How to Track State with Type 2 Dimensional Models

In this post, I’ll show how you can create Type 2 dimensional models using modern ETL tooling like PySpark and dbt (data build tool).

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Shopify
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Ian Whitestone
•13 min read•intermediate•
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•View Original
MySQLPySparkRailsRubySQL

Overview

The article discusses how to track historical state using Type 2 dimensional models in application databases, contrasting it with the traditional Type 1 dimension approach. It highlights the importance of capturing historical user data for analytics and retention analysis, and provides practical implementation strategies using tools like PySpark and dbt.

What You'll Learn

1

How to implement Type 2 dimensional models for tracking historical user data

2

Why capturing historical state is crucial for user retention analysis

3

How to utilize dbt for building Type 2 dimensions from event logs

4

When to apply event logging for real-time data tracking

Prerequisites & Requirements

  • Understanding of data modeling concepts and dimensional modeling
  • Familiarity with PySpark and dbt(optional)

Key Questions Answered

What is a Type 2 dimensional model and how is it implemented?
A Type 2 dimensional model captures historical changes in data by creating new records for each change rather than updating existing ones. This allows analysts to track how user settings evolve over time, which is essential for understanding user behavior and retention.
How can event logging be used to track user data changes?
Event logging captures changes to user data in real-time, allowing for the creation of Type 2 dimensions. By logging events such as language changes in a Kafka stream, businesses can maintain an accurate historical record of user preferences.
What are the challenges of modifying core application models for analytics?
Engineers often resist changing existing data models due to performance concerns and the complexity of migrations. This can hinder the ability to track historical data effectively, necessitating alternative solutions like event logging or snapshots.
What are the best practices for implementing Type 2 models?
Best practices include using event logging to capture changes, ensuring data integrity with after_commit callbacks, and regularly validating the Type 2 model against current data to identify discrepancies.

Technologies & Tools

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Data Processing
Pyspark
Used for building data models that are loaded into the data warehouse.
Data Modeling
Dbt
Facilitates building new data models in pure SQL.
Data Streaming
Kafka
Used for real-time data transfer between applications and data storage.

Key Actionable Insights

1
Implement event logging to capture user data changes in real-time.
This allows for the creation of Type 2 dimensions, providing valuable insights into user behavior and preferences over time.
2
Advocate for the design of core application models that support historical tracking.
While challenging, this approach ensures that data integrity is maintained and analytics can be performed directly from the source of truth.
3
Utilize dbt for building Type 2 dimensions from event logs.
This tool simplifies the process of data modeling in SQL, allowing for efficient transformation and analysis of historical data.

Common Pitfalls

1
Logging record changes before they are committed to the database can lead to mismatches.
This occurs when events are captured prematurely, so it's crucial to use after_commit callbacks to ensure data integrity.
2
Changes in application code can disrupt event logging mechanisms.
If engineers refactor code and remove logging calls, it can lead to gaps in historical data. Implementing safeguards like CODEOWNERS can help mitigate this risk.
3
Missing updates from external processes not using the application code.
This can happen if changes are made outside the expected workflow, so it's important to monitor all data modification sources.

Related Concepts

Dimensional Modeling
Event Sourcing
Data Warehousing
User Retention Analysis