Data Versioning- How to Version your Data

AI/ML models are used by businesses to make business decisions. Effective AI/ML models require high-quality data to create accurate forecasts about future situations. That is why data is said to be the “new oil” and why successful businesses need their refinery. Obtaining high-quality data, on the other hand, is a difficult task. 

The preservation of distinct versions of data generated or updated at particular moments is known as data versioning. There are several reasons for making modifications to data. Data scientists may test the ML models to improve efficiency and, as a result, make modifications to the dataset. Datasets can also alter over time as a result of information flow. As a result, archiving older versions of data can assist organizations in replicating the previous environment.

Why is Data Versioning important?

• Preserving the working version while testing

The purpose of AI/ML models is to maximize corporate efficiency. It is common for development teams to experiment with novel methods of increasing efficiency. One of these activities may be introducing a new dataset into systems. However, in pursuing the unknown better, no one wants to jeopardize the prior functioning version. Unsurprisingly, the majority of the engineers’ initiatives result in inefficient experiments. Engineers save the last dataset as a result. If an attempt fails, they simply reload the previous working data set into the pipeline, avoiding potential business loss.

• Measuring the business performance

Datasets can evolve without the assistance of engineers. Each transaction, for example, modifies sales data. Storing sales data from many years over some time can help organizations learn consumer preferences. As a result, data versioning can lead to a more successful firm.

Consider a food firm that offers both plant-based and animal-based products. Over time, this firm can observe the consumer transition from animal to plant-based foods by versioning sales data. As a result, the company may arrange its investment initiatives, marketing expenses, and product catalog based on this information.

• Compliance and auditing benefits

Data versioning can aid internal and external audits and compliance procedures by keeping data from specific timeframes. Furthermore, some data protection requirements, such as the GDPR, require businesses to stay particular data sources. Data versioning can help companies to save time while satisfying such regulations. Companies that have versioned their data may also find it easier to detect fraud.

What are the main formats for data versioning?

There is no standard paradigm for data versioning; however, there are three frequently used standard formats:

The most popular style for expressing various versions is the three-part semantic version number convention. 3.2.4, for example, denotes a specific data version. The left-hand number (3) denotes a significant difference between data versions. The middle numeral (2) denotes new features compatible with previous versions, while the right-hand side number (4) denotes minor bug fixes compared to previous versions.

It is also possible to name data versions based on their state. A dataset, for example, might be incomplete-complete, filtered-unfiltered, cleaned-uncleaned, and so on. Specifying this information may benefit practitioners, mainly when collaborating on a dataset via a cloud system.

A data version can be named based on the most recent process to which it has been exposed—for example, normalized or modified by anything, etc.

The Challenges of Data Versioning

• Storage Space

Training data may take up a lot of space in Git repositories. This is because Git was designed to track changes in text files rather than big binary files. If a team’s training data sets include big audio or video files, this might lead to a slew of issues down the road. Each modification to the training data set will frequently result in a duplicated data set in the repository’ history. Not only does this result in a bug repository, but it also makes cloning and rebasing extremely sluggish.

• Data Versioning Management

When it comes to managing versions, whether it’s code or user interfaces, there’s a general tendency—even among techies—to “manage versions’ ‘ by appending a version number or word to the end of a file name. In the context of data, this may imply that a project has data.csv, data v1.csv, data v2.csv, data v3 finalversion.csv, and so forth. This terrible habit is more than cliche; in reality, most engineers, data scientists, and UI specialists begin with terrible versioning habits.

• Multiple Users

One of the most challenging aspects of working in a production setting is interacting with other data scientists. If you don’t use version control in a collaborative workplace, files will be destroyed, changed, and relocated, and you’ll have no idea who did what. Furthermore, restoring your data to its original form will be tough. This is one of the most difficult challenges in managing models and datasets.

Options for versioning the data

• File Versioning

One method for data versioning is to save versions to your PC manually. File versioning is helpful for:

Small businesses: Businesses with less than a few data engineers or scientists operating in the same area.

Protecting sensitive information: If the data contains sensitive information, it should only be examined and analyzed by a small group of executives and data engineers.

Individual work: When a task is not appropriate for cooperation and several persons cannot work together to reach a common goal.

• Using a data versioning tool

Aside from file versioning, specialized tools are available. You have the option of developing your software or outsourcing it. DVC, Delta Lake, and Pachyderm are among the companies that provide such services.

Data versioning systems are better suited for businesses that require:

Real-time editing: When more than one person is working on a dataset, it is more efficient to use a dedicated tool. This is because file versioning does not allow for real-time editing with a group of individuals.

Collaboration from multiple places: When individuals need to work from separate locations, employing software rather than file versioning is more efficient.

Accountability: Data versioning software allows you to discover where errors occur and who produces them. As a result, the team’s responsibility is increased.

Challenges to Data Versioning

• Limited storage

Each data versioning necessitates more significant storage space. It would be expensive for firms that create or consume vast volumes of data to version the data too frequently. It is critical for businesses to strike an ideal balance between the advantages of versioning and storage expenses.

• Security Issues

Organizations must ensure data security to safeguard their reputation. However, when more data versions are saved, the chance of data loss or leakage grows. This danger is magnified for cloud customers since they simply outsource their IT activities, giving them less control over their data. To develop an optimal data versioning strategy, organizations must assess and comprehend this risk.

• Choosing the right service provider

If you decide to employ a data versioning solution, you should select the most appropriate one that matches your company needs.

Different cloud providers provide additional capabilities and charge varying fees. As a result, it is recommended that you assess the many choices available to achieve cloud cost optimization. You should evaluate the tools using the following criteria:

• Whether open-source or not
• Storage space
• Is there a user-friendly UI or not?
• Support for the most common clouds (AWS) and storage types is optional.
• Cost

Best Data Version Control Alternatives

One of the cornerstones to automating a team’s machine learning model development is data versioning. While developing your system to handle the process might be highly hard, this does not have to be the case.

• DVC

DVC, or Data Version Control, is one of several open-source technologies available to aid data science and machine learning projects. The programme is similar to Git in that it provides a simple command line that can be configured in a few easy steps. As the name implies, DVC is not just concerned with data versioning. It also assists teams in managing pipelines and machine learning models. Finally, DVC will aid your team’s consistency and the repeatability of your models.

• Delta Lake

Delta Lake is an open-source storage layer designed to aid in the improvement of data lakes. It enables ACID transactions, data versioning, metadata management, and data version management.

The technology is more akin to a data lake abstraction layer, filling in the gaps left by typical data lakes.

• Git LFS

Git LFS is a Git extension created by a group of open-source volunteers. By employing pointers instead of files, the programme seeks to eliminate big files that may be uploaded to your repository (e.g., images and data sets).

The pointers are lighter and point to the local sporting goods store. As a result, when you push your repo into the central repository, it updates quickly and takes up less space.

When it comes to data management, this is a pretty lightweight solution.

• Pachyderm

Pachyderm is one of the list’s few data science platforms. The goal of Pachyderm is to provide a platform that makes it simple to replicate the outcomes of machine learning models by controlling the complete data process. Pachyderm is known as “the Docker of data” in this context.

Pachyderm packages your execution environment using Docker containers. This makes it simple to replicate the same output. The combination of versioned data with Docker makes it simple for data scientists and DevOps teams to deploy and maintain the consistency of models.

Pachyderm has agreed to its Data Science Bill of Rights, which describes the product’s core goals: reproducibility, data provenance, collaboration, incrementality, and autonomy, as well as infrastructure abstraction.

These pillars drive many of its features, allowing teams to utilize the platform entirely.

• Dolt

Dolt is a one-of-a-kind data versioning system. Dolt is a database; instead of some of the other solutions provided, just version data.

Dolt is a SQL database that supports Git-style versioning. Unlike Git, which allows you to version files, Dolt will enable you to version tables. This means you may update and modify data without fear of losing the changes.

While the programme is currently in its early stages, there are hopes to make it fully Git and MySQL compatible shortly.

• LakeFS

LakeFS enables teams to create data lake activities that are repeatable, atomic, and versioned. It’s a newbie on the scene, but it delivers a powerful punch. It offers a Git-like branching and version management methodology designed to operate with your data lake and scale to Petabytes of data.

It delivers ACID compliance to your data lake the same way as Delta Lake. However, LakeFS supports both AWS S3 and Google Cloud Storage as backends, so you don’t have to use Spark to get the benefits.

You don’t necessarily have to put in a lot of work to manage your data to reap the benefits of data versioning. For example, much of data versioning is intended to aid in the tracking of data sets that change significantly over time.

Some data, such as web traffic, is simply appended to. That is, data is added but seldom, if ever, updated. This implies that the only data versioning needed to get reproducible results is the start and finish dates. This is significant because, in such circumstances, you may be able to bypass all of the tools mentioned above.

Also Read: Why Is Data Science In Demand?