Keeping Data in Sync Across Time with Data Version Control
Introduction
In today's internet age, you must act by making data-driven decisions to grow your business and ensure sustainability in areas where competition is high. In order to take data-driven actions, you must first invest in building robust and scalable data infrastructures. However, due to the development of technology and the increase in internet usage, the sources of the data produced that you will use in data infrastructures are changing.
This data source may sometimes be sensor data in an automation system, or sometimes it may be user activities in a mobile application. In addition to that, as the size of the data produced increases rapidly, you should keep your data up-to-date and synchronized over time with the data infrastructure you have built.
Many organizations and businesses face the problem of keeping data synchronized across time and applying various methods to feed their data infrastructure with accurate and high-quality data. In this article, we will discuss keeping data in sync across time using the data version control approach.
What is Data Infrastructure?
The data infrastructure is an end-to-end data system that is built on top of automated data pipelines to serve clients accurate, in sync, and high-quality data to use for many purposes like machine learning or business intelligence projects. A typical data infrastructure contains the following main data pipeline steps:
Gathering raw data from various data sources such as APIs, user activities, social media, etc.
Checking the quality and accuracy of gathered data for requirements.
Storing raw data on various relational databases, data warehouses or datalakes.
Processing raw data according to the project requirements with ETL scripts.
Serving processed data for mainly machine learning or business intelligence projects.
Automating all these steps to provide up-to-date data to the clients according to time grain.
Why is Data Infrastructure Important?
Most organizations started to invest in data teams with the effect of rising trends in big data and machine learning. The pioneers already make business decisions based on data for healthy and continuous growth. However, there is also no doubt that you cannot get good enough outcomes from your business intelligence or machine learning projects without building a robust and constantly updating data architecture.
For example, when you publish a mobile application, you decide to spend money on advertising for your application. However, you should track both growth and revenue versus advertising expenses with marketing metrics for your app. By providing the automated, accurate and high-quality data flow to your business intelligence system with the data infrastructure to be built at this point, you can make the right data-driven decisions for your business. On the other hand, if there is no automated data flow and check for data quality, your wrong marketing strategies may result in losses as you cannot track the app's growth.
What is Data Version Control?
Data version control is a versioning system with which you can track or manage any changes in data used in your data infrastructure. Like general version control systems in software engineering methodology, you can interact with remote sources to send or receive any version of the data.
Data version control improved this concept for data teams by making available any version of datasets or machine learning models with the support of documentation, continuous integration, and delivery.
Why is Data Version Control Important?
As the type and size of data sources increase, the data flowing into the data infrastructure of businesses can constantly change. For this reason, in order for businesses to achieve good results from their business intelligence projects, they need to keep the data flowing into their systems synchronized across time.
On the other hand, due to the nature of machine learning projects, the model used in the production environment or the dataset feeding this model can be constantly changed by data scientists or data engineers. Version control systems are needed to manage all these changes easily and flexibly.
For example, your data science team decided to develop a churn project, developed a machine learning model, and the project started running in production. While the success rate of the project was sufficient in the first period, it started to decrease after a certain time. Here, since user behavior may change over time, you should synchronize the datasets across time for the model you feed in your project.
Role of Data Version Control for Keeping Data in Sync Across Time
Your business has built a robust and sustainable data infrastructure, but the system must have automated data flow, and data quality control processes must be applied during this data flow. Additionally, the system should be reversible and have backup mechanisms for adverse scenarios.
Considering all these needs, the data version control approach plays a very important role in the data infrastructure. In this way, it prevents you from making wrong data-driven decisions by keeping your data synchronously updated across time, and also with its versioning mechanism, it provides you with great flexibility to take quick action in negative situations in your projects.
In addition to that, it saves data thanks to its reliability and so it allows data teams to move easily and comfortably while trying different developments and technologies.
Conclusion
As a summary of this article, you should build your own data infrastructure for the right data-driven decisions and to make your data systems as robust you need to utilize a data version control approach in your tech stack to be successful in your business. I hope you found this article informative and that it helped you understand the necessity of usage data version control in your data systems!