Best Practices - Cumulative Data

Master Data Science Workflows with Best Practices

Unlock the full potential of your data pipeline with proven practices for automation, governance, and quality control.

Key Benefits of Best Practices

Guaranteed Data Integrity

Build reliable pipelines on immutable data principles, ensuring reproducibility and eliminating data corruption.

Predictable Processing

Create deterministic, side-effect-free data transformations that are easy to test and maintain

Built-in Quality Control

Implement automated validation and monitoring at every stage of your data pipeline.

Core Principles for Modern Data Teams

Identity-Based Management

Separate data identity from storage location using snapshot catalog for powerful data governance and observability.

dataset:
  id: 432
  class: features
  instance: "20240301"
  label: prod
  repository: data-lake
  version: 3
  schema: 'v2'
  uri: s3://bucket/features/432/

Pure Transformation Jobs

Jobs create new datasets and identities instead of modifying existing ones, ensuring reproducibility and parallel processing safety.

name: feature_engineering
execution_profile: python
inputs:
  - class: raw_data
    label: prod
    repository: data-lake
    instance_cron: "0 0 * * *"
outputs:
  - class: engineered_features

Immutable Data as Foundation

Every dataset is treated as immutable, creating perfect reproducibility and eliminating data corruption risks.

datasets:
- instance: "20240301"
  label: prod
  uri: s3://bucket/features/432/
- instance: "20240302"
  label: prod
  uri: s3://bucket/features/454/
- instance: "20240302"
  label: add_feature-gender
  uri: s3://bucket/features/468/

Automated Quality Control

Integrate quality checks directly into your data definitions:

# Quality Rules in Schema
customer_features:
  version: v2
  columns:
    - name: customer_id
      type: int
    - name: customer_lifetime_value
      type: double
  sodacore_checks: |
    - row_count > 1000
    - missing_count(customer_id) = 0
    - min(customer_lifetime_value) >= 0

Implementation Steps:

Define quality rules with schema
Set up automated validation
Configure monitoring alerts

Managing Schema Evolution

Handle schema changes safely and systematically:

# Schema Version Migration
schema_version:
  - [_until, "20240907", "1"]
  - [null, null, "2"]

Key Steps:

Version schemas explicitly
Define clear migration paths
Test changes in isolation

Dynamic Model Management

Automate model updates based on performance:

# Performance-Based Updates
job_name: clv.model.dynamic
execution.additional_arguments: [" --iterations", "1", " --depth", "4", " --performance_threshold", "0.8" ]
execution_profile: databricks_notebook

Implementation Guide:

Define performance metrics
Set up retraining triggers
Configure automated validation

Ready to Build Better Data Pipelines?

Start implementing these best practices with Trel’s automated platform