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How to Ensure Enterprise Data Integrity in the DevOps Era

In this article, we will know about the How to Ensure Enterprise Data Integrity in the DevOps Era.
Submitted by IncludeHelp, on JUL 28, 2022

Data integrity refers to the accuracy, consistency, and completeness of data It entails the establishment and maintenance of a system that data is not lost, tampered with, or corrupted. Also, it calls for a system that makes it easy to find data whenever it is needed. Users of specific sets of data should be able to find and retrieve all data they need with no components or sections lost or separated from their respective sets.

DevOps, on the other hand, is a collection of software development and IT practices, strategies, and tools aimed at shortening the system development life cycle while providing rapid product delivery and scalability without compromising reliability. As DevOps teams accelerate their processes, even with the emphasis on maintaining high quality, it is inevitable for them to take steps that can impair data integrity.

The data ecosystem in modern organizations is becoming more complex day by day. The growing number of customers, the addition of more web-enabled digital devices in the workplace, and the increasing reliance on analytics and machine learning lead to more data accumulation that requires efficient sorting, storage, management, and security.

So how do enterprises level up their data integrity in line with DevOps principles? Here's a rundown of the fundamental measures to take.

Using the right data systems

Organizations may need to use databases, data warehouses, data lakes, or other solutions to effectively organize, store, and make use of the data they have. These are meant to ensure data accuracy, consistency, and completeness but at different scales and for different purposes.

Basically, databases are intended for structured data. They are useful in generating reports, data entry audits, business process automation, and the analysis of relatively small sets of data.

Data warehouses are a step higher than databases, as they are meant for storing large amounts of data accumulated from various sources. It is often used by midsize to large organizations to store various kinds of data intended to be shared with other departments or teams to avoid data siloing.

Meanwhile, data lakes are huge data repositories that tend to contain unstructured and raw data. They are often used in data science research and testing by data scientists and engineers.

Aside from using the right data management system, it is equally crucial to be decisive with the specific data management solution to pick. In the case of data warehousing, for instance, there are many solution providers to choose from. These include Druid, Snowflake, Yellowbrick, and Teradata. The different options have their respective pros and cons. It helps to consult detailed references like this Apache Druid vs Snowflake comparison review.

Choices can be evaluated based on their architecture specs (storage and computing separation, cloud infra support, etc), scalability, performance, and prescribed use cases. In view of DevOps, it is important to have features that support data movement automation, cloud support, agile scalability, and reliable performance with rapid data flow.

Data input validation

As mentioned, data can come from various users. To make sure that only accurate, consistent, and useful data is retained, it is important to undertake input validation. Data coming from customers, end users, and third parties cannot be presumed as accurate and usable as they are. They have to be validated from time to time to ascertain that they truly represent the situations or realities an enterprise is trying to examine.

Customer surveys that have been manipulated or worse fabricated, for example, should not make it to an enterprise's data storage. They only distort analyses and reports and also possibly mislead decision makers. Even with the best data management systems, if the data collected or entered into data warehouses or lakes is incorrect or tampered with, achieving data integrity is out of the question.

Eliminating duplicates and keeping proper backups

Duplicate data is a major problem for enterprises. It skews analytics and prevents organizations from making the best possible assessments and decisions. It is easy to commit data duplicates as organizations try to be as speedy with their processes as possible. There are also those who think that it's better to have duplicates than to have no copies at all.

Data duplicates may be created as a crude form of data backup. This should no longer be the case in the age of modern IT. There are data storage solutions that readily come with backup functions. Cloud storage services, in particular, ensure data accessibility from anywhere across different devices and platforms without duplication and with secure backups.

The removal of duplicate data does not have to be done manually. There are reliable solutions to do such, and many of them are open-source tools like BleachBit, GDuplicateFinder, FSlint, and Rdfind.

Ensuring proper data security

Aside from having proper data backup, it is also vital to have solid access control policies. It is advisable to implement the principles of zero-trust and least privilege. No access by a specific employee or team should be presumed legitimate or secure. Also, only the minimum privilege or access rights should be granted in line with the specific task the employee requesting access is set to accomplish.

Additionally, organizations should have audit trails with their data management systems. Every event involving data generation, access, storage, modification, and discarding should automatically generate audit trails to provide security teams with something significantly useful to work with whenever data breaches happen. These audit trails should be tamper-proof.

Moreover, security audits should be regularly undertaken. This is not necessarily the job of data managers, but it may be necessary to emphasize to the security team the need for audits regarding access controls, backup generation, and other vulnerable areas of data management. Encryption should also be enforced and its effectiveness regularly evaluated. It helps to conduct regular volume and stress tests on databases, data warehouses, and data lakes.

DevOps is mostly about expediting processes and less on security if there is even any bit of security considered. Data integrity should never be compromised even as everyone is trying to speedily accomplish their respective tasks.

Providing training and fostering collaboration

As part of a solid data strategy that takes DevOps practices into account, it is important to bring everyone together to fulfill shared responsibilities. Data integrity amid fast-paced processes is impossible to achieve with just a team or a few key persons trying to make it happen. Everyone has to play a part.

Those involved in the collection and generation of data should be trained to ensure data accuracy, completeness, and consistency. They should be aware of the scenarios wherein they are already unwittingly aiding a data breach or permitting data distortions and inaccurate data accumulation to happen.

It is important to foster a culture of data integrity. Preferably, everyone should learn how to instinctively report the problems they notice and own up to errors or mistakes they commit that could be instrumental to data integrity policy violations.

The takeaway

The speed and reliability thrust of DevOps is unlikely to be attained perfectly. There will always be obstacles that make it difficult to achieve rapid releases over a short period of time. The use of distributed work teams in DevOps also poses the risk of losing control, including the control over data governance.

The pointers discussed above, however, help address the data integrity risks posed by DevOps, since it (DevOps) is not that keen on cybersecurity and its rapidness can create many opportunities for data integrity issues. Using the right data management systems, ascertaining data accuracy and reliability, removing duplicates, having dependable backups, providing employee training, and nurturing collaboration are essential in maintaining data integrity while coping with the DevOps era.

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