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Stream, Store, Visualize: Fast-track Real-Time Analytics the OSS Way
Thu, 24 April
Many developers struggle with the complexity of setting up streaming analytics—wrestling with configurations, compatibility issues, and integration challenges. However, choosing the right open-source tools can drastically reduce setup time, enabling real-time analytics pipelines in minutes, not weeks.
This session features a live demonstration of building a complete streaming pipeline on a laptop, using Apache Iceberg for storage and Streamlit for real-time visualization. Attendees will gain hands-on experience in integrating data sources, configuring storage, and creating dashboards, all within a local development setup that mirrors production architecture.
Key Takeaways
- Configure a local streaming development environment.
- Integrate various data sources and sinks efficiently.
- Implement Apache Iceberg for scalable data storage.
- Build real-time dashboards using Streamlit.
- Understand production deployment patterns for real-time analytics.
Target Audience
This session is ideal for data engineers, software developers, and AI practitioners who want to build real-time analytics pipelines using open-source technologies. It is also valuable for architects and DevOps engineers interested in optimizing data infrastructure for low-latency, high-throughput analytics.
Prerequisites: Basic knowledge of SQL and Python.
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About the speaker
Kamesh Sampath
Lead Developer Advocate, Snowflake
Kamesh is a veteran tech innovator, author, and Lead Developer Advocate at Snowflake, India, with over 20 years in the trenches and more than a decade of open source contributions. As an author and developer advocate, he’s on a mission to demystify data engineering, cloud architecture, and emerging AI technologies. His expertise spans data cloud platforms, serverless computing, and distributed systems.
Passionate about empowering developers to harness the power of cutting-edge tools and frameworks, Kamesh has been actively contributing to the open source landscape for more than 10 years. With a track record of crafting robust enterprise solutions across diverse industries and participating in community-driven projects, he brings battle-tested insights on everything from integrating AI into enterprise data ecosystems to building scalable cloud-native architectures.
Kamesh is always eager to collaborate, innovate, and push the boundaries of what’s possible in tech, bringing the power of AI to enterprise data challenges.
