Data Versioning: Because Data Changes, Like, a Lot!

What is data versioning? Data versioning is the storage of different versions of data that were created or changed at specific points in time.

Data versioning plays a critical role in big data and data-centric software applications because data changes a lot. Every day, data keeps getting added, removed, and modified. To track those changes - It is vital to understand how data has changed over time. It will help to make the important decision.

As a developer, you want to analyse and track the issues by comparing the new data with older ones, and if you find any discrepancy or bug you can easily go back to the older one. In this way, you can avoid any bug raised by the user, and it will buy you some time to solve your bug / correct your data.

Why data versioning is important?

Data Integrity: Data versioning helps maintain data integrity by providing a historical record of changes. It ensures that you can trace back to previous states of the data, helping to prevent unintended changes.

Auditing and Compliance: Many domains have regulatory requirements that mandate data auditing and compliance. Data versioning allows for tracking changes, which is essential for demonstrating compliance and ensuring transparency.

Recovery: When errors occur with data, having a version history can be invaluable. It enables you to identify when and how errors were introduced and simplifies the process of rolling back to a known good state.

Data Analysis: Having access to historical data versions allows to reproduce results and the exploration of data changes over time.

Data Governance: Effective data governance requires the ability to manage, control, and track data changes. Data versioning supports data governance practices by ensuring data is managed systematically.

Testing and Development: In software development and testing, data versioning can be crucial for managing test datasets and ensuring that development and testing environments reflect real-world scenarios accurately.

Data Migration: As data evolves and systems change, data versioning aids in managing data migrations and transitions between different data structures or formats.

How to implement data versioning?

Basically, data versioning means creating a unique reference for the datasets. The unique references can be anything — a number, ID or datetime which is the most common identifier.

Delta Versioning: In this approach, we are going to store only the changes (deltas) between versions. This is when you want to process the data daily and add it to the existing dataset. We could use a metadata table to store the versions. Before we refer that version anywhere, we will make sure we run test cases to validate the data and if we find any problem in the data, we can avoid referring it and investigate the problem further. This is the most space-efficient approach. This works best for larger datasets. For example — Finance, Healthcare, Media and entertainment, etc.

Snapshot Versioning: Create a new copy of the entire dataset with each change. This is straightforward but can lead to increased storage usage. This works best for smaller datasets.

What are the challenges to data versioning?

With each new version, data is increasing, which means we need more storage. For organizations that produce or use large amounts of data, it would therefore be costly to version the data too often. It is important for organizations to find an optimal balance between the benefits of versioning and the costs incurred by storage.

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