ClickHouse Jakarta Meetup - August 2026
Details
We've got something special for Indonesia's data community! 🚀
ClickHouse Indonesia community is back. Join us in Jakarta on August 5 for an evening of learning from database experts and great conversations with the ClickHouse community.
Come connect with fellow data enthusiasts, hear insights from speakers in the field, and dive into what's new in the world of ClickHouse.
🗓️ AGENDA:
- 6:30 PM: Registration, Dinner & Chitchat
- 7:00 PM: Welcome and Introductions
- 7:05 PM: Stream, Ingest, Monitor: Scaling OTT Analytics with ClickHouse by Rafif Abdus Salam, Senior Data Engineer @ Vidio
- 7:35 PM: Talk - TBD
- 8:00 PM: Talk - TBD
- 8:25 PM: Q&A
- 8:40 PM: Networking & Close
👉🏼 RSVP to secure your spot!
If anyone from the community is interested in sharing a talk at future events, complete this CFP form and we’ll be in touch.
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🎤 Session Details: Stream, Ingest, Monitor: Scaling OTT Analytics with ClickHouse
As OTT platforms grow, managing telemetry data streams from active viewers becomes a significant engineering challenge. Processing millions of events per minute—ranging from playback initializations and user interactions to performance indicators like buffering—requires a robust pipeline architecture to prevent infrastructure cost inflation or system performance degradation.
In this session, we will discuss how ClickHouse serves as a core component in our OTT analytics data pipeline architecture. The discussion will be broken down into three key pillars aligned with our title: how large-scale telemetry data is streamed, efficiently ingested in high volumes, and processed to monitor performance metrics in near real-time. We will share the end-to-end architecture used to handle millions of events per minute directly from edge devices.
Speaker: Rafif Abdus Salam, Senior Data Engineer @ Vidio
Rafif Abdus Salam is a Data Engineer at Vidio. He is passionate about building scalable data pipelines, driving data governance, and enabling AI from data to solve complex business challenges. His work centers on managing high-velocity streaming data and building efficient architectures for large-scale analytics.