Idempotent Pipelines and Slowly Changing Dimensions

Jul 10, 2024

Mindmap

Lecture on Idempotent Pipelines and Slowly Changing Dimensions

Key Topics

  • Idempotent Pipelines
  • Slowly Changing Dimensions (SCDs)

Idempotent Pipelines

Importance

  • Essential for maintaining consistency in data pipelines
  • Avoid data quality problems that are hard to troubleshoot
  • Beneficial for both production and backfill pipelines

Definition

  • Pipelines should produce the same results regardless of when or how many times they are run
  • More critical for batch pipelines than streaming pipelines

Example

  • Mathematical Function: f(x) = 2x, which gives 8 when x = 4. It should always give the same output for the same input
  • Input dataset should yield the same output dataset consistently

Troubleshooting Non-Idempotent Pipelines

  • Backfilling can cause inconsistencies
  • Errors are hard to catch with unit tests and integration tests
  • Creates silent failures and very hard-to-troubleshoot bugs

Causes of Non-Idempotency

  • Using INSERT INTO without truncating the table first
  • Using start_date > ... without an upper bound
  • Not using a full set of partition sensors
  • Not using depends_on_past for cumulative pipelines

Slowly Changing Dimensions (SCDs)

Overview

  • Part of dimensional data modeling
  • SCDs can introduce non-idempotent behavior if not managed properly
  • Consider alternative approaches like daily dimensions if storage is not a concern

Types of SCDs

  1. Type 0: Fixed Attributes (e.g., Birthdate)
  2. Type 1: Overwrites old data, not recommended for historical data needs
  3. Type 2: Maintains full history, considered the gold standard for SCDs
  4. Type 3: Only retains the previous value and the current value
    • More usable but less precise
    • More susceptible to non-idempotency

SCD Type 2 Detailed Example

  • Tracks historical changes fully
  • Ideal for scenarios where historical accuracy is crucial
  • Contains Start Date, End Date, and Is Current flags
  • Backfilling and updates maintain historical accuracy

Lab Exercises

  • Creating a Type 2 SCD from NBA player season data
  • Transforming it into Type 1 and Type 3 SCDs

Insights on Using SCDs

  • SCD Type 2 is the best for maintaining idempotency and historical accuracy
  • Daily snapshots can be easier and sufficient for many scenarios
  • Assess the rate of change in dimensions to choose the right SCD type

Practical Considerations

  • Understand the trade-offs between storage efficiency and complexity
  • Be aware of the company scale and data volume in deciding whether to use SCDs
  • Consider using tools like Trino, SQL window functions, and careful data transformations for implementing SCDs

Questions and Answers

  • Addressed various questions from participants about SCDs, maintaining separate tables for current and historical data, and efficient ways of capturing changes in data.

Conclusion

  • Importance of building robust, idempotent pipelines and choosing the right type of SCDs
  • Recommendation to consider the scale and specific use cases when choosing data modeling techniques
  • Announcement about upcoming boot camps and resources for further learning

Final Notes

  • Thanked participants and encouraged ongoing learning and participation in future sessions.