Why data versioning is harder than code versioning in MLOps - Why It Works This Way

Overview - Why data versioning is harder than code versioning

What is it?

Data versioning means keeping track of changes in datasets over time, similar to how code versioning tracks changes in software code. However, data versioning involves handling large files, complex formats, and frequent updates, which makes it more challenging. It ensures that teams can reproduce results, audit changes, and collaborate effectively on data-driven projects. Without proper data versioning, it becomes difficult to trust or reproduce machine learning models and analyses.

Why it matters

Without data versioning, teams risk losing track of which data was used for training or testing models, leading to inconsistent results and wasted effort. It can cause confusion, errors, and mistrust in data-driven decisions. Proper data versioning helps maintain transparency, reproducibility, and collaboration in projects that rely heavily on data. This is crucial for building reliable machine learning systems and making informed decisions.

Where it fits

Learners should first understand basic version control concepts used in code, such as Git. After grasping data versioning challenges, they can explore specialized tools like DVC or Delta Lake. Later, they can learn about data pipelines, data governance, and MLOps practices that build on data versioning.

Mental Model

Core Idea

Data versioning is harder than code versioning because data is larger, more complex, and changes more frequently, requiring specialized tracking beyond simple text diffs.

Think of it like...

Imagine trying to keep track of every change in a huge photo album where photos can be edited, added, or removed, versus tracking changes in a small notebook of text notes. The photo album is like data—big and complex—while the notebook is like code—small and simple.

┌───────────────┐       ┌───────────────┐
│   Code Files  │──────▶│   Git System  │
│ (small text)  │       │ (text diffs)  │
└───────────────┘       └───────────────┘
        ▲                       ▲
        │                       │
┌───────────────┐       ┌───────────────┐
│   Data Files  │──────▶│ Data Versioning│
│ (large, binary)│      │ (special tools)│
└───────────────┘       └───────────────┘

Build-Up - 7 Steps

1

Foundation

Basics of Code Versioning

Concept: Introduce how code versioning works using tools like Git that track changes in text files.

Code versioning tracks changes line-by-line in text files. Tools like Git store snapshots and differences (diffs) efficiently. Developers can see who changed what and when, and revert to previous versions easily.

Result

Learners understand how code changes are tracked and managed with simple text diffs.

Understanding code versioning sets the foundation to appreciate why data versioning is more complex.

2

Foundation

Nature of Data Files

Concept: Explain the characteristics of data files that make them different from code files.

Data files are often large, binary, or structured in complex formats like images, videos, or databases. They can be gigabytes or terabytes in size, unlike small text code files. Data also changes frequently and can be updated partially or fully.

Result

Learners see that data files are bigger and more complex than code files.

Recognizing data file complexity helps explain why simple text-based versioning tools struggle with data.

3

Intermediate

Limitations of Traditional Version Control

🤔Before reading on: do you think Git can efficiently handle large binary data files? Commit to your answer.

Concept: Show why traditional code versioning tools like Git are not suitable for data versioning.

Git stores entire copies of binary files on each change, causing huge repository sizes and slow operations. It cannot diff or merge binary data effectively. This makes it impractical for large datasets or frequent data updates.

Result

Learners understand that Git and similar tools are inefficient for data versioning.

Knowing these limitations clarifies why specialized data versioning tools are necessary.

4

Intermediate

Challenges in Data Versioning

🤔Before reading on: which is harder to version, a text file or a dataset with millions of rows? Commit to your answer.

Concept: Detail the specific challenges that make data versioning harder than code versioning.

Challenges include large file sizes, binary formats, partial updates, data dependencies, and the need to track metadata like data lineage and provenance. Data changes may not be line-based, making diffs complex. Also, storage and bandwidth costs increase with data size.

Result

Learners grasp the multiple dimensions that complicate data versioning.

Understanding these challenges helps learners appreciate the design of data versioning solutions.

5

Intermediate

Specialized Data Versioning Tools

Concept: Introduce tools designed to handle data versioning challenges.

Tools like DVC, Delta Lake, and Pachyderm use techniques like storing data pointers, hashing, and incremental storage to efficiently version data. They integrate with code versioning and support reproducibility and collaboration.

Result

Learners see practical solutions that overcome data versioning difficulties.

Knowing these tools bridges theory and practice in data versioning.

6

Advanced

Data Lineage and Provenance Tracking

🤔Before reading on: do you think data versioning only tracks file changes or also tracks data origin and transformations? Commit to your answer.

Concept: Explain how data versioning includes tracking where data came from and how it was transformed.

Data lineage records the origin, movement, and transformation of data through pipelines. Provenance ensures reproducibility by linking data versions to processing steps and code versions. This is critical for audits and debugging in ML workflows.

Result

Learners understand that data versioning is more than file snapshots; it includes metadata and process tracking.

Recognizing lineage and provenance is key to mastering data versioning in real-world systems.

7

Expert

Scaling Data Versioning in Production

🤔Before reading on: do you think storing every data version fully is practical at scale? Commit to your answer.

Concept: Discuss advanced strategies for efficient, scalable data versioning in large systems.

Production systems use techniques like content-addressable storage, delta encoding, and cloud object storage integration. They balance storage cost, retrieval speed, and consistency. They also handle concurrent updates and integrate with CI/CD pipelines for ML models.

Result

Learners gain insight into the complexity and engineering behind scalable data versioning.

Understanding these strategies prepares learners for designing or working with enterprise-grade data versioning.

Under the Hood

Data versioning systems use hashing to uniquely identify data chunks, store only changes (deltas), and maintain metadata about data origin and transformations. They often separate metadata from data storage, using pointers to avoid duplicating large files. This allows efficient storage and retrieval despite large data sizes.

Why designed this way?

Traditional code versioning tools were designed for small text files with line-based diffs. Data versioning needed to handle large, binary, and complex data formats efficiently, so new designs use chunking, hashing, and metadata tracking to overcome storage and performance limits.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│   Raw Data    │──────▶│ Chunking &    │──────▶│ Hashing &     │
│ (large files) │       │ Delta Storage │       │ Metadata Store│
└───────────────┘       └───────────────┘       └───────────────┘
        │                       │                       │
        ▼                       ▼                       ▼
┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│ Versioned     │◀──────│ Data Pointers │◀──────│ Lineage Info  │
│ Dataset       │       │ & Provenance  │       │ & Provenance  │
└───────────────┘       └───────────────┘       └───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Is data versioning just like code versioning but with bigger files? Commit yes or no.

Common Belief: Data versioning is basically the same as code versioning but for bigger files.

Reality: Data versioning requires different techniques because data is often binary, huge, and changes in ways that text diffs cannot capture efficiently.

Why it matters: Assuming data versioning is the same leads to using wrong tools like Git for large datasets, causing slow performance and storage bloat.

Quick: Can you always diff two versions of a dataset line-by-line like code? Commit yes or no.

Common Belief: You can diff datasets line-by-line just like code files.

Reality: Datasets often have complex structures and binary formats that do not support line-by-line diffs, requiring chunk-based or metadata-based diffing.

Why it matters: Trying to diff datasets as text causes errors and inefficiencies, making version control unreliable.

Quick: Does data versioning only track file changes, ignoring data origin? Commit yes or no.

Common Belief: Data versioning only tracks changes in data files, not their origin or processing history.

Reality: Effective data versioning tracks data lineage and provenance to ensure reproducibility and auditability.

Why it matters: Ignoring lineage leads to irreproducible results and difficulty debugging data issues in ML pipelines.

Quick: Is storing every full copy of data versions practical at scale? Commit yes or no.

Common Belief: Storing full copies of every data version is practical and simple.

Reality: Storing full copies is costly and inefficient; data versioning uses delta storage and pointers to save space.

Why it matters: Not using efficient storage leads to high costs and slow data operations in production.

Expert Zone

1

Data versioning systems often separate metadata from data storage to optimize performance and scalability.

2

Handling concurrent data updates requires careful locking or conflict resolution strategies uncommon in code versioning.

3

Integrating data versioning with ML pipelines involves linking data versions to specific model versions for full reproducibility.

When NOT to use

Data versioning is not necessary for static, small datasets that never change. In such cases, simple backups or snapshots suffice. For real-time streaming data, specialized streaming data management tools are better than traditional versioning.

Production Patterns

In production, data versioning is integrated with CI/CD pipelines to automate model retraining when data changes. Teams use content-addressable storage and cloud object stores to manage large datasets efficiently. Metadata tracking is combined with data catalogs for governance.

Connections

Software Configuration Management

Builds-on

Understanding software configuration management helps grasp the principles of tracking changes and managing versions, which data versioning extends to complex data.

Database Transaction Logs

Similar pattern

Database transaction logs track changes to data over time, similar to data versioning, showing how incremental changes can be recorded efficiently.

Library Archiving in Museums

Analogous process

Just as museums archive artifacts with detailed provenance and condition reports, data versioning archives datasets with lineage and metadata to preserve history and context.

Common Pitfalls

#1 Using Git to version large binary datasets.

Wrong approach:

git add large_dataset.bin
git commit -m "Add dataset"

Correct approach:

dvc add large_dataset.bin
git add large_dataset.bin.dvc
git commit -m "Add dataset with DVC tracking"

Root cause:

Misunderstanding that Git is optimized for text files and cannot efficiently handle large binary data.

#2 Ignoring data lineage and provenance in versioning.

Wrong approach:

Only storing dataset files without metadata or processing history.

Correct approach:

Using tools that track data transformations and link data versions to processing steps and code versions.

Root cause:

Underestimating the importance of reproducibility and auditability in data-driven workflows.

#3 Storing full copies of every data version leading to storage bloat.

Wrong approach:

Copying entire datasets for each version manually or automatically.

Correct approach:

Using delta storage or content-addressable storage to save only changes between versions.

Root cause:

Lack of awareness of efficient storage techniques for large data.

Key Takeaways

Data versioning is fundamentally harder than code versioning due to data's size, complexity, and format.

Traditional code versioning tools like Git are not suitable for large or binary datasets.

Effective data versioning requires tracking not just data files but also lineage and provenance for reproducibility.

Specialized tools and storage techniques are essential to manage data versions efficiently at scale.

Understanding these challenges and solutions is critical for reliable machine learning and data-driven projects.