2 weeks Ago By Miller Victor
Share Tweet Share Share Email As the volume and velocity of data grow significantly, organizations face increasing pressure to develop scalable, efficient, and reliable data systems that align with business objectives. Navigating these complexities requires not only technical expertise but also strategic foresight—qualities that define Rudra Sinha’s two-decade career. As a Senior Manager in Data Engineering at Capital One, Rudra has consistently delivered innovative solutions, helping companies overcome some of the most pressing challenges in the data engineering landscape.
Rudra’s career spans influential roles at industry giants like Capital One, Neudesic, and JP Morgan Chase. At Capital One, he leads teams building advanced data platforms such as Delta Lake and Iceberg, ensuring data systems meet both business needs and technical demands. In his previous role as Principal Cloud Solution Architect at Neudesic, Rudra spearheaded the migration of legacy monolithic applications to the cloud, transforming operations and enabling seamless scalability.
His career reflects a rare blend of strategic vision and hands-on expertise, driving meaningful progress in each role he undertakes. This article explores Rudra’s notable achievements through detailed case studies, offering insights into his innovative problem-solving methods. From reducing data latency and optimizing resource allocation to balancing cost management with cutting-edge advancements, these narratives highlight how his leadership and technical acumen continue to shape the future of data engineering.
Cracking the latency puzzle Crafting a low-latency data publishing system was a complex challenge requiring inventive problem-solving to reduce data lake write time from 25 to 30 minutes to under 2 minutes. The project necessitated an overhaul of the existing architecture, balancing cost-efficiency without increasing expenses. As Rudra noted, “The people who wrote the original code have left the team or company a while ago,” adding another layer of complexity.
Rudra tackled these challenges by analyzing the architecture to identify areas for improvement, leveraging AI-based tools like Git Copilot to recode critical components. Following extensive testing with high-velocity datasets and resolving issues like schema locking, the new system was successfully deployed. “The promised SLA of under two minutes was achieved,” Rudra reports, marking a significant milestone in data publishing.
Cost-saving innovations Facing high data processing costs, Rudra addressed the issue by restructuring the infrastructure supporting his Delta Lake platform. “The main driver behind looking for a solution was cost,” he states, as the inefficiency of maintaining static clusters demanded a resolution. To achieve this, Rudra implemented on-demand dynamic clusters, which were “40% cheaper” and spun down as soon as jobs were completed, dramatically cutting compute costs.
In addition to adopting dynamic clusters, Rudra developed a new algorithm that allocated clusters based on data size, with thresholds defined for small, medium, large, and extra-large workloads. The algorithm also considered the nature of jobs—distinguishing between more resource-intensive upserts and lighter data appends—ensuring optimal cluster sizing. Rudra shares, “An additional branch of logic was added to use a dedicated dynamic cluster for high-velocity datasets,” guaranteeing the system met its performance targets without compromising cost efficiency.
This new design yielded a transformative 90% reduction in processing costs, making the Delta Lake platform not only sustainable but also strategically viable for Capital One. “With the reduced cost, a contemporary technology-based Delta Lake solution made it possible for us to move towards Delta Lake,” Rudra notes. Moving to Delta Lake enabled enhanced storage flexibility and seamless integration with other modern technologies like Iceberg, providing greater out-of-the-box solutions for data consumption.
Kubernetes to the rescue While working at Neudesic, Rudra played a crucial role in modernizing legacy applications by introducing Kubernetes as the core infrastructure solution for cloud migration. Faced with the challenge of moving around 20 applications, built on diverse tech stacks like Python, Java, and Scala, Rudra identified Kubernetes as the ideal platform due to its built-in capabilities such as scalability, load balancing, self-healing, productivity, and resource efficiency. These features allowed the team to shift their focus toward optimizing the applications rather than worrying about infrastructure orchestration.
The migration involved breaking down monolithic applications into smaller, manageable services, which were then deployed on Kubernetes. This approach enabled the infrastructure to adjust dynamically based on the load, ensuring optimal efficiency. Rudra notes that Kubernetes’ availability across major cloud platforms “as a service” significantly eased cluster management and maintenance.
Furthermore, the seamless integration between tools like Terraform and GitHub ensured smooth deployment, with Rudra emphasizing, “Code deployment is also easily integrated between GitHub and cloud-based Kubernetes services,” making the entire migration process both streamlined and efficient. Built to scale and last Ensuring that technical solutions are scalable and adaptable requires a thoughtful combination of design principles and strategic planning. Rudra prioritizes modularity, explaining “By breaking the system into independent, loosely coupled modules or services, each component can be updated or scaled without impacting the entire system.
” This modular approach not only supports scalability but also simplifies future maintenance and upgrades. Rudra emphasizes the role of cloud-native infrastructure, noting that platforms like AWS and Azure offer scalable services such as auto-scaling, load balancing, and elastic storage to handle fluctuating demand efficiently. He also highlights the importance of continuous monitoring and CI/CD pipelines, explaining that automated testing and deployment ensure rapid feature releases without downtime.
By designing with scalability, future-proofing, and monitoring in mind, Rudra ensures his solutions remain adaptable and robust in the face of evolving business needs. Leadership in motion Leading a team through the complexities of Kubernetes adoption required both technical expertise and effective leadership. Rudra recalls working with a group of six developers who had no prior experience with Kubernetes.
To foster client confidence, he ensured that he gathered and documented the requirements thoroughly and translated them into technical workable deliverable units. This approach reassured the client about the team’s capability to deliver on the project. In parallel, Rudra collaborated with the client to convert the first application into Kubernetes-friendly modules, successfully deploying it into a cloud-based Kubernetes cluster.
“This boosted my client’s confidence and also educated my team on the work at hand,” he explained. With this foundation in place, the team was able to independently drive forward the modernization and migration of 20 applications, demonstrating both efficiency and proficiency under his leadership. Cost management for success Balancing innovation with cost management requires a strategic approach that ensures the value delivered outweighs the expenses.
Rudra highlights, “Innovation always comes at a cost,” underscoring the importance of aligning new solutions with long-term benefits. In a key project, Rudra implemented a centralized infrastructure for real-time data availability, which initially introduced higher costs. However, he explains that this new system replaced multiple individual solutions, valued in millions of running costs, that were previously necessary for each real-time data requirement.
The investment in the new infrastructure also provided future savings by eliminating the need for additional solutions for similar use cases. “Any future real-time data requirement use cases didn’t need to create any further solutions,” Rudra notes, emphasizing how the upfront cost ultimately enabled sustainable efficiencies. This thoughtful approach not only delivered innovative capabilities but also ensured long-term financial viability.
Overcoming cloud migration challenges Cloud migration at Neudesic presented several challenges, including undefined project goals, unclear team roles, and resistance from client-side development teams. Rudra reflects on the importance of setting clear expectations early on, explaining, “I called for meetings between the client’s management and also my leadership to define the intent of the project clearly.” This collaborative approach was well-received and established a clear path toward success.
Rudra also addressed the concerns of the client’s teams, ensuring they understood that his team was there to modernize and support existing applications, not take control. “They will remain in charge of the applications. We are not taking their jobs away!” he emphasized.
His strategy of showcasing an initial modernization effort helped both teams understand the process and eased concerns, fostering a smooth and collaborative migration effort. This approach ultimately transformed resistance into cooperation, allowing the project to progress efficiently. Rudra’s substantial contributions to data systems demonstrate his capacity for innovative solutions that balance performance and cost.
His trajectory, marked by roles at Capital One and Neudesic, is a testament to his dedication to optimizing system functionality and sustainability. His leadership at Neudesic, particularly during pivotal cloud migrations, exemplifies how strategic clarity and stakeholder education can overcome significant challenges. His expertise in aligning technological innovation with business objectives continues to shape resilient, adaptable data systems ready for future progress.
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