Classplus, an Ed-Tech startup, is on a mission to reshape education by equipping educators with cutting-edge digital tools and a unified platform. This platform empowers coaching institutes to create custom apps, streamlining tasks and connecting them with students nationwide. Classplus not only offers a comprehensive digital solution for task management and student engagement but also fosters institute growth. Within this initiative, Classplus has integrated a feature that involves tracking influential individuals through their social profiles using linked data technology. This feature compiles historical data and identifies trends, storing user profile data in JSON-LD format. This comprehensive approach underscores Classplus's commitment to advancing education through innovation and technology.
The client's objectives included a major system upgrade to handle a significant TPM increase, standardizing their technology stack, strengthening security measures, optimizing social media data retrieval, and implementing advanced machine learning for trend analysis. These objectives posed several hurdles, including scalability issues, tech stack diversity, security complexities, data fetching challenges, and the intricacies of deploying machine learning solutions.
The challenge was centered around upgrading the monolithic system to accommodate a remarkable surge in transactions per minute (TPM), necessitating a transition from 5,000 to 500,000 TPM. This demanding task involved extensive optimization, fine-tuning the database, and adopting a microservices architecture to ensure robust scalability.
Managing a wide array of technologies and programming languages presented a multifaceted challenge. Ensuring robust security practices, including safeguarding user information, data protection, and meticulous access control, was paramount. The intricate integration and security enhancement effort demanded meticulous attention and expertise, requiring a delicate balance to maintain system integrity while ensuring a smooth user experience.
The challenge involved efficiently fetching data from multiple social media platforms several times daily, particularly for social profiles. Managing large volumes of data necessitated sophisticated service worker management to enable the daily retrieval of substantial datasets.
Implementing machine learning to analyze trend data and detect anomalies was a complex endeavor. The goal was to identify noteworthy patterns and irregularities, subsequently triggering the creation of specialized news content based on both trends and anomalies.
As part of our solutions, we facilitated the system's transition from handling 5,000 TPM to a remarkable 500,000 TPM, achieved through comprehensive optimization, database fine-tuning, and a phased migration to a microservices architecture. We streamlined the diverse tech stack, consolidating it around a NodeJS and Golang-based backend, complemented by a ReactJs-based frontend. Additionally, we bolstered security by implementing stringent measures, including OpenDJ and OpenAM.
To address the scalability challenge, the solution involved extensive optimization efforts to enhance system performance. Database tuning was performed to improve data retrieval efficiency. Additionally, transitioning from a monolithic architecture to a microservices architecture allowed for better scalability, enabling the system to handle the increased TPM load effectively.
In response to the complex challenge of managing diverse technologies, we initiated a strategic standardization effort. This involved consolidating the backend using Node.js and Golang while implementing React.js for the front end. This standardization streamlined development processes, improved system compatibility, and reduced maintenance complexity. Concurrently, we fortified the system's security to ensure data protection and secure access, meticulously implementing robust security practices, including the introduction of stringent security protocols like OpenDJ and OpenAM. This comprehensive approach seamlessly balanced technology standardization with heightened security measures, ultimately guaranteeing a seamless and secure user experience.
Robust security practices were implemented to safeguard data and ensure secure access. The introduction of security protocols such as OpenDJ and OpenAM enhanced data protection and access control, strengthening the overall security posture of the system.
To efficiently fetch data from multiple social media platforms multiple times a day, the solution included the implementation of specialized queues for data retrieval. This optimized service workers' management, enabling the system to handle the substantial data fetch requirements on a daily basis.
Addressing the complexity of machine learning for trend and anomaly detection, the solution involved the development and deployment of advanced machine learning algorithms. These algorithms analyzed trend data to identify significant patterns and detected anomalies, triggering the automated creation of specialized news content based on both trends and anomalies.
