Ram Pedapatnam
Staff Software Engineer, Walmart
Dublin, California, United States
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Ram is a Staff Software Engineer at Walmart Global Tech and has 18 years of experience. He is specialized in building large-scale streaming applications, scalable event-driven microservices, data platforms, multi-tenant integrations. His favorite area is “data at scale,” whether it be insights, analytics, batch processing, streaming, or machine learning. He believes that “frameworks and languages are temporary, whereas concepts and problems are permanent.”
Area of Expertise
Topics
Cost Optimization Strategies for Real-Time Streaming Applications
In an era in which real-time data defines competitive advantage, developing scalable and resilient streaming applications has become both essential and increasingly complex. Effective cost management for maintaining such applications plays a pivotal long-term role in ensuring sustainability and performance.
This session explores the key components influencing cost in the development of streaming applications and outlines strategies for customization based on varying data volumes and system criticality. We compare the computation of streaming costs across leading cloud providers—including Azure, AWS, and GCP—as well as open-source streaming engines. Finally, we examine targeted approaches for cost optimization and illustrate these concepts through a detailed practical example.
Knowledge Level - Intermediate for Data Streaming Applications in specific and Data Engineering in general
Target Audience - Senior Software/Data Engineers, Leads, Architects, and Leadership who manage cost
Session Duration - Can customize for lightning talk (15 mins), regular talk (30-40 mins).
Where is my event? Patterns for building scalable event-driven applications
In an era where real-time data defines competitive advantage, building scalable and resilient streaming applications is more critical — and more challenging — than ever.
In this session, we will unpack the realities of modern streaming architectures, starting with "Hybrid Event-Driven Architectures" — blending asynchronous flows with synchronous coordination to replicate business logic reliably at scale.
We'll confront the often-overlooked risks of data loss and enterprise reconciliation, tracing the evolution of "lost ownership" in the world of microservices.
From there, we’ll dive into the pressure points of scalability, revealing how database performance varies under the intense demands of streaming workloads — and what you can do about it.
Finally, we'll explore strategies for building efficient multi-tenant systems and the critical code optimizations that can flatten the learning curve as engineering teams move from monolithic batch processing to distributed, real-time ecosystems.
Whether you’re designing your first streaming platform or scaling your tenth, this session will deliver practical insights, hard-earned lessons, and real-world patterns you can apply immediately.
My talk covers below topics which are relevant to the audience - in advance category
1. Streaming is Becoming the Norm, Not the Edge Case - All organizations want insights as early as possible.
2. Scaling is critical and default in Streaming Applications - Without precise techniques (like "surgical scaling"), most teams either overprovision (wasting money) or under-prepare
3. Learning curve is steep for developers, with many streaming frameworks, and there is a need to understand the concepts in a framework-agnostic manner
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