Data Versioning

Every potential manner of modifying datasets and the manner we analyze our datasets, which naturally requires changing our code, is an “experiment,” and we want to maintain a count of every “experiment” we undertake. We must manage versions of the data required to test ML models.

  • Data versioning is the process of capturing a precise time in the development of data using a unique version number. 

This machine learning approach is significant because the importance of going back to a specific circumstance that led to the construction of a certain model cannot be stressed.

Importance of Data Versioning

Data versioning in a database is significant because it enables faster data product creation while eliminating mistakes.

Have you ever accidentally destroyed terabytes of production data? Restoring a prior edition of a dataset is far easier than rerunning a backfill task, which might take all day.

Need to detect changed entries in a table in the absence of a reliable last updated column or CDC log? Saving numerous snapshots of the data and checking for discrepancies will suffice.

Reducing the cost of errors and disclosing how data has evolved over time are two methods for a data team to boost its development pace. The trigger that makes this feasible is data model versioning.

Drawbacks of Data Versioning

Because cloud software solutions are widely used, selecting the proper supplier is quite tricky. Additionally, versioning data causes certain data security difficulties and consumes storage space.

  • Choosing the Best Provider – If you decide to use a versioning solution, then you should choose the best one for your organizational requirements.

Numerous cloud providers provide multiple functions and charge varying fees. As a result, it is necessary to weigh your alternatives to achieve cloud cost efficiency. You should evaluate the tools using the basic guidelines: whether open source or not, user-friendly UI, Support for the most common clouds, storage space, and, of course, cost.

  • Security concerns– To safeguard their brand, firms must ensure data security. However, as more information versions are kept, the risk of data loss or leaking increases. This risk is heightened for cloud clients in particular since they merely outsource their IT activities, leaving companies with less control over their data. Organizations must identify and comprehend this vulnerability to develop an optimum data versioning strategy.
  • Storage problems – Whenever the versioned files are large in quantity, versioning might cause problems. This is because a Git repository holds the whole history of every file, and frequent changes to them result in a repository that takes ages to clone and consumes a lot of disk space.

To manage individual files, one way is to use the Git LFS extension. This is a suboptimal option because file sizes are limited to 4GB.

The second approach is to manage versions by appending a firmware version without modifying earlier versions of the material.

If no one else is involved in the project, this is an appropriate approach. In a collaborative atmosphere, when all can enter the same data and may potentially alter it and generate a new dataset, the immediate outcome will be massive shambles.

Furthermore, storing large files alongside code is still a horrific idea for the apparent reason – speed reduction. We don’t want to obtain every dataset used for the lifetime of our project; we only want one.

That is why we need to save those files somewhere. That is the fundamental principle around which all data versioning tools are built.

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Because ML systems are more fragile than you think. All based on our open-source core.

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Data Versioning tools

File versioning can be replaced using specialized tools. You have the option of developing your program or outsourcing it. Such services are provided by a variety of companies, including DVC machine learning and Pachyderm.

Versioning solutions are more appropriate for enterprises that demand:

  • Responsibility: Data versioning control allows you to discover where errors occur and who produces them.
  • Editing: If more than an employee works on data, using a separate tool is more productive. This is because file version control does not allow for real-time cooperative modification.
  • Collaboration: When people must operate from different places, using programs instead of data versioning is more practical.