Devora Roth Goldshmidt
Senior Software Architect, NICE Actimize
Ramat HaSharon, Israel
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Devora is a software architect with 15 years of experience in the high-tech industry, specializing in cloud-native SaaS platforms and financial crime compliance solutions.
Currently, as Senior Software Architect at NICE Actimize, she leads the architecture of the X-Sight platform, a multi-tenant, microservices-based system on AWS, built for scale, security, and cost-efficiency.
With a strong background as a Java and Scala developer and tech lead, she has led teams delivering advanced compliance solutions to major global financial institutions.
Area of Expertise
Topics
Micro Service - Mega Cost: Architectural Styles and Their Hidden Price Tags
As developers, we love debating monoliths vs. microservices, event-driven vs. REST, serverless vs. Kubernetes. But beyond scalability, isolation, productivity, and other engineering trade-offs, there is a less glamorous, yet critical, dimension to these choices: cost.
In this talk, I’ll share real-world lessons from my experience building cloud-native systems and uncover the hidden price tags behind popular architectural styles. We'll break down the surprising costs of microservices, the savings (or not?) of monoliths, the trade-offs of serverless, and how an event-driven system can affect the budget.
This talk will give you a practical lens for evaluating architecture not just by performance or maintainability, but by the unexpected financial impact that comes with scale.
Debugging Data Skew in Flink - and Teaching the Pipeline to Investigate Itself
Running large-scale streaming systems in production often reveals behaviors that never appear in small tests or synthetic benchmarks. In this talk we share lessons learned while operating high-throughput Apache Flink pipelines at scale, where subtle distribution mechanics created unexpected performance bottlenecks. Through real production incidents we will explore how data skew can emerge from different layers of the streaming stack: operator semantics, runtime key partitioning, and upstream source distribution.
These experiences also raise a broader operational question: how do you continuously detect and investigate such issues in complex streaming systems?
In the second part of the talk, we explore a forward-looking approach to operating Flink at scale: embedding agents directly into the streaming environment to continuously analyze metrics, detect anomalies, and trigger automated investigations using LLMs and tool-based workflows. This enables the streaming platform itself to act as an intelligent observability layer, helping operators diagnose performance issues faster and reason about system behavior as workloads evolve.
Together, these perspectives combine practical production lessons with a glimpse into the future of AI-assisted operations for large-scale streaming systems.
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