Managing Data Changes with Data Version Control

In today’s world, data is constantly changing. Managing data changes over time efficiently and securely is essential. Managing data changes can also be a difficult and time-consuming task. Data Version Control is a powerful tool to manage data changes and ensure the quality of the data over time. 

It enables teams to track the data changes and versions over time, collaborate with other team members, and ensure data quality. With data version control, teams can easily manage the entire data change management process from development to deployment.

Defining Data Version Control

Data Version Control is a system for managing and tracking the changes made to data files and datasets used in data engineering, machine learning, and data science projects. Data version control helps teams to collaborate efficiently, ensure the reproducibility of experiments, and keep track of the evolution of data over time. With a data version control system, users can version control their files and metadata, similarly to how software code is versioned using tools like Git.

Overview of Benefits

Reproducibility

Data version control ensures that data science and machine learning experiments can be easily reproduced by other team members or by future project generations.

Collaboration

Data version control facilitates collaboration by enabling multiple team members to work on isolated versions of the data files and track changes made to the data.

Data resilience

Data version control enables data quality hooks on the data. These hooks check the data that is coming in and validate that it is of high quality and 

Record keeping

Data version control provides an audit log that allows you to trace the history of data changes and determine who made them.

Data Recovery

Data files are versioned according to their commit history, enabling time travel and fast recovery in case of errors.

Scalability

A data version control solution needs to be designed to handle large datasets and can efficiently manage files in the petabyte and even exabyte range.

Flexibility & compatibility

Data version control systems need to work seamlessly with the most common data science and frameworks and can also be integrated with code version control systems like Git.

Exploring Tools & Platforms

There are several popular tools and platforms for Data Version Control. But the best ones are:

lakeFS: lakeFS is the most advanced data version control system within the existing solutions. It is located over the data lake and based on Git-like semantics. Engineers can use it to create isolated versions of the data, share them with other team members, and merge changes into the main branch effortlessly. lakeFS integrates with all cloud storage systems with an S3 interface. It also smoothly integrates with popular data frameworks such as Spark, Hive Metastore, dbt, Trino, Presto, and others. lakeFS unifies all data sources in data pipelines, from analytics databases to key-value stores, via a unified API that lets you easily manage the underlying data in all data stores. This is done regardless of the size of the data lake, with milliseconds performance and with zero copies of data files.

DVC: DVC is an open-source tool specifically designed for data version control, mostly for machine learning scenarios. It integrates with popular version control systems like Git and can be used with a variety of data formats and cloud storage providers. Its main limitations are scale data retrieval performance.

Git-LFS: Git Large File System (LFS) is an extension to Git that allows users to version control large files. Git-LFS is popular among software developers and data scientists alike and supports a wide range of file types, but it is not built for large data lakes, and it is not fit for data that is transient.

Quilt: This is a data versioning platform for data scientists and machine learning engineers. Quilt provides a simple and intuitive interface for managing data files and tools for collaboration, version control, and data sharing, although it is lacking some functionalities that are needed in data version control, such as scale and throughput, git-like-actions, etc.

Pachyderm: Pachyderm is an open-source platform for data science and machine learning that provides data version control, data management, and scalable data processing capabilities. It is mostly targeting ML scenarios and less optimal for all the needs of data engineering teams.

Strategies for Implementation

Data version control focuses on helping organizations manage changes to their data, enabling them to access and review different versions of their data sets for improved accuracy and efficiency. Implementing data version control strategies can help organizations stay organized and collaborate better within the team. It also alleviates the stress that comes with managing data changes thanks to its branching and working in isolation features. Finally, it plays an important role in ensuring the accuracy of their information.

Organizations looking to use data version control should start by mapping the specific changes they wish to make and how they will track them. This should include policies and procedures for tracking current and historical data versions, as well as methods for identifying which version is the most up-to-date. It’s also critical to decide who will be in charge of making any necessary changes or updates.

Best Practices to Improve your Data Manageability

Use a centralized repository.

Store data files, metadata, and scripts in a centralized repository and ensure they are versioned using a data version control system.

Version control metadata.

Use data version control systems to track changes to the metadata and ensure that it is up-to-date.

Automate data pipelines.

Automate the data pipelines as much as possible to minimize manual intervention and reduce the risk of errors.

Store raw data.

Store raw data files and processed data to ensure that it is possible to trace the history of data processing steps.

Use version-controlled scripts.

Use version-controlled scripts to process data and store the scripts in the same repository as the data files.

Collaborate with team members.

Encourage collaboration and sharing of data files, scripts, and metadata among team members. When using a data version control, this is done even more efficiently and intuitively.

Conclusion: Achieving Data Versioning

Achieving data versioning is possible. By leveraging the right tools and processes, businesses can be sure that their core data remains intact and that their versions are tracked appropriately. Undoubtedly, Data Version Control provides a centralized and efficient way to manage and track changes to large datasets. This makes it an essential tool for data engineering, data science, and machine learning projects.

Data version control systems allow companies to maintain governance over their information at all times, reducing the risk of errors in their data lakes. With a date version control system in place, teams can rest assured that their production data is always accurate and of high quality – ensuring accuracy and integrity and providing traceability for each update made. Furthermore, these systems can help save money by reducing the cost associated with creating multiple copies of the data, manual work of  duplicating the data, or corruption or loss of valuable information.