Data Version Control Best Practices to Keep Up with the Evolution of Information

By Srikanth
7 Min Read
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With the development of technology in recent years, the use of the internet has been increasing rapidly. This has caused the popularity of digital products to become widespread and has exponentially increased the size of the data produced by users every day. Unlike organizations in the past, those that are open to development in the modern age invest in processing big data and making data-driven decisions.

However, in order to develop data analytics-related projects that are successful and sustainable, organizations must first build a robust and flexible data infrastructure. Since data flows from many different sources to the systems to be built, automated control mechanisms for the management, quality, and freshness of the data become very crucial.

Many businesses try to achieve the best results by trying different tools for control mechanisms in the systems where they have established automated data flow. In recent years, the data version control method has come to the fore among these solutions. In this article, we will discuss the benefits and the best practices of this approach.

What is a Data System?

The data system is a general definition of the set of processes that ingest raw data as input and send analyzed or processed data as output. The system can fetch raw data from a variety of sources, such as server logs, events, IoT, etc. Raw data can be stored in relational or non-relational databases according to the purpose and scope of the project. Data teams create special datasets by processing raw data, using complex or simple logic with ETL scripts to ensure project requirements. These datasets can be used for machine learning or reporting projects.

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The best practice with data systems is that every stage of data flow should be automated with respect to a certain time grain. In addition to that, there should be automated check pipelines that control the quality and accuracy of the ingested data. The main aim of the data system is to serve stakeholders with up-to-date, high-quality, and reliable data that can be used for many purposes with automated data pipelines.

As mentioned above, you cannot achieve your business goals in your data analytics projects without building reliable data systems. You can make data systems reliable by feeding them with high-quality data. Therefore, most technology companies today focus on developing data quality check solutions by applying different approaches.

What is Data Version Control?

Data version control is an advanced management system that can trace the modifications in data that flows in your systems. You can commit, pull, or push any changes in the data by connecting with the remote server as applied in the traditional software development lifecycle.

The main serving area of this methodology is that each data team member can reach any version of a raw or processed dataset with its detailed keynotes, and ensure that data quality problems are solved.

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Data Version Control Best Practices

When you build strong and flexible data architecture, you will have no difficulty in obtaining profitable outcomes from the projects you develop. One of the keys to this is to try to keep the data quality at the maximum level. However, at this point, you should integrate the data version control approach into your systems with the right steps. Otherwise, you may incur losses and waste time by trying different methods. Let’s discuss the best practices for using data version control and integrating it into infrastructure:

Work with Data Contracts

Data contracts are official agreements that can contain types, definitions, scope, schema, or any detail of the data sent from producer to consumer. It is highly recommended that you build your data quality check mechanism based on these data contracts. Since all the details of the data flowing into the system have been previously agreed upon in the contract, the possibility of an issue with the quality of the data is very low. This helps eliminate complexity and uncertainty regarding the data flowing into the system.

Apply CI/CD Pipelines

In the real world, data flows into big data systems from many different sources. Proceeding with manual processes for version and quality control of the data in the entire system can lead to extraordinary waste of time and cause human error. On the contrary, working on the architecture of a modern automated continuous integration and continuous delivery (CI/CD) pipeline can contribute to reducing the workload and output time of projects. In this way, you can also minimize unexpected manual errors.

Integrate System Alerts

You can build an efficient quality and version control process on a strong data infrastructure, but you may encounter unexpected errors or bugs in the production environment. Therefore, it is of great importance to develop an alert system that will send notifications for unexpected situations and integrate it into the data infrastructure. This way you can prevent possible major damages by taking early action against errors and easily balance the working load of the whole system.


In today’s data-driven world, organizations that embrace change and invest in data analytics hold the key to success. Achieving this requires not just the right tools, but also a robust and flexible data infrastructure. Data version control emerges as a powerful solution, offering a range of benefits and best practices to keep pace with the evolving information landscape. By implementing data contracts, CI/CD pipelines, and an integrated alert system, organizations can ensure data quality, minimize errors, and optimize project outcomes. Ultimately, embracing data version control helps businesses build a reliable data foundation, drive profitable results, and sustain better in the ever-changing technological landscape.

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