Does data versioning mean what you think it means? Read this overview with use cases to see what data versioning really is, and the tools that can help you manage it.
By **[Einat Orr, PhD.](https://www.linkedin.com/in/einat-orr-359ba6/?originalSubdomain=il), Co founder & CEO at Treeverse**
When we first thought about a tagline for lakeFS, our recently released OSS project, we instinctively used terms such as “Data versioning”, “Manage data the way you manage code”, “It’s git for data”, and any random variation of the three that is a grammatically correct sentence in english. We were very pleased with ourselves for 5 minutes, maybe 7, before realizing these phrases don’t really mean anything, or mean too many things to properly describe what value we bring. It is also commonly used by other players in the domain that address completely different use cases.
We decided to map the world of projects declaring “_Data Versioning_”, “_Manage data the way you manage code_”, and “_It’s Git for Data_” according to use cases.
The pain: Data analysts and data scientists using many data sets, external and internal, that change over time. Managing access to data sets, and the different versions of each data set over time, is hard and error prone.
The solution: An interface that allows collaboration over the data and version management o. The actual repository may be a proprietary database (e.g. DoltHub), or providing efficient access to data distributed within your systems (e.g. Quilt or Splitgraph). These interfaces grant easy access and management of different versions of the same data set. Most players in this category also provide collaboration of other aspects of the workflow. Most popular is the ability to collaborate over ML models. In this category you can find the likes of DAGsHub, DoltHub, data.world, Kaggle, Splitgraph, Quilt, FloydHub and DataLad.
The pain: Running ML pipelines, from input data to tagged data, validation, modeling, optimizing hyperparameters, and introducing the models to production. There’s no simple way to manage this pipeline, and the very many tools used in the process.
** The solution**: MLOps tools. At this point you might be asking yourself, why would Ops tools be mentioned in the context of the “Data Versioning”? Well, it’s because managing data pipelines is a major challenge in ML application life cycle. Since ML is a scientific work, it requires reproducibility, and reproducibility means data + code. There are a few MLOps tools that enable data versioning, and they include: DVC, Pachyderm and MLflow.
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