The Tool Desk
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Keep large dataset bytes in a content-addressed cache or shared object store, and version lightweight references, manifests, code, and pipeline metadata instead of committing a fresh copy of the data for every branch. Use DVC when the project is organized around Git and ML workflows; use lakeFS when the source of truth is a shared data lake or object store and teams need Git-like branches and commits over that data.
Why does ordinary Git versioning become a storage problem for AI datasets?
Training datasets can be much larger than the code and configuration used to process them. If every branch or release stores another complete copy of the data, storage use grows with the number of versions, transfers become expensive, and engineers may struggle to get a consistent snapshot onto their machines. Putting those dataset bytes directly in Git also makes the repository a poor fit for the workload: the data is large, while the information needed to identify and reproduce a dataset version is comparatively small.
A more efficient design separates the bytes from the version history. Store data in a cache or object store, identify content by hashes or immutable snapshots, and version the references and metadata that say which contents belong to a release. A branch can then point to a different dataset state without requiring a full second copy of every unchanged object.
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“Without duplicating storage” has two different meanings in practice. It can mean avoiding a second local copy when a workspace is materialized, as with suitable DVC link modes, or avoiding a second copy of the underlying objects when creating a data branch, as with lakeFS zero-copy branching. Neither approach means the data has no storage cost: the original bytes still need a durable home, and retained versions still require a retention policy.
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How do DVC and lakeFS differ?
DVC connects data versioning to a Git-centered project. lakeFS provides a Git-like control plane for data already held in a shared object store or data lake. The right choice depends mainly on where the source of truth belongs and how data branches need to be used.
| Decision point | DVC | lakeFS |
|---|---|---|
| Source of truth | Git contains lightweight DVC files and pipeline metadata; data contents live in a cache and a configurable remote. | A shared object store or data lake holds the data; lakeFS provides version and branch operations over it. |
| How a branch relates to data | Git versions references and metadata. A workspace is materialized using configured cache link behavior or ordinary copies. | Branches and commits represent data states over existing objects. lakeFS describes this as zero-copy branching. |
| Typical workflow fit | Repository-centered ML work, targeted file or directory updates, and pipelines coordinated with project code. | Data-lake-centered work, multiple services or pipelines sharing dataset states, and branch or rollback operations at the lake level. |
| Granular operations | dvc add can update a file or subdirectory within a tracked dataset; dvc pull can retrieve selected targets. |
Provides branch, commit, merge, and rollback semantics over the lake. The cited guidance does not describe these as DVC-style per-target pull commands. |
| Main operational caution | Link modes have different safety properties; careless editing can damage a hard-linked cache or violate symlink assumptions. | Keep immutable commits and their referenced objects available for the retention period needed by readers, pipelines, and rollback. |
DVC describes itself as a tool for data management, ML pipeline automation, and experiment management. Its documentation is explicit that “DVC does not replace Git!” Git remains the place for code and versioned project metadata; DVC manages the data relationship and transfers. lakeFS documentation describes Git-like branches, commits, merges, and rollbacks while data remains in place, stating that zero-copy branching eliminates the need for data duplication.
When should an ML project use DVC?
Choose DVC when the dataset belongs to a particular Git project and developers need to switch code and data references together. It works well when the team wants dataset versions represented alongside code, configuration, metrics, and pipeline definitions, while the larger contents stay in a cache and remote storage.
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What goes in Git and what stays out?
- Commit to Git: source code, configuration, metrics, DVC metadata such as
.dvcfiles, and pipeline metadata needed to describe reproducible work. - Keep out of Git: large raw or processed dataset contents. DVC documentation describes those contents as living in a cache and configurable remote.
- Keep credentials local: put secrets in local ignored configuration. Commit only non-secret remote configuration so collaborators can understand the remote setup without exposing access credentials.
A Git checkout by itself is not a copy of the dataset contents. The DVC metadata identifies what the workspace needs; the data must be materialized from the cache or a configured remote. This distinction is useful for repository size, but it also means a collaborator needs access to the corresponding remote or another valid copy of the cache.
How does DVC avoid copying data into each workspace?
DVC can materialize workspace files using reflinks, hard links, or symbolic links. Reflinks use copy-on-write when the filesystem supports them: the workspace initially shares underlying storage with the cached content, but a workspace edit is separated rather than changing the cached original. This makes reflinks a strong option when supported by the local filesystem and DVC configuration.
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Hard links and symbolic links can also avoid allocating a second full set of bytes, but they are not interchangeable with reflinks. A hard link refers to the same underlying file data as the cache; editing through it can corrupt the cached original. A symbolic link points to a target, so its safety depends on the target and path assumptions remaining valid. Use these modes only with the read-only and corruption precautions described in DVC’s documentation. Copy mode is broadly compatible, but it creates ordinary duplicate bytes between cache and workspace, so it does not solve local duplication.
| Workspace mode | Storage behavior | Safety consideration |
|---|---|---|
| Reflink | Copy-on-write sharing where supported; avoids a full initial duplicate. | Requires filesystem support; edits separate from the cached original. |
| Hard link | Workspace and cache refer to the same file data. | Editing linked content can corrupt the cache; follow documented precautions. |
| Symbolic link | Workspace entry points to content at a target path. | Relies on the target and link assumptions remaining valid. |
| Copy | Creates a full ordinary copy in the workspace in addition to the cache. | Broadly compatible, but duplicates local bytes. |
Set cache.type deliberately rather than assuming the most space-efficient mode is automatically safe on every machine. The right configuration depends on filesystem support and on whether workspace contents will be modified. In particular, do not treat a linked workspace copy as an independent writable copy unless its link mode guarantees that behavior.
How does DVC handle dataset changes and partial retrieval?
DVC tracks content using hashes rather than timestamps. That makes checkouts and comparisons depend on file content, supporting reproducible switching between versions and avoiding unnecessary recomputation when the tracked content has not changed. For collections too large to handle as one indivisible unit, DVC’s granular operations can update a file or subdirectory inside a tracked dataset with dvc add, and retrieve selected targets with dvc pull.
Targeted operations are especially useful when a dataset contains many files but a developer or pipeline needs only a subset. Pulling only the needed target reduces unnecessary transfers and local materialization. DVC’s large-dataset guidance specifically advises avoiding full-dataset operations for collections with tens of thousands of files and using granular paths instead.
When is lakeFS a better fit?
Choose lakeFS when the shared object store or data lake is already the source of truth and multiple teams, services, or pipelines need coordinated states over that data. Instead of treating every dataset version as a project-local collection to fetch, lakeFS exposes branch, commit, merge, and rollback operations over the lake’s objects.
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lakeFS describes its branches as zero-copy: creating a branch does not duplicate all the underlying objects. That is useful when branches are needed for experiments, validation, or coordinated data changes but copying the full lake for each branch would be impractical. Commits are immutable, which gives readers a stable dataset state and provides a basis for rollback. The relevant advantage is control over references and states, not elimination of the need to preserve the data itself.
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DVC’s guidance for large datasets points readers toward lakeFS when data is object-store-scale or the file count is very large. That is a scale and architecture signal, not a strict cutoff: the decision should account for how many pipelines share the data, whether data operations need transactional branch semantics, and whether repository-local caches or file-by-file workflows have become a bottleneck.
lakeFS internal documentation describes tiered access in which newer commits are accessed frequently and older commits can be served from lower-cost storage. It also gives an internal example modeled on a 200-million-object repository. That figure is an example in lakeFS documentation, not a generally applicable capacity limit, benchmark, or guarantee for a particular deployment.
How should a team set up a versioned dataset without filling local disks?
Start by deciding what makes a dataset release identifiable and recoverable, then choose the storage and versioning layer around that decision. A useful implementation sequence is:
- Define immutable snapshots. Decide how a release is named and what manifest or hash identifies its contents. Avoid silently changing data that a published training run is expected to reproduce.
- Version reproducibility metadata. Keep code, configuration, metrics, and the DVC or lakeFS metadata under version control. The metadata should let a teammate identify the intended state without storing the full data payload in Git.
- Put bytes in appropriate storage. Store large dataset contents in shared object storage or, for a DVC project, in its cache and configured remote. The repository should not become the storage system for large dataset versions.
- Configure the collaboration path. For DVC, configure a remote accessible to the team, keep credentials in ignored local configuration, and commit only non-secret remote configuration. DVC documentation gives S3 as an example and describes support for cloud, SSH, and other storage platforms.
- Choose DVC materialization deliberately. Prefer reflinks where supported if the goal is to avoid another full workspace copy while protecting the cache from edits. Set
cache.typeintentionally; use hard-link or symlink modes only with the documented precautions. Use copy mode when compatibility matters more than local space. - Fetch only what a job needs. Use DVC’s targeted pull and granular paths where practical instead of retrieving every file in a large collection. For data-lake workflows, use lakeFS branches and commits to identify the desired state without copying the objects for each branch.
- Set retention and cleanup rules. Define how long old snapshots and unreachable objects must remain available. Validate lifecycle and garbage-collection behavior in a staging repository before production use so cleanup does not remove data still needed for reproducibility, active pipelines, or rollback.
- Reassess the architecture as usage grows. If the team increasingly coordinates many pipelines against shared object storage, or repository-local caches and file-by-file operations become a bottleneck, evaluate lakeFS rather than forcing a repository-centered workflow to carry data-lake responsibilities.
What are the main failure modes, and how can teams avoid them?
The most common problems are not caused by versioning metadata itself; they come from confusing references with stored bytes, using an unsafe workspace link mode, or deleting objects without considering what older versions still require.
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A Git commit exists, but the dataset is unavailable
A Git-tracked DVC metadata file is not the dataset payload. If the remote or cache that holds the referenced contents is inaccessible or the contents are no longer retained, a checkout may not be enough to reconstruct the workspace. Maintain access to the remote, use a backup strategy appropriate to the data, and test retrieval of representative snapshots rather than assuming the Git repository alone contains everything.
A workspace edit changed or damaged cached data
This risk is most important with hard links, which share the underlying file data with the cache. Review the configured cache type, use reflinks when supported and appropriate, and follow DVC’s documented read-only and corruption precautions for hard-link and symlink modes. If independent edits are required and safe copy-on-write behavior is unavailable, copy mode is more compatible but costs additional local storage.
A pull transfers much more than the job needs
Do not treat every dataset collection as an all-or-nothing target. DVC supports adding and pulling selected paths; use these granular operations for large collections, especially where file counts reach tens of thousands. This reduces needless retrieval and avoids filling local disks with files a particular job will not read.
Old versions disappear after cleanup
Garbage collection and storage lifecycle policies must agree with the team’s reproducibility and rollback requirements. Before enabling deletion in production, validate the policy against a staging repository and verify that snapshots still needed by active jobs or retained experiments remain retrievable. Immutable commits help identify stable states, but they do not make the underlying object bytes immune to deletion by a separate storage policy.
How do you choose between local efficiency and shared data-lake control?
Use DVC if the central task is to coordinate ML project code and data references through Git, share data through a configured remote, and fetch only the files needed for a checkout or pipeline. Its cache link modes address duplicate local workspace bytes, while its granular operations help avoid unnecessary transfers.
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Use lakeFS if the central task is to version a shared lake as a whole operational asset: multiple services or pipelines need consistent branches and commits, and teams need merge or rollback behavior over object-store data without copying objects per branch. If both patterns appear in an organization, choose the system that owns the source-of-truth state for the workflow in question; the available guidance does not prescribe a universal combined architecture.
In either case, the storage-saving mechanism is not a substitute for data governance. Immutable release identities, access to the bytes behind each reference, deliberate local materialization, and tested retention policies are what make a smaller storage footprint compatible with reproducible training.
Frequently Asked Questions
Does zero-copy branching mean lakeFS branches cost nothing to store?
No. It means a new branch does not copy all the underlying objects. The original data still occupies object storage, and retaining data states or supporting access to them remains an operational storage responsibility.
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Yes. DVC keeps lightweight metadata in the project repository while contents can live in a configurable remote; its documentation describes cloud, SSH, and other storage platforms. Team members still need access to the remote or another valid copy of the cache.
Can DVC’s 200-million-object example tell me how many files my deployment can handle?
No. The 200-million-object figure is an internal lakeFS documentation example modeled on a repository, not a general benchmark or deployment capacity guarantee. Actual performance depends on the storage and workload, and the cited material does not establish a universal threshold.
Should a team combine DVC and lakeFS for the same dataset?
The documented guidance distinguishes repository-centered DVC workflows from lake-centered lakeFS workflows but does not prescribe a universal combined setup. Decide which system owns the dataset state for each workflow before adding overlapping version references.
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