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You can do that with no effort by using the right software! A good data version control tool will allow you to have unified data sets with a strong repository of all your experiments.

Neptune is the most scalable experiment tracker designed with a strong focus on teams that train foundation models. It lets you monitor months-long model training, track massive amounts of data, and compare thousands of metrics in the blink of an eye.

Pachyderm is a complete version-controlled data science platform that helps to control an end-to-end machine learning life cycle. It comes in three different versions, Community Edition (open-source, with the ability to be deployed anywhere), Enterprise Edition (complete version-controlled platform), and Hub Edition (a hosted version, still in beta).

When you find a problem in a previous version of your ML model, DVC saves your time by leveraging code data, and pipeline versioning, to give you reproducibility. You can also train your model and share it with your teammates via DVC pipelines.

DVC can cope with versioning and organization of big amounts of data and store them in a well-organized, accessible way. It focuses on data and pipeline versioning and management but also has some (limited) experiment tracking functionalities.

Git Large File Storage (LFS) is an open-source project. It replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server like GitHub.com or GitHub Enterprise.

Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.

The explosion in the volume of generated data forced organizations to move away from relational databases and instead store data in object storage. This escalated manageability challenges that teams need to address before realizing the full potential of their data.

In software engineering, the solution to this in software engineering is Git, which allows engineers to commit changes, create different branches from a source, and merge back our branches to the original, to name a few.

Data version control is the same paradigm for datasets instead of source code. Live data systems constantly ingest new data while different users experiment on the same datasets. This can easily lead to multiple versions of the same dataset, which is definitely nothing like a single source of truth

For example, in the context of machine learning, data scientists can test their models to increase efficiency and make changes to the dataset. With this type of versioning, teams can easily capture the versions of their data and models in Git commits, and this provides a mechanism to switch between these different data contents.

The result is a single history for data, code, and machine learning models that team members can traverse. This keeps projects consistent with logical file names and allows you to use different storage solutions for your data and models in any cloud or on-premises solution.

Data versioning is based on storing successive versions of data created or changed over time. Versioning makes it possible to save changes to a file or a certain data row in a database, for instance. If you apply a change, it will be saved, but the initial version of the file will remain as well.

That way, you can always roll back to an earlier version if there are problems with the current version. This is essential for people working in data integration processes because incorrect data can be fixed by restoring an earlier, correct state.

lakeFS is a version control system located over the data lake and based on Git-like semantics. Data engineers can use it to create isolated versions of the data, share them with other team members, and effortlessly merge changes into the main branch.

lakeFS supports managing data in AWS S3, Azure Blob Storage, Google Cloud Storage, and any other object storage with an S3 interface. The platform smoothly integrates with popular data frameworks such as Spark, Hive Metastore, dbt, Trino, Presto, and others.

This open-source project integrates a versioned database built on top of the Noms storage engine and allows for Git-like operations on data. If you use a relational database and want to continue using it while also having version control capabilities, Dolt is a good pick.

How does Dolt work? It relies on a data structure called a Prolly tree (a Prolly tree is a block-oriented search tree that brings together the properties of a B-tree and a Merkle tree). This combination works well because B-tree is used to hold indices in relational databases, allowing you to balance its structure and providing good performance when reading or writing from a database.

Git LFS integrates seamlessly with every Git repository. But if you decide to use it, expect your code and files to live there. This means that you have to lift and shift your data to coexist with your code

DVC was designed to work with version-controlled systems like Git. When you add data to a project using DVC commands, it will upload the data to a remote storage service and generate a metadata file that points to that location.

Next, the metadata file will be added to a Git repository for version control. When data files are modified or added/removed, the metadata file is updated, and new data is uploaded. That way, you can keep track of data and share it with collaborators without actually storing it in the repository by using the metadata files.

The open-source project Nessie provides a new level of control and consistency around data too. Nessie draws inspiration from GitHub, a platform where programmers create, test, release, and update software versions.

By extending analogous development methodologies and concepts to data, Nessie enables data engineers and analysts to update, restructure, and repair datasets while maintaining a consistent version of truth.

To provide consistent data version control, Nessie leverages CI/CD. Data engineers and analysts can generate a virtual clone of a data set, update it, and then merge it back into the original data set.

Data practitioners who use the right data version control tools to handle the scale, complexity, and constantly changing nature of modern data can transform a chaotic environment into a manageable one.

As datasets grow and become more complex, data version control tools become essential in managing changes, preventing inconsistencies, and maintaining accuracy. This article introduces five leading solutions that practitioners can rely on to handle these daily challenges.

A Comprehensive Guide for Data Scientists Explore the essentials of data version control using Python. This guide covers isolation, reproducibility, and collaboration techniques, equipping you with the knowledge to manage data with precision.

Einat Orr is the CEO and Co-founder of lakeFS, a scalable data version control platform that delivers a Git-like experience to object-storage based data lakes. She received her PhD. in Mathematics from Tel Aviv University, in the field of optimization in graph theory. Einat previously led several engineering organizations, most recently as CTO at SimilarWeb.

A data version, also known as a world version,[1][2] is a positive integer used in a world saved data to denote a specific version, and determines whether the player should be warned about opening that world due to client version incompatibilities.

Upon selecting and loading a singleplayer world, the game checks if the client has a data version newer or older than the selected world. If it does, the game then prompts the user whether they want to back up their world before playing it if the world is older, or warn them that their world may become corrupted if the world is newer.

Every version of Java Edition since 15w32a, including minor releases and snapshots, has its own data version. The version takes the form of an ever-increasing positive integer unlike client versions. Data versions are necessary because client versions usually cannot be directly compared since they use different formats (i.e., "1.14" and "19w02a" cannot be compared). Data versions may skip numbers between any release.

DVC is a free and open-source, platform-agnostic version system for data, machine learning models, and experiments.[1] It is designed to make ML models shareable, experiments reproducible,[2] and to track versions of models, data, and pipelines.[3][4][5] DVC works on top of Git repositories[6] and cloud storage.[7]

DVC is designed to incorporate the best practices of software development[10] into Machine Learning workflows.[11] It does this by extending the traditional software tool Git by cloud storages for datasets and Machine Learning models.[12]

Data and model versioning is the base layer[21] of DVC for large files, datasets, and machine learning models. It allows the use of a standard Git workflow, but without the need to store those files in the repository. Large files, directories and ML models are replaced with small metafiles, which in turn point to the original data. Data is stored separately, allowing data scientists to transfer large datasets or share a model with others.[6]

DVC enables data versioning through codification.[22] When a user creates metafiles, describing what datasets, ML artifacts and other features to track, DVC makes it possible to capture versions of data and models, create and restore from snapshots, record evolving metrics, switch between versions, etc.[6]

DVC provides a mechanism to define and execute pipelines.[25][26] Pipelines represent the process of building ML datasets and models, from how data is preprocessed to how models are trained and evaluated.[27] Pipelines can also be used to deploy models into production environments.

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