Apache Doris × Apache Kafka × OLake Meetup

Hello, streaming community!
We're thrilled to invite you to a morning of deep-dives, architectural insights, and great conversation at our upcoming Bangalore Meetup! In the true spirit of the open-source community, this event is all about sharing real-world lessons and exploring the tools that make our data lives easier and more powerful. We've brought together a fantastic lineup of speakers from VeloDB, Confluent, and OLake. Whether you're a long-time open source contributor or just beginning your journey into the real-time ecosystem, come join us to learn and network!

Location
Hustlehub Tech Park
PWD Quarters, 1st Sector, HSR Layout, Bengaluru, Karnataka 560102, India
Google Map

Agenda

• 11:00 - 11:10 Welcome!
• 11:10 - 11:50 Apache Doris 4.0: Evolving from Real-Time Analytics to AI-Native Data Intelligence by SiShuo Yang, Senior Solutions Architect at VeloDB
• 11:50 - 12:30 Fast & Lightweight data ingestion from Kafka to Apache Iceberg by Shubham Baldava, CTO at OLake
• 12:30 - 12:40 Break
• 12:40 - 13:20 Shift Left to get It Right by Kumar Keshav, Engineering Manager at Confluent
• 13:20 - 14:20 Lunch

Session Briefs

Session 1:
Apache Doris 4.0: Evolving from Real-Time Analytics to AI-Native Data Intelligence
Apache Doris has established itself as a leading foundation for real-time analytics, and one of the key elements to its success is its native integration with modern data streaming platforms like Apache Kafka. By leveraging features like Routine Load and exactly-once semantics, the combination of Doris and Kafka simplifies data architectures by eliminating complex ETL layers while maintaining high-throughput, sub-second query latency on fresh data streams.

The next phase of the Doris evolution addresses the growing demand for AI-integrated data systems. This session explores the architectural advancements in Doris 4.0 that enable a transition from standard OLAP toward AI-native data intelligence. By examining ByteDance's large-scale implementation journey, we will discuss how these new capabilities perform within demanding, real-world production environments.

Speaker Bio:
SiShuo Yang, Senior Solutions Architect at VeloDB
As a seasoned architect with a decade of full-stack development experience, SiShuo specializes in real-time analytics. As an AWS Professional and Certified Kafka Administrator, his career includes roles at VeloDB, Confluent and Alipay. He currently leads APAC technical success for VeloDB (Apache Doris), bridging high-performance data streaming and AI-native intelligence.

Session 2:
Fast & Lightweight data ingestion from Kafka to Apache Iceberg
Shubham Baldava, CTO at OLake
How we at OLake, have designed Kafka as a source, keeping concurrency at the center while keeping it lightweight. OLake - an open-source data ingestion & table-maintenance tool.

Session 3:
Shift Left to get It Right
Kumar Keshav, Engineering Manager at Confluent

The modern data landscape is often hindered by a persistent divide between analytical and operational systems, resulting in brittle ETL pipelines that break during source changes and forcing data scientists to spend the majority of their time cleaning low-quality data in landing zones. To address these challenges, the "Shift Left" approach moves data governance and validation as close to the source as possible, transforming raw inputs into trustworthy, reusable data products that serve both real-time and batch requirements simultaneously. Apache Kafka plays a pivotal role in this strategy by leveraging Kafka Connect and Change Data Capture to bootstrap database rows into real-time state events, while the Schema Registry enforces strict data contracts at the producer level to reject malformed data before it ever reaches downstream systems. By embracing the concept of stream-table duality—providing data as both Kafka topics and Iceberg tables—organizations can finally eliminate data silos and redundant duplication, ensuring that high-quality, governed data is consistently available for both operational services and deep analytical insights.