Open Source: Innovating Data Science With Minimum Dependency

Code, Open Software, Open Source

Open Source: Innovating Data Science With Minimum Dependency

Organizations should adopt open source software to enhance agility and security.

Open-source data architecture is no more unfamiliar for organizations since it is being deployed in various data science projects. Data science has become an asset to industries considering its importance in advanced data analytics and data-driven business intelligence. The rapid digital transformation across the globe accelerated the adoption rate of disruptive technologies and automation.

Data is the digital currency today and WE Forum says that by 2025, it’s estimated that 463 exabytes of data will be created each day globally. With such huge amounts of data, businesses find it difficult to process and analyze them on a continuous basis. This is where open-source steps in as it can navigate and manage these data to provide insights.

Open-source technology is leveraged considering its feasibility and cost-efficiency in real-time data analysis and data storage. Data scientists can build data frameworks with open-source tools to streamline their workflows and make the most out of available datasets. Linux. Python, PHP are some open source software examples.

Being a publicly accessible and modifiable framework with open-source code, it is reckoned better than other proprietary softwares. Open-source software has great advantages in the field of data science.

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