ClickHouse Jakarta Meetup - August 2026

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: Why Real-Time Makes Batch Data Modeling Harder Than Expected: Lessons from a Personal Data Engineering Project by Yunata Gunawan, Data Engineer @ Global IT consulting firm (withheld for privacy) 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. _____________________________________ 🎤 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. 🎤 Session Details: Why Real-Time Makes Batch Data Modeling Harder Than Expected: Lessons from a Personal Data Engineering Project Organizations are increasingly adopting real-time analytics to enable faster decision-making and more responsive applications. However, many existing data platforms were built for batch processing, where data is collected, transformed on a schedule, and stored as stable historical snapshots. Enabling real-time capabilities therefore requires not only infrastructure changes but also a fundamental shift in data modeling. Traditional batch models assume datasets are complete, consistent, and immutable after processing. In contrast, real-time systems continuously process streaming events that may arrive late, out of order, or be corrected after ingestion. As a result, analytical outputs must be updated continuously rather than generated as fixed batch results. This transition introduces several modeling challenges, including handling late-arriving events, maintaining consistency between fact and dimension data over time, and ensuring reproducible analytics despite continuous updates. To address these issues, data models must support event-time processing, incremental updates, upserts, versioned records, and slowly changing dimensions instead of relying solely on static star schemas. Modern lakehouse technologies such as Delta Lake, Apache Iceberg, Apache Hudi, and Apache Spark help unify batch and streaming workloads through features including ACID transactions, schema evolution, and incremental processing. However, these technologies do not remove the need for careful data model design, as correctness