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Also known as: Dataset Versioning
Managing and tracking changes to datasets over time to ensure reproducibility and lineage.
DESCRIPTION
Data versioning records changes to datasets, enabling reproducible experiments, rollback, and auditability across pipelines. It uses content addressing, metadata, and immutable snapshots to manage updates and branching. Tools like DVC, lakeFS, and Delta Lake provide versioned storage, lineage capture, and time travel. Effective versioning complements schema versioning and model tracking to ensure reliable ML and analytics.
ALTERNATIVE DEFINITIONS
DVC Documentation
Data versioning is the practice of tracking changes to data over time so experiments are reproducible and collaboration is easier.
Emphasizes reproducibility and collaboration in ML workflows.
lakeFS Documentation
lakeFS turns your object storage into a Git-like repository, enabling versioning, branching, and committing of data.
Explains Git-like operations for dataset version control in object storage.
Delta Lake
Delta Lake supports time travel, allowing you to query older versions of data using timestamp or version number.
Highlights immutable snapshots and querying historical data versions.
RELATED TERMS
Lineage Reproduction Data Version Control lakeFS Delta Lake Git ML Experiment Tracking Time Travel Model Registry
COMPETING TERMS
RELATIONS
- Broader: Versioning
KNOWLEDGE AREAS
MLOps Data Engineering Data Management Observability & Telemetry Testing & Validation
TARGET AUDIENCE
Data Engineer MLOps Engineer Data Scientist Analytics Engineer AI Engineer
EXTERNAL SOURCES
- Wikipedia - Version control
- DVC - Conceptual guide to data versioning
- lakeFS - Versioned data lake documentation
- Delta Lake - Delta Lake versioning and time travel
TOP QUESTIONS
Why is Data Versioning critical for ML?
It ensures experiments are reproducible and results traceable. You can recreate training sets and diagnose model changes reliably.
Category: Relevancy
What techniques enable Data Versioning?
Content hashing, immutable snapshots, branching, commit metadata, and storage-layer change logs are common techniques.
Category: Overview
What is the biggest misunderstanding about Data Versioning?
Many assume versioning only applies to code. Data changes often drive outcomes, so tracking them is equally vital.
Category: Misunderstanding
How does Data Versioning relate to lineage?
Versioning records states; lineage records transformations and dependencies. Together they enable full traceability and audits.
Category: Relation
What are common mistakes with Data Versioning?
Versioning only at file names, missing metadata, and failing to link versions to models and features. This hampers reproducibility.
Category: Mistakes
How does Delta Lake support Data Versioning?
Delta Lake uses transaction logs and immutable snapshots to provide time travel and consistent historical queries.
Category: Application
What are risks of skipping Data Versioning?
Irreproducible results, conflicting updates, and difficulty debugging production issues. Compliance and audits suffer without traceability.
Category: Risks
How do teams start with Data Versioning?
Adopt tools like DVC or lakeFS, define versioning policies, and integrate with CI and model tracking.
Category: Learning
What’s the difference between data and schema versioning?
Data versioning tracks dataset content changes; schema versioning tracks structure changes. Both are needed for robust pipelines.
Category: Comparison