In the constantly evolving field of the Internet of Things, BL.Predict is continually striving to push back the boundaries to offer its customers innovative and reliable solutions. At the heart of this ambition is the transition from BL.Predict’s V1 to V2 architecture, a crucial step in our commitment to provide state-of-the-art platforms adapted to current and future needs.
The transition from BL.Predict V1 to V2 marks a major milestone in our industrialization process. We have taken into account technological advances and best practices, to build a more robust, integrated and scalable architecture. This evolution reflects our commitment to providing solutions that meet the most stringent requirements in terms of security, confidentiality, availability and performance.
So what are the challenges encountered with the V1 architecture, and the improvements made in BL.Predict V2 to meet them? What are the new integrated practices and the benefits they offer users?
A necessary evolution
BL.Predict’s V1 architecture was a crucial milestone in our innovation journey, enabling the successful deployment of a functional instance for each customer. V1 was designed to create value and meet essential needs such as data collection via a message broker, data storage in the form of time series, data analysis from specific tasks, and the configuration of alerts on data flows.
However, the V1 architecture presented certain limitations in the face of rapidly changing customer needs and the technological environment. Individualized management for each customer was becoming increasingly complex, limiting our ability to offer a seamless, scalable experience. In addition, requirements for security, confidentiality and integration with other systems had become paramount, necessitating a complete review of the architecture to meet these challenges.
It was against this backdrop that BL.Predict’s V2 architecture was born, representing a necessary evolution to meet the demands of the technological environment. This new version has been entirely redesigned to enable shared management of multiple customers, while placing security, confidentiality and integration at the heart of its concerns. By integrating modern SaaS development methodologies such as DevSecOps and DataOps, V2 offers a more robust, automated and scalable solution, capable of adapting to the changing needs of our customers and providing ongoing added value to our solutions.
Towards a more robust, automated platform
BL.Predict’s V2 architecture represents a major transformation towards a more robust and integrated platform, aimed at delivering optimal user experience and increased reliability in IoT data management.
A key component of this evolution is the adoption of the multi-tenant model, enabling shared and secure client management within the same infrastructure. The multi-tenant model is a software architecture where a single instance of an application serves several clients (or tenants). Each client shares the same resources and code, but their data and configurations are isolated from each other. This enables resources to be shared and managed efficiently, reducing costs and complexity while delivering a consistent user experience.
At the same time, further integration with the Berger-Levrault information system, notably through BL.Security, ensures natural convergence with the company’s other systems. This ensures greater consistency and interoperability, facilitating business processes and maximizing the value of data for the entire organization. Data security and confidentiality are also central to this evolution.
By focusing on data protection and secure exchanges, we guarantee user confidence in our platform.
Finally, the adoption of modern methodologies such as DevSecOps and DataOps reinforces the platform’s performance and reliability. DevSecOps is a software development approach that integrates security (Sec) from the earliest stages of the software development lifecycle (Dev), in harmony with DevOps principles. The aim is to automate and streamline security processes throughout the development lifecycle, to ensure rapid, secure and reliable software deployments. DataOps is a data management methodology that applies principles of automation, collaboration and agility to the data lifecycle. This approach aims to accelerate and improve the quality of data processing, from collection to analysis, by fostering collaboration between development teams, operations and data. In this way, we ensure the sustainability and relevance of our solutions for our users, now and in the future.
For a smoother user experience and more efficient management of customer data
The transition to BL.Predict’s V2 architecture offers a range of significant benefits for our users. By focusing on improving security, confidentiality and integration with BL.Security, we have reinforced our customers’ confidence in our platform. The new architecture, designed to host and manage multiple customers on a shared basis, guarantees data isolation and enhanced protection, meeting the growing demands for data security and confidentiality.
The multi-tenant model enables users to benefit from optimum performance and constant service availability, while retaining their own isolated and secure data space. This model guarantees a smoother user experience and more efficient data management, reinforcing the added value of our solution for our customers.
By adopting modern development methodologies such as DevSecOps and DataOps, we have also improved the platform’s performance and reliability. Automated deployment of software components, combined with agile data management, ensures greater responsiveness to customer needs and adaptability to technological developments in the industry.
Conclusion
The evolution of BL.Predict’s architecture from version 1 to version 2 represents a crucial milestone in our ongoing commitment to deliver cutting-edge solutions that meet our customers’ needs. Over the years, our platform has evolved to offer an ever more fluid, reliable and secure user experience, reflecting our commitment to excellence and innovation.
