IN PERSON Apache Kafka® Meetup Hyderabad - Feb 2026

Hello everyone! Join us for our first IN PERSON Apache Kafka® meetup on Feb 21st from 10:30 am hosted by Microsoft!

IMPORTANT:
THE FORM IS NOW CLOSED
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Please fill out this form for security purposes to enter the venue:
https://forms.gle/aFjGLdy15Lc9DrhR6
As space is limited, spots will be allocated on a first-come, first-served basis based on form submissions, so please submit it as soon as possible. The form will be open until Feb 16.

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Agenda:

• 10:00 AM to 10:30 AM: Registration, Networking & Welcome Note
• 10:30 AM to 11:00 AM: Kafka as the Split Brain: Hot & Cold Data Architectures in Practice by Sasi Teja, OSS Community Catalyst, GlassFlow
• 11:00 AM to 11:30 AM: Krishna Teja, Confluent
• 11:30 AM to 11:50 AM: Break and Networking
• 11:50 AM to 12:20 PM: From Prompts to Productivity: How to Work with AI Agents Efficiently by Ramana Kumar from Silicon Labs
• 12:20 PM to 12:35 PM: Community Spotlight (Submit for Spotlight)
• 12:35 PM to 13:00 PM: Closing Note and Networking followed by Lunch

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** Speaker:**
Sasi Teja K, OSS Community Catalyst, GlassFlow

Talk:
Kafka as the Split Brain: Hot & Cold Data Architectures in Practice

Abstract:
Modern data platforms often struggle to serve two very different needs: *real-time decision making and long-term analytics* . In this talk, we’ll explore how Kafka becomes the natural split point for these two data paths. We’ll look at a *Hot vs Cold data architectures* , where the same Kafka stream feeds both Cold and Hot paths.

*Cold path* → Apache Iceberg via Kafka Connect Long-term storage, batch-friendly analytics, schema evolution, and the ability to fix data quality issues later.
*Hot path* → ClickHouse Low-latency queries, real-time dashboards, and immediate business value—where data must be clean, query-ready, and trustworthy from the moment it arrives.

We’ll discuss why data quality expectations differ between hot and cold paths, how tools like GlassFlow help clean and prepare data in real time, and how different use cases (dashboards, alerts, and even “bad data” detection) can coexist on the hot path.

The session will focus on *architectural decisions, real-world patterns, a demo showing a live Hot path* - you can apply all this when building streaming platforms with Kafka.

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** Speaker:**
Krishna Teja, Solutions Engineer, Confluent

Talk:
Mastering Kafka Performance: A Tuning Deep Dive for Producers, Consumers, and Brokers

Abstract:
In this session, we’ll cover:

• Identifying service goals such as throughput, latency, and durability before tuning
• How efficient producer batching helps optimize overall cluster performance
• Understanding consumer group behavior and the fetch process to reduce lag
• Why broker performance depends on healthy nodes and proper request handling
• How iterative benchmarking and monitoring help achieve your desired results

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** Speaker:**
Ramana Kumar AI/ML Engineer from Silicon Labs

Talk:
From Prompts to Productivity: How to Work with AI Agents Efficiently

Abstract:
AI agents are everywhere—but most developers struggle to get consistent, high-quality results from them. The bottleneck isn’t the model; it’s the context you give it. In this talk, we’ll break down practical context engineering, structured prompting, and battle-tested workflows that turn flaky agents into reliable collaborators. Learn how top ML engineers minimize ML complexity while maximizing real-world impact—and how you can apply the same principles to ship faster with fewer surprises.

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DISCLAIMER
BY ATTENDING THIS EVENT IN PERSON, you acknowledge that risk includes possible exposure to and illness from infectious diseases including COVID-19, and accept responsibility for this, if it occurs.
As the classroom is a mask-on setting, please be reminded that masks should still be worn at all times unless actively eating or drinking
NOTE: We are unable to cater for any attendees under the age of 18.
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If you would like to speak or host our next event please let us know! community@confluent.io