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Compilation of Our Hard Work
Big-Data Architectural Renovation
To improve system scalability and efficiency
A leading fintech company offering a SaaS platform that delivers financial reference information for Fortune 100 institutions.
The client faced a tenfold increase in demand, which necessitated a new infrastructure design to enhance the platform’s reliability, scalability, and maintainability, while also reducing processing time and costs. This challenge aligned with the organization’s broader business objectives of delivering unparalleled service and staying ahead of the competition.
Our team assessed the existing infrastructure and data requirements to design and implement a new, scalable architectural framework. This innovative solution incorporated cutting-edge, easy-to-understand methodologies that enabled efficient handling of increased demand, laid the foundation for future growth, and opened doors for further innovation in the platform.
The revamped infrastructure empowers the SaaS platform to accommodate the tenfold surge in demand, providing a strong competitive advantage in the market. Key performance metrics include:
The solution not only saved the client time and money but also improved the customer experience, contributing to the company’s long-term growth and success. Additionally, the updated infrastructure paves the way for further innovation, creating new opportunities to enhance the platform and maintain a competitive edge in the industry.
Distributed Processing Infrastructure
An immuno-sequencing and genomics startup from a top academic institution, working towards advancing cutting-edge research and driving innovation in their field.
The client needed to process, analyze, and visualize terabytes of immuno-sequence data with speed, flexibility, and accuracy, while also enabling interactive data exploration to derive meaningful research insights. This challenge was directly linked to the organization’s strategic goals of accelerating research and generating valuable intellectual property.
In collaboration with the top academic institution, our team developed a highly efficient, scalable distributed computing framework that aligned with the organization’s objectives. We used open-source technologies like Apache Spark (PySpark) to create a user-friendly system capable of handling the processing, analytics, and visualization of large immuno-sequence datasets, while ensuring data security and compliance.
To facilitate seamless, interactive data exploration, we developed a Python Dash App visualization and user control dashboard, deployed using Heroku. The solution was designed to be highly versatile and easily integrable with various cloud services, enabling the client to adapt to their changing needs.
Our solution had a transformative impact on the client’s ability to process, analyze, and visualize large immuno-sequence datasets. The distributed computing framework and visualization tool we developed provided:
Overall, our solution supported the client’s strategic objectives, accelerated their research data exploration, and enhanced their platform, positioning the company for continued growth and innovation.
Big-data processing optimization
via Big Query
A thriving advertising platform startup, focused on enhancing customer experience and driving rapid business expansion by onboarding ten times more clients within the next year.
The client’s existing infrastructure faced slow processing speeds, high costs, fragility, and maintenance issues, which hindered their ambitious growth plans and ability to provide an optimal user experience. Overcoming these challenges was crucial to achieving their strategic objectives and unlocking new opportunities in the advertising industry.
Our team collaborated closely with the client to develop a streamlined solution that addressed their infrastructure pain points while aligning with their broader business goals. We simplified the technical aspects by migrating and optimizing their existing mySQL queries into GCP BigQuery queries, which were then wrapped in user-friendly Python scripts. The solution was containerized using Docker and scheduled with Airflow for efficient processing and orchestration, ensuring scalability and growth potential.
The new approach resulted in a robust, portable, and accessible service that empowered the client to process and orchestrate data more effectively, in line with their strategic goals. Key improvements included:
Overall, our solution supported the client’s strategic objectives, addressed their infrastructure challenges, and unlocked new opportunities for innovation. The updated infrastructure paves the way for future advancements, ensuring the client remains competitive in the rapidly evolving advertising industry.