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Never Rely on Open Source Tools for Data Validation

blog | 5 min read

Never Rely on Open Source Tools for Data Validation

Can You Rely Open-Source Tools for Data Validation?

With strict budgets to adhere to, adopting free, open-source tools for data validation seems a no-brainer, especially with how powerful automation can be. The trouble is open-source software is a bigger drain on resources than you think.

On average, a company’s developers spend 25% of their time researching, testing, and maintaining open-source code. If you pay a developer £23 per hour, their time spent working on open-source software is actually rather expensive.

Here you’ll learn the pros and cons of open-source data validation tools, helping you decide when it’s the right time to upgrade to a paid data platform.

The Benefits of Open-Source Tools for Data Validation

Open-source data validation software is perfect for use cases where you’re just starting out. Perhaps, you’re a solopreneur or you run a small business. In that case, you may not have the budget to invest in a commercial tool.

Furthermore, open-source code tends to be led by a community of passionate data engineers building code which adapts to new trends and developments such as machine learning algorithms.

These developers, and users of the tools, form a helpful community that is often happy to assist with any issues you run into.

All in all, using open-source tools for data validation is a sensible starting point which can consist of various data sources and data connectors. But, as your business and data pipeline expands, you’ll need more robust data validation software.

Reasons You Shouldn’t Rely on Open-Source Tools for Data Validation

Open-source data validation software has serious pitfalls including the security, functionality, and usability – all of which contribute towards lost time, poor data quality and issues migrating throughout your data warehouse.

1. Lack of User Support

Yes, there’s a helpful community of open-source software users that might come to your aid when you run into an issue. But this means waiting for responses to come in, which at times, can take weeks, if not, months.

If, for example, you were cleansing data in preparation for a time-critical campaign, this would be a serious roadblock.

With a commercial tool, this isn’t an issue – instead, you have a tech support team on hand to assist whenever you need them.

Furthermore, these open-source tools often require a range of skills from simple database administration and cloud computing to in-depth data science and Python knowledge. When in-house skills are lacking, you’ll often need the advanced features and user support found in a commercial tool to help fill that skills gap.

2. Limited Functionality

Open-source software lacks the advanced features associated with commercial data validation tools. Hopewiser, for instance, has a simple-to-use, user-friendly dashboard with convenient account management tools and usage reports built-in. 

With limited features, it becomes increasingly difficult to integrate open-source software with existing systems as it creates data silos. These prevent multiple departments from accessing the same datasets which makes data profiling harder and stunts collaboration. Plus, modern companies see the benefits of having a technology stack with various tools and APIs working together in harmony allowing users to follow efficient and effective workflows.

Additionally, whilst open-source platforms allow a variety of file formats including CSV, JSON and XML, you’ll need to find a plug-in which allows for them – and as they are updated far less frequently compared to commercial tools, you’ll likely run into frequent usability and security issues.

3. Security Risks

Open-source data validation tools may not prioritise data security. The code isn’t proprietary making it easier for hackers to go in and find vulnerabilities in the systems.

Furthermore, proprietary data validation software must adhere to strict compliance laws, such as GDPR. If a company works with sensitive client data, it needs a secure, compliant file system to avoid leaks and legal issues.

4. User Experience

Commercial data validation tools are much more likely to have a user-friendly interface that employees of varying tech skills can navigate. If employees from other departments, for example, marketing, need to clean data they work with, such as email addresses, the task doesn’t necessarily need to go to a skilled IT professional.

Open-source software is less refined. It’s notorious for having limited documentation and access to different data types, making it difficult to install and onboard users. 

5. Long-Term Costs

Though open-source tools are technically free, there are still costs involved. As the user experience is lacking, it takes time and resources to train new employees on the system. Plus, employees are less productive when systems are complex and lack advanced features.

As mentioned above, developers must spend time developing and maintaining open-source code at the company’s expense. Overall, it is more cost-effective in the long term to invest in a proprietary tool like Hopewiser.

Final Word

Free data validation tools make sense if you have a limited budget. But they simply can’t meet the demands of growing companies. An expanding business must be concerned with data security and compliance. It must have streamlined workflows on user-friendly tools that can be adopted across departments.

Hopewiser has the capacity to help you cleanse and maintain expansive address and email lists easily. With accurate, up-to-date data on your side, you’ll make better business decisions, enhance productivity, and increase conversions. Sign up for a free trial to access our robust set of data validation tools.



Topic: Data