The centralization of data has been a prevalent trend for many years. From large corporations to small businesses, data is collected, processed, and stored in central databases. However, with the rise of data privacy regulations across the world, there is a growing interest in decentralized data processing.
This blog is the second part of the blog series on Regulation-Compliant Federated Data Processing. In the previous blog, we looked at Federated Data Processing, data regulations through the GDPR lens, and the challenges these regulations bring when running federated data analytics. In this blog post, we will shed light upon how Databloom’s Blossom Sky data platform makes a leap forward in enabling decentralized data processing, which is critical to regulation-compliant federated analytics as discussed in the previous post.
What is Decentralized Data Processing?
Decentralized data processing is a technology that allows for data processing and analysis to occur without relying on a central authority. Instead, the data is stored on multiple nodes within a decentralized network. This means that there is no central authority in the data pipeline, where data needs to be stored and analyzed in order to derive insights.
Benefits of Decentralized Data Processing
Decentralized data processing has numerous advantages including
- Increased Security: With decentralized data processing, data is stored on multiple nodes within a network, making it more secure and resistant to cyber-attacks.
- Improved Data Privacy: Decentralized data processing allows for better data privacy as no central authority controls the data.
- Better Data Accessibility: Decentralized data processing enables better data accessibility as there is no single point of failure. This means that data is always accessible, even if one node fails.
- Lower Costs: Decentralized data processing reduces the costs associated with centralized data processing, such as hardware and maintenance costs.
- Increased Efficiency: Decentralized data processing is more efficient as multiple nodes can work together to process data in parallel.
Decentralized Data Processing with Blossom
Blossom Sky allows connecting to any data source without having to copy the data into a centralized data warehouse or data lake. Blossom Sky thus is a more suitable data platform for organizations’ data mesh, where it can break down silos of data across an organization and distribute data processing responsibilities to multiple systems and teams across locations. This approach allows for greater flexibility and scalability in data processing, as well as improved data governance and security through decentralization.
Our platform provides a holistic framework that provides appropriate safeguards: at one end, to data controllers that can easily specify what data and how data should be processed; and at the other end, to data scientists, data analysts, and data engineers that specify data analytics over decentralized data. Blossom’s optimizer ensures that the distribution of analytical operations across compute nodes adheres to organization-wide data regulations.
Data processing with Blossom’s Federated analytics engine is thus decentralized and distributed, allowing for processing to occur at compute nodes such that processing is compliant. Additionally, the processing is always closer to the data source, which reduces latency and increases processing efficiency. This approach also enables organizations to innovate and experiment with new analytical pipelines, as they are no longer limited by a centralized data processing infrastructure.
About Databloom
Databloom is a software company that has developed a powerful AI-Powered Data Platform Integration as a Service platform called Blossom Sky. This platform enables users to unlock the full potential of their data by connecting data sources, enabling generative AI, and gaining performance by running data processing and AI directly at independent data sources. Blossom Sky allows for data collaboration, increased efficiency, and new insights by breaking data silos in a unified manner through a single system view. The platform supports a wide range of ML and AI algorithms and is designed to adapt to a wide variety of AI algorithms and models.
