 00-00, 1m section 2. Model 2h :model, after upload, 2m section 3. Visualize 2h :Visualize, after model, 3m section 4. Track 3h :track, after Visualize, 3m section 5. Next steps 1h :next steps, after track, 1m

Prerequisites

Modeling and Visualization

  • dbt CLI installed or dbt Cloud account available
    • New dbt project created and configured
  • Python 3 Installed
  • Snowflake, BigQuery or Databricks account and a user with access to create schemas and tables

Tracking

  • Snowplow pipeline
  • Mobile app to implement tracking on

Please note that Snowflake, BigQuery and Databricks will be used for illustration but the snowplow-mobile dbt package also supports , Postgres and Redshift.

What you will build

Advanced Analytics for Mobile Dashboard

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System overview

The diagram below gives a complete overview of the system covered in this accelerator:

  1. Events are tracked from app logic inside the mobile app.
  2. Tracked events are loaded into a warehouse by the Snowplow BDP or Open Source Cloud.
  3. The raw events are modeled into higher level entities such as screen views, sessions, or users using the snowplow-mobile dbt package.
  4. Finally, we visualize the modeled data using Streamlit.
flowchart TB subgraph mobileApp[Mobile App] appCode[App logic] tracker[Snowplow iOS/Android/React Native/Flutter tracker] appCode -- "Tracks events" --> tracker style tracker fill:#f5f5f5,stroke:#6638B8,stroke-width:3px click tracker "https://docs.snowplow.io/docs/collecting-data/collecting-from-own-applications/mobile-trackers/installation-and-set-up/" "Open tracker docs" _blank end subgraph cloud[Cloud] snowplow[Snowplow BDP/OS Cloud] warehouse[(Warehouse)] dbt[snowplow-mobile dbt package] streamlit[Streamlit] snowplow -- "Loads raw events" --> warehouse dbt -- "Models data" --> warehouse warehouse -- "Visualizes modeled data" --> streamlit style dbt fill:#f5f5f5,stroke:#6638B8,stroke-width:3px click dbt "https://docs.snowplowanalytics.com/docs/modeling-your-data/the-snowplow-mobile-data-model/dbt-mobile-data-model/" "Open dbt package" _blank click snowplow "https://snowplow.io/snowplow-bdp/" "Snowplow BDP" _blank end tracker -- "Sends tracked events" --> snowplow