Charlotte Java User Group

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Solving the Hard Problems in Microservices (Without Adding More Complexity)
Microservices architectures sound great ‐ until reality shows up. Databases become bottlenecks. Cache invalidation turns into a daily headache. Distributed coordination breaks under load. Latency stacks up with every service call. And somehow, you end up managing five new data systems just to keep things running.
In this session, we'll explore the real-world challenges that emerge once microservices hit scale ‐ the tough stuff most architecture diagrams conveniently ignore. We'll dig into proven strategies for managing distributed state, reducing latency, ensuring consistency, and processing data in real time ‐ all while keeping your system architecture manageable.
Along the way, we'll demonstrate how Java developers can apply these patterns using JVM-native tools like Hazelcast, helping teams simplify distributed systems without sacrificing performance.

What you'll take away:

• Patterns for solving common bottlenecks in microservices
• Approaches to coordination, state, and performance at scale
• Live demos of techniques in action using Java-first tooling
• Ideas for reducing complexity without adding yet another system

About our speaker
Samir Sharma is a Senior Solution Architect at Hazelcast for North America. He brings deep expertise in designing and delivering high-performance systems for financial services, with a career spanning leading investment banks, hedge funds, fintech firms, and research institutions.
Prior to Hazelcast, Samir held key technical roles at JP Morgan, Morgan Stanley, and Goldman Sachs, as well as at hedge funds including Tudor Investments and Graham Capital. In these roles, he led the architecture and development of systems for derivatives trading, pricing and risk analytics, and portfolio optimization.
At Citigroup and later at OnDeck, he led cross-functional teams of software engineers and data scientists to build cutting-edge credit risk analytics platforms, integrating machine learning and big data technologies such as Hadoop and Spark.
Earlier in his career, worked at IBM Research’s Mathematical Sciences Department, where he contributed to AI and machine learning efforts in the Business/Data Analytics ML Research group. He is a named inventor on two US patents in the fields of machine learning and optimization.

Agenda
5:30pm – 6:00pm: Networking, food and drinks
6:00pm – 7:00pm: Talk session
7:00pm – 7:30pm: More networking