How to Build Data Frameworks with Open Source Tools to Enhance Agility and Security

Let’s take a look at how to harness open source tools to build your data frameworks.

By Nahla Davies, KDnuggets on October 27, 2021 in Data Democratization, Deployment, Open Source, Security

Open source technology is being leveraged by enterprises of all sizes due to its cost-efficiency and real-time analytics and storage capabilities. Data scientists now have the ability to build entire data frameworks with open source tools that allow them to streamline workflows and make the most out of existing data sets. Linux, Python, and PHP are all examples of open source software that is publicly accessible and widely used for a number of applications.

Data science has become a huge asset, as companies are focused on accelerating their digital transformations. Open source data architecture is currently experiencing widespread deployment in diverse data science projects, and data-driven analytics and business intelligence powered by machine learning is being adopted at a rapid pace. That is because data has become something like a currency, and businesses are looking for new ways to store and address mass amounts of data. To that end, let’s take a look at how to harness open source tools to build your data frameworks.

Building data frameworks with open source

Automation and machine learning are not new, but the push toward analyzing and implementing data in new ways has paved the way for high quality AI models that don’t require extensive programming knowledge. Instead of one-off programs that are used to find solutions for certain data sets, now open source tools can also be used to create production-grade apps powered with AI.

Open source tools are important for data scientists because they offer the resources to build complex data frameworks with or without low-code APIs. As organizations continue to innovate, it's important to have a lock on the operational side as well. Data-driven applications that use microservices and containers can get out of hand. But data scientists can use this data sprawl to their advantage with the ability to update and change services without affecting connected applications.

Open source plays a central role on all levels of infrastructure. For example, the database layer can provide distributed support for services with scalable capabilities. Services act like the messengers that manage streaming between application components and services, all while Kubernetes acts as the key facilitator that manages the infrastructure and scales as needed. This can all be done within an hour total including development and deployment.

Open source benefits

As the saying goes, knowledge is power, and organizations that put data scientists in charge of data realize the benefits of real-time analytics and data-driven decisions. Open source tools make it easier to build these frameworks and allow organizations to have more control over how data sets will run in the future.

According to recent surveys, the average developer has less than five years of experience, which means many organizations are working with less experienced data scientists in an age where data is everything. Fortunately, open source tools are available for everyone to learn and can be easily deployed to simplify the route towards innovation.

Here are five additional benefits of using open source tools in development:

Low-cost deployment

Because open source projects do not have any associated licensing costs, it's less expensive for organizations to give them a try. Additionally, each project has the potential to become an extensive deployment, as several collaborators can all work together to meet the specific project requirements and learn how to use data more efficiently.

Modernization

Modernizing legacy applications is of growing importance across industries as more organizations turn to cloud computing and open source technology. Updating existing applications, reducing costs, and finding opportunities to innovate can all be achieved through deploying open source tools. The open source ecosystem offers a wide range of technologies that run the gamut from basic standard operations to modern, cloud-native applications.

Innovation

Many businesses have been pushed by COVID-19 and digital transformations to find new ways of working and creating solutions. Open source solutions allow organizations to reinvent core business processes within days instead of weeks or months. With open source tools, the speed at which innovation can occur increases dramatically.

Data democratization

Data scientists are in high demand due to the rapid evolution of digital business applications, but not all organizations have the resources available to make use of their expertise. That’s why democratizing data science is important when it comes to enhancing the knowledge of other employees in an organization. Organizations can utilize the strengths of professional data scientists while open source tools increase accessibility so the workload is shared across the organization.

Security

Vulnerabilities increase once data is fed to online platforms through the internet and third-parties. But with open source, it’s possible for data scientists to govern the data. Open source technologies utilize machine learning models that can help clean up a clogged security ecosystem so that organizations can simplify their security protocols. However, there are still a number of concerns regarding security and open source APIs.

Open source security concerns

Like any other innovative technology, there are a number of risks that are associated with open source technology. An increase in ransomware and other cyberattacks has given data scientists and organizations plenty to worry about when it comes to the security of their data and applications.

Some of the go-to security measures for organizations can not cover open source tools. For example, according to cybersecurity expert Ludovic Rembert from Privacy Canada, a VPN is one of the most effective tools for encrypting online communications.

“A Virtual Private Network (VPN) may sound complicated, but the idea is pretty simple,” says Rembert. “A VPN is a service that creates a virtual tunnel of encrypted data flowing between the user (that’s you) and the server (that’s the internet)...A VPN protocol determines how your data gets routed between your machine and the server. Different protocols have different costs and benefits depending on what you need.”

However, hackers will choose routes and applications that provide ample attack surface, but that also provide an easy way into your database and API. Since open source is the foundation of most of today’s application code, one could suspect that open source vulnerabilities are one of the most far-reaching risks to application securities. In fact, more than 80% of all cyberattacks target business applications.

Since open source vulnerabilities are usually disclosed to the public, much of the updating and bug fixing is left to organizations and data scientists with open source deployments. Following regulations such as maintaining CCPA compliance and PCI compliance are crucial for organizations to create a foundational layer of privacy and security for their customers and can be very costly for organizations that choose to rely on traditional security practices alone.

The NSA has recently released data that reveals that the average SAST tools can only find 14% of vulnerabilities that affect open source applications. Most vulnerabilities are reported by security researchers after their discovery through audits. So while open source does provide organizations with the ability to optimize their cybersecurity ecosystem, without skilled data scientists and professional developers on site, open source technologies can lead to increased vulnerability.

Wrapping up

Legacy systems with proprietary applications are rapidly being replaced by open source tools and business applications to enhance every day business activities. Data scientists must shift their focus and embrace open source tools to increase agility and efficiency. This will leave your teams more room for innovation towards future business growth.

Bio: Nahla Davies has worked professionally in NYC and the Bay Area for a handful of companies building and managing compliance teams. In 2020, Nahla took a less active role in the industry to pursue a career on copywriting and professional consulting for SMBs. Nahla holds an undergraduate degree in Computer Science and a Master's degree in Software Engineering.

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