Modern machine learning pipelines involve a mix of tools for experiment tracking, data preparation, model registry, and more. MLflow, DataChain, Neptune, and Quilt are some MLOps tools serving these needs. However, one critical piece underpins them all: data version control. This is where lakeFS comes in.
lakeFS is not an experiment tracker or ML platform; it’s an infrastructure-level solution that brings Git-like version control to data. In this post, we’ll compare MLflow, DataChain, Neptune, and Quilt in the context of data versioning, and explore how integrating lakeFS with each enhances experimentation, reproducibility, traceability, and data governance.
lakeFS Git-Like Version Control for Data
lakeFS is an open-source platform that applies the principles of Git to datasets stored in cloud or on-prem object stores (e.g., Amazon S3, Azure Blob, GCS, MinIO, etc.). With lakeFS, you can create a data repository and you can branch, commit, merge, compare, and tag dataset versions just as you would with code.
Notably, these operations are lightweight: creating a branch in lakeFS doesn’t duplicate data but instead creates an isolated metadata copy, so it will perform in milliseconds at any scale, and you can experiment without extra storage cost. Each commit yields an immutable snapshot of your data, forming a tamper-proof history for comparisons, audits and rollbacks.
A Foundational Layer for Reliable MLOps
In practice, lakeFS sits as a layer on top of your storage. Downstream tools continue to read/write data via lakeFS’s S3-compatible interface, gaining version control benefits without major changes to their workflows.This design means lakeFS complements tools like MLflow, DataChain, Neptune, and Quilt rather than competing with them.
lakeFS provides the data versioning backbone – ensuring that every experiment or pipeline can refer to a consistent data snapshot – while the MLOps tools handle experiment tracking & evaluation, model registry, metadata logging, and user interface. The result is a more reproducible, traceable, and governed ML pipeline: you know exactly which data was used for each model run, and you can recreate any result by checking out the corresponding data commit.
MLflow – Experiment Tracking with lakeFS for Data Reproducibility
What is MLflow?
MLflow is a widely-used open-source platform for managing the ML lifecycle, including experiment tracking, model packaging, and deployment. It allows data scientists to log parameters, code versions, metrics, and artifacts for each experiment run, making it easier to compare results and reproduce models. However, MLflow by itself does not deeply version the training data – it can record references to datasets or log small artifacts, but it doesn’t provide Git-like version control over large datasets.
How lakeFS complements MLflow:
Integrating lakeFS with MLflow brings true data versioning into experiment tracking. MLflow’s Tracking API lets you log an “input dataset” artifact for each run; by pointing this to a lakeFS path (which includes a repo, branch, and commit ID or tag), you precisely record which version of the data was used. This yields several key benefits:
1. Experiment Reproducibility
Every run can be reproduced by checking out the same lakeFS commit of the dataset. Even as the underlying data evolves, the commit reference in MLflow ensures you can retrieve the exact training data used. There’s no ambiguity about which “version of the CSVs or images” went into training a model – the lakeFS commit hash is the source of truth.
2. Parallel Experiments without Data Copies
lakeFS supports branching of data, so you can create a new branch for each experiment trial without duplicating the entire dataset. For example, if you want to try a new feature engineering on a subset of data, create a branch in lakeFS, make your changes, and point MLflow to that branch. Multiple branches (experiments) can diverge and run in parallel safely, since changes on one branch don’t affect others. This isolation accelerates experimentation and saves storage – no need to manually copy large files for each variation.
3. Traceability & Lineage
By logging lakeFS dataset URIs in MLflow, you establish clear lineage: each MLflow run is linked to a specific data commit. Later, if a model shows unexpected behavior, you can trace it back not just to the code and parameters (which MLflow tracks), but also to the exact data snapshot. lakeFS’s commit log can show what changed in the data between versions, aiding debugging and model validation.
4. Data Governance
lakeFS adds governance around data changes (e.g., requiring review or tests before merging data to “main” branch). Combined with MLflow, this means that experiments are conducted on properly versioned data, and only approved data makes it into production training. For instance, you might protect the main data branch and only merge experiment branches after QA – ensuring models in MLflow’s registry are always trained on high-quality, reviewed data.
In short, MLflow + lakeFS yields end-to-end reproducibility: MLflow covers experiments and models, while lakeFS covers data. They integrate seamlessly – lakeFS is accessed via a standard S3 API, so MLflow logging and loading functions work as usual. The difference is that behind the scenes, you get a consistent, versioned dataset for your experiments. This powerful combo has been noted to elevate machine learning workflows with robust reproducibility.
DataChain – Data Warehousing for AI, Enhanced by lakeFS
What is DataChain?
DataChain (developed by Iterative.ai) is a relatively new MLOps tool focused on AI data management and processing. It serves as a Python-based “AI data warehouse” for unstructured data like images, audio, videos, PDFs, etc. DataChain helps teams curate and enrich datasets: retrieving files from cloud storage, running transformations or AI models (including LLMs) on them, and storing metadata/results in an embedded database. It emphasizes dataset versioning and data lineage tracking, so that each transformed dataset is reproducible and auditable.
Under the hood, DataChain doesn’t store the bulk data itself – the large files remain in your cloud storage (S3, etc.). Instead, DataChain manages references to those files and their versions. In practice, DataChain leverages external versioning mechanisms (like content-addressable storage or S3 object versioning) to keep track of dataset versions by reference. It may also provide experiment tracking and model versioning as part of its suite, aiming to be a one-stop MLOps solution for data + experiments.
How lakeFS complements DataChain:
lakeFS and DataChain address different layers – lakeFS is a dedicated data versioning engine, while DataChain focuses on metadata, transformations, and experiments. Integrating the two can supercharge your data pipeline:
1. Stronger Data Versioning
DataChain’s approach of versioning “by reference” means it relies on the storage layer for actual version control. By using lakeFS as that storage layer, you get a full Git-like versioning engine behind DataChain. Rather than just tracking object versions or hash pointers, you can use lakeFS branches, tags and commits as the references. Every time DataChain produces a new dataset or transformation, you could commit those results in lakeFS. This gives you a full history of data changes with commit messages, diff capabilities, and the ability to rollback if needed – features beyond basic object versioning. lakeFS provides atomic, consistent snapshots of data, so DataChain can reliably reference a set of files knowing they won’t change or disappear unexpectedly.
2. Experiment Isolation and Collaboration
DataChain will often be used to create different data processing pipelines or feature sets for experiments. With lakeFS, you can isolate each pipeline’s data on separate branches. For example, one team can be working on “Branch A” of the data (say, applying a new image augmentation), while another works on “Branch B” (different preprocessing), both starting from the same base dataset. Each team uses DataChain to run their ETL and analyses on their branch. lakeFS ensures these branches don’t conflict, and if one approach turns out superior, you can merge that branch’s data changes into the main dataset seamlessly. This branching model encourages safe parallel development on data, much like Git branches in software engineering.
3. Lineage and Metadata
DataChain itself tracks metadata about how data was produced (operators used, maybe the model outputs, etc.). By integrating lakeFS, you tie that rich metadata to concrete data commits. This gives complete lineage: not only do you know what transformations were applied (from DataChain’s records), but also on which exact data inputs and where the outputs reside. If someone needs to reproduce a result from six months ago, they can identify the DataChain run and the corresponding lakeFS commit of the dataset to fetch the exact inputs.
4. Data Integrity & Governance
lakeFS brings features like access controls (RBAC), audit logs, and retention policies which DataChain alone might lack (as it’s currently focused on functionality). Storing DataChain-managed data in a lakeFS repository means you can enforce who can create or merge branches, require certain checks (via lakeFS hooks) before data is finalized, and maintain an audit trail of all data changes. This is critical for governed workflows in enterprises: e.g., ensuring that any AI-generated annotations on data go through a review process before being merged to the official dataset.
5. Scaling to Large Datasets
DataChain is built to handle large volumes of unstructured data by pushing computation to where data lives. lakeFS is also designed for scalability, proven to handle billions of objects without performance loss. Using lakeFS, you avoid copying data for versioning (branches are metadata-only), which means even very large datasets can have many versions or experimental branches without blowing up storage. DataChain can fetch data via lakeFS’s S3 gateway as if it were a normal S3; behind the scenes, lakeFS ensures those reads are pointed at the correct version. This synergy lets DataChain focus on ETL and analysis, while lakeFS efficiently manages the data diffs.
6. Smart Data Lifecycle Management
As teams scale, managing the lifecycle of AI datasets becomes a major challenge – especially with tools like DataChain that rely on object store versioning. Since DataChain doesn’t own or control the underlying storage layer, it’s difficult to enforce systematic cleanup or retention policies. Over time, this can lead to sprawling buckets filled with orphaned, intermediate, or redundant data that’s hard to trace or delete safely. lakeFS brings built-in Garbage Collection (GC) capabilities that allow you to clean up unused data intelligently, based on your business data retention logic. You can configure policies to retain critical datasets – like those used in production models for years, while automatically removing short-lived experiment outputs after days or weeks. GC in lakeFS is safe – only deleting unreferenced objects – policy-driven, and aligns with governance best practices. This keeps your data lake tidy, cost-effective, and compliant, even as experiments and datasets multiply.
In summary, lakeFS enhances DataChain by providing a robust version control layer under its data management workflows. DataChain users get the benefit of Git-style data ops (instant branching, commit history, safe merges) without leaving their existing tool. And importantly, lakeFS doesn’t replace DataChain’s functionality – rather, it ensures that DataChain’s outputs and inputs are versioned and governed at the data layer. A recent comparison noted lakeFS’s dedicated versioning engine and integration ecosystem as advantages, whereas DataChain’s strength lies in AI-centric metadata and processing (with versioning delegated to underlying storage). Used together, DataChain can act as the “brain” for data prep and experiment tracking, while lakeFS serves as the reliable “memory” of all data states.
Neptune – Experiment Tracking & Metadata with lakeFS Integration
What is Neptune?
Neptune is a cloud-based experiment tracking and model registry tool, akin to MLflow but offered as a managed service (or self-hosted option). It is built to track a large volumes of experiments and metadata for teams, and is known for its slick UI and ability to handle thousands of runs and even monitor long-running model training jobs. Neptune lets you log pretty much any metadata – hyperparameters, metrics, charts, model files, even interactive visualizations – and organize them in a centralized dashboard.
One relevant feature is Neptune’s handling of artifacts: you can log data files or directories as artifacts, and Neptune will calculate a hash for each artifact and store metadata such as the location, size, and file structure. This effectively allows Neptune to track dataset versions used in experiments by storing a content hash and a link (e.g., a file path or S3 URI) for the dataset. However, Neptune itself doesn’t version the data – it simply records the hashes. It doesn’t manage the data content, may not even store it, and only reads it to compute the hash. If the data changes outside Neptune, you’d have to log it again to treat it as a new artifact version.
How lakeFS complements Neptune
By integrating lakeFS with Neptune, teams can strengthen data versioning and reproducibility in their experiment tracking:
1. Explicit Data Version Logging
Instead of pointing Neptune to a generic file path (which could change over time), you point it to a lakeFS URI for your dataset. Neptune will register the artifact and compute a hash as usual, but now that artifact corresponds to a fixed data commit. This ensures perfect reproducibility: if someone wants to rerun that experiment, they can use the same lakeFS commit ID to fetch the data. Neptune even allows querying runs by dataset version or comparing runs to see if they used the same data. With lakeFS, the “dataset version” Neptune sees can be exactly the commit hash, tag or branch name, which is human-meaningful and traceable in the lakeFS UI.
2. Artifact Differencing and Data Diff
Neptune can show if artifacts differ between runs, since it tracks the hash and file list. lakeFS can actually generate the diff of the data itself: which files were added/removed/changed between two commits. By combining these, an engineer can see that Run 5 and Run 6 used different data versions; Neptune shows different artifact hashes, and lakeFS can show which files changed between those commit hashes. This is powerful for debugging model differences caused by data. Without lakeFS, Neptune would tell you two dataset artifacts differ, but you’d manually have to figure out what changed in the data. With lakeFS, you have a built-in diff for the data.
3. No Data Duplication for Experiments
Neptune doesn’t copy dataset files into its system; it references external data (optionally on S3). lakeFS fits naturally here: you keep one copy of the data in your object store, and create lakeFS branches for different experiment needs. Each experiment logs the lakeFS branch or commit as an artifact. There’s no need to create separate physical copies of data for Neptune to track versions – lakeFS branching is zero-copy. This saves storage and avoids synchronization headaches since everyone always references the central lakeFS store, at specific commits.
4. Data Governance and Access Control
In teams, not everyone should access all data freely. lakeFS allows fine-grained access control, e.g., only certain users can promote a dataset to the “production” branch, etc.. Neptune integration means you can enforce that experiments logged in Neptune only use data from certain branches. For example, junior researchers might only have access to a “staging” data branch; once their work is vetted, senior engineers merge that data to “production” which future official experiments use. Neptune doesn’t itself enforce such policies on data, but by coupling with lakeFS which can restrict branch access, you indirectly enforce governance on experiments. Moreover, every access to data through lakeFS can be audited – so you could trace which user fetched which data version, complementing Neptune’s logging of who ran what experiment.
In essence, Neptune + lakeFS marries experiment tracking with enterprise-grade data version control. Data scientists can continue using Neptune’s friendly UI and APIs to track experiments, and behind each experiment’s data artifact is a lakeFS commit that guarantees reproducibility. The integration is straightforward: Neptune treats lakeFS like any S3 source. The payoff is significant in traceability and confidence – any model metric tracked in Neptune can be traced back to the exact data that produced it, with lakeFS ensuring that data is consistent and accessible in the future.
Quilt – Data Packaging on S3 with lakeFS for Branching and Control
What is Quilt?
Quilt is an open-source data management tool that provides a “versioned data hub” on top of cloud object storage (primarily AWS S3). The idea behind Quilt is to make data discovery, sharing, and collaboration an easy package manager for code. With Quilt, teams can organize data into packages, which are versioned bundles of files on S3, with pointers to the exact S3 objects and versions.
Quilt emphasizes data version control and traceability to ensure data integrity and provenance. It essentially acts as a layer that catalogs data, stores metadata alongside the data, and enables users (even non-engineers) to find and pull specific dataset versions easily. Quilt leverages S3 features – like enabling S3 object versioning – under the hood to track file changes. It provides a friendly interface – web UI or CLI – for browsing and searching datasets, and can integrate with data lakes by storing metadata that is queryable (e.g., via AWS Athena). In short, Quilt brings an approachable interface for working with versioned data in S3, aiming to empower broader teams beyond just engineers, to collaborate on data.
How lakeFS complements Quilt:
At first glance, Quilt and lakeFS both deal with data versioning on object stores – but they operate at different levels and can be used together to cover more use cases:
1. Branching and Experimentation
Quilt’s model typically involves publishing versioned packages (each package version is immutable once published). It’s great for sharing stable datasets, but less focused on the process of getting to that version. lakeFS excels in the experimentation phase by allowing many intermediate versions (commits) and parallel branches of data. For example, suppose you are curating a new “Customer Dataset v2”. With lakeFS, you can branch off main into feature-update branch, add new data files or make modifications, and iterate, possibly creating multiple commits as you refine. You can even have multiple branches for different approaches to curating the data. Once you’re satisfied, you merge to main and perhaps use Quilt to package and publish the curated dataset as an official version (so that non-technical users can easily access it via Quilt). lakeFS thus provides the Git-like workflow leading up to a published dataset, whereas Quilt provides the distribution and discovery mechanism for the final dataset version. Without lakeFS, teams might do this “staging” informally, e.g., copying data to dataset_draft/ folders, which is error-prone and lacks traceability.
2. Data Versioning vs. Package Versioning
lakeFS versioning works at the repository level, snapshotting the entire collection of data in the repo. Quilt’s versioning is at the package level – each package can be versioned independently. When using them together, you might maintain a lakeFS repository that contains multiple datasets or logical groupings, and you can still publish each grouping as a separate Quilt package. lakeFS ensures that within each commit, the data across packages is consistent which can be especially useful if there are dependencies between datasets. If something goes wrong with a Quilt package, you can refer back to the lakeFS commit it originated from to see the context or even reproduce how that package was built.
3. Data Governance and Compliance
lakeFS offers features like branch protection and audit trails, which can complement Quilt’s usage. Imagine an organization where Quilt is used to allow scientists to freely browse data versions. You could use lakeFS to implement rules such as: only data that has passed QA and is merged to a protected branch will be packaged in Quilt. This prevents “experimental” or faulty data from accidentally being shared organization-wide. Additionally, lakeFS’s immutable history can serve compliance needs, similar to S3 Object Lock but with more control. Quilt recommends enabling S3 Object Versioning for integrity; lakeFS essentially provides a more user-friendly and controlled versioning than raw S3 versioning.
4. Smart Data Lifecycle Management
As teams scale, managing the lifecycle of AI datasets becomes a major challenge – especially with tools that rely on object store versioning, similar to DataChain in this case. Since Quilt doesn’t own or control the underlying storage layer, it’s difficult to enforce systematic cleanup or retention policies. Over time, this can lead to sprawling buckets filled with orphaned, intermediate, or redundant data that’s hard to trace or delete safely. lakeFS brings built-in Garbage Collection (GC) capabilities that allow you to clean up unused data intelligently, based on your business logic. You can configure policies to retain critical datasets – like those used in production models for years, while automatically removing short-lived experiment outputs after days or weeks. GC in lakeFS is safe (only deleting unreferenced objects), policy-driven, and aligns with governance best practices. This keeps your data lake tidy, cost-effective, and compliant, even as experiments and datasets multiply.
In summary, Quilt + lakeFS marries user-friendly data discovery with rigorous version control. Quilt makes it easy for teams to collaborate on data by sharing versioned data packages, and lakeFS ensures those versions are created through a controlled, reproducible process with branching, commits, and all the goodness of Git-style workflows. For MLOps engineers, this means you can confidently provide data snapshots to your stakeholders via Quilt, knowing that each snapshot came from a governed lakeFS commit. And if any questions arise about a dataset version, you can dive into the lakeFS repo history to inspect changes or even restore a previous state if needed – capabilities beyond Quilt’s scope, enabled by lakeFS.
Feature Comparison Table
To clarify the roles of each tool and how lakeFS augments them, the table below compares MLflow, DataChain, Neptune, and Quilt on key areas, and notes the impact of integrating lakeFS:
| Capability | MLflow | DataChain | Neptune | Quilt |
|---|---|---|---|---|
| Primary Focus | Experiment tracking and model lifecycle | Unstructured data processing and metadata logging | Experiment tracking, metadata management, and model registry | Immutable data packaging and discovery layer on top of S3 |
| Ideal Use Case | Logging experiments, metrics, artifacts; model reproducibility | AI-driven data prep and curation pipelines with basic tracking | Scalable experiment tracking for ML teams with rich visualization and collaboration features | Publishing versioned datasets for sharing and reuse |
| Experiment Tracking |  Core feature |
 Partial – included as part of broader data/ML pipeline tooling |
Core feature |
 Not a tracking tool |
| Data Versioning | Partial – via artifact logging and external paths; no built-in dataset control |
Partial – via references; potentially DVC-integrated but with limited control |
Partial – via artifact hashes; not built for full data lineage or rollback |
Partial – via S3 object versioning and package snapshots; limited support for branching or rollback |
| Metadata Handling | Experiment metadata: parameters, metrics, tags, artifact URIs | Data-centric metadata: transformation lineage, AI outputs, basic dataset annotations | Rich experiment metadata: parameters, metrics, file-level metadata | Dataset-level metadata: documentation, previews, tags, schemas |
| Integration with lakeFS | Documented – lakeFS URIs can be logged for reproducible data input |
Conceptual – compatible via DVC-style paths or lakeFS S3 gateway |
Informal – can use lakeFS via S3 endpoint; no native plugin |
Informal – can build Quilt packages from data in lakeFS-managed buckets |
| Value from lakeFS | Reproducible runs tied to commits- Branch-per-experiment workflows- Data diff | Stronger dataset lineage via lakeFS branches- Adds garbage collection and governance controls | Guarantees around dataset immutability and lineage- Better traceability with commits | Controlled publishing from lakeFS-staged data- Recovery via previous commits |
| Data Governance Support | None built-in; external access control needed |
Minimal – basic SSO in Studio; no RBAC or audit |
None built-in; relies on external infra |
None; access governed by S3 policies or layered systems like lakeFS |
| Scalability | Proven scalable via backend DB + object storage |
Designed for large unstructured data, but relies on S3 versioning; lacks garbage collection support |
Scales for high-volume experiment tracking |
Scales with S3; metadata catalog can grow large with proper infra (e.g. Athena, Elastic) |
| Open Source | Yes |
Yes |
Core offering is open source |
Yes |
Conclusion
In the landscape of MLOps, tools like MLflow, DataChain, Neptune, and Quilt each tackle distinct challenges – from experiment tracking to data preparation to dataset distribution. lakeFS acts as the missing piece that ties them all together at the data layer. By introducing Git-like version control for data, lakeFS doesn’t replace these tools; it enhances them. MLflow and Neptune become more potent when every experiment’s data is versioned and immutable. DataChain’s AI data warehouse gains reliability and collaboration through lakeFS’s branching and commit history. Quilt’s data hub benefits from lakeFS’s ability to safely evolve data through experimentation before it’s packaged for wider use.
For MLOps engineers and technical decision-makers, adopting lakeFS alongside your existing MLOps stack can significantly improve experimentation speed (no more clunky data copying), reproducibility (one-click access to old data versions), traceability (audit trails of how data changed over time), and governance (permission and policies on data similar to code repos). It ensures that as your data lake grows, you maintain control and insight into every change – just as Git does for software code. The integration is generally seamless because lakeFS presents itself as an S3-compatible storage; your tools continue to operate as usual but now with a powerful versioning layer underneath.
In summary, lakeFS + MLOps tools = a complete ML lifecycle where code, models, and data are all versioned and managed. This combination leads to more reliable ML outcomes and easier collaboration across teams. Whether you’re tracking thousands of experiments in MLflow/Neptune, orchestrating complex data pipelines in DataChain, or sharing curated datasets via Quilt, adding lakeFS to the mix will give you confidence that you can always reproduce results and understand how your data evolved. In an era where “data is the new code” for ML projects, treating data with the same rigor by using lakeFS is a wise investment that supercharges the capabilities of your entire MLOps toolkit.
