Data ManagementHow Open Source Is Integral To Data Analytics

How Open Source Is Integral To Data Analytics

Data generation and consumption are both continually increasing, rapidly expanding as individuals and businesses alike use data on a daily basis.

Especially in the corporate and industrial sectors, data is a vital commodity, used to create effective insight and analytics for data-driven decision-making. 

On any given day, over 2.5 quintillion bytes of data are created, with businesses needing to construct and maintain comprehensive data infrastructure to capture, process, and utilize this incoming wave of data.

For DevOps teams that seek to deliver services as quickly as possible, open-source components can be an incredibly valuable contribution to ongoing projects.

One of the core benefits of using open source software is that it can increase the delivery speed of projects, cutting out much of the initial development stages and supplying refined components to developers.

Yet, alongside speed, DevOps teams also prioritize reliability, collaboration, and security. In the world of data analytics, these core pillars are essential to the effective use and extrapolation of data.

In this article, we’ll dive into the world of data analytics, demonstrating exactly how open source components can streamline development, ensure a high baseline level of data quality, and help to scale analytics with the DevOps model.

How Open Source Software Can Elevate Data Analytics

How Open Source Software Can Elevate Data Analytics

Open source software is now widely used in the world of software development. However, this wasn’t always the case.

OSS was once thought of as a lazier option, with developers pointing out the higher chance of security flaws and potential vulnerabilities that publicly available data could hold.

Flash forward a few decades, and this is now far from the truth. Opens source software is highly secure, incredibly flexible, and can empower DevOps teams to rapidly produce highly complex software in less time.

For the field of data analytics, which relies on speed and accuracy, opens source software is a valuable contribution.

Let’s explore how open source software can elevate the production speed and quality of data analytics.

Free Usage

One of the central reasons that open source software is popular, despite the field of operation, is due to its highly available nature.

When developing proprietary software, even in highly efficient DevOps or Agile environments, businesses will have to wait for their developers to plan, create, refine, and secure any software they want to use.

If a business wants to expand their data analytics suite with a new tool or system, then having to wait may lose them a vital market advantage.

Instead of going through this extended process, businesses can opt to use open source components to radically decrease the total development time.

What’s more, OSS is completely free to use and distribute, making it a powerful method of reducing development cycle costs without losing any functionality.

Without the need for a specific license to distribute OSS, DevOps teams can incorporate it directly into their development cycles and provide data analytics tools in a fraction of the time.

Improving Security Focus

The overall security focus on open source software has gone through a revolution over the past decade.

Open source is now highly regulated, with a mixture of country tech jurisdiction, globally-recognized ISO standards, and increasing SBOM usage making it a much safer field.

There are a number of ways that open source is experiencing improved security:

  • SBOMs – Creating a software billing of materials is now much more common, with businesses having to produce them to document all of the components they use. Especially in regard to OSS, an SBOM can help to localize any vulnerabilities if they occur, improving the security of all systems that utilize open source.
  • Jurisdiction – Countries like the United States have recently updated their rules and regulations around cybersecurity and the minimum standards for open source usage. These changes hold developers up to a higher standard, helping to increase security across the board.
  • ISO Standards – ISO standards for the usage and security of open source software now ensure that any components produced have a much higher level of cybersecurity. 
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For developers that want to use open source components to speed up the production of data analytics tools, these improvements to security allow them to do so without putting their software at risk.

For data analytics, this means faster production while still maintaining the same high security standards of fully proprietary software.

data engineering

Highly Flexible

Data analytics, by nature, is an incredibly flexible field that encompasses a huge quantity of internal processes.

Due to the variety of processes and operations that occur within data engineering, developers need to be able to create comprehensive software that covers a huge range of functions.

Data analytics goes far beyond just the final segment, data analysis, and will include processes like extraction, transformation, loading, and more.

In order to achieve the flexibility that data analytics demands, businesses would have to work tirelessly to produce software for each segment of the overarching process.

While this is certainly possible, it always incurs a large development cost and will drain time and resources from a company. 

As a freely accessible pool of resources, open source software is much more flexible than proprietary software in terms of the production cost, scale, and time allocation.

By opting to use open source software to supplement development, teams can focus on creating highly effective data analytics tools without having to incur huge costs and spend lots of time on each component.

Open source software provides DevOps teams with much-needed flexibility, allowing them to overcome challenges during the production cycle and deliver highly-effective data analytics software as per client requirements.

Final Thoughts

Over the past few decades, open source software has grown from a little-used bank of components into a global pool of highly-useful resources.

Instead of spending additional time creating proprietary software, teams are able to leverage open source components to rapidly construct, produce, and distribute new software for data analytics.

As a field that relies on speed, precision, and low time-to-market rates, data analytics is opportune for enrichment with open source.

Equally, for teams that follow DevOps and attempt to produce error-free software as quickly as possible, utilizing open source can radically diminish the overall scope and complexity of creating data analytics tools.

By utilizing open source, businesses can prioritize the creation of effective data analytics tools in a fraction of the time.

And, due to the increasing awareness of open source security and the maturity of the software supply chain, DevOps teams are now perfectly positioned to integrate and use open source without incurring security risks. 

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