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DTSTAMP:20260802T151305Z
DTSTART:20260805T113000Z
DTEND:20260805T130000Z
SUMMARY:ClickHouse Jakarta Meetup - August 2026
LOCATION:GoWork Treasury Tower
DESCRIPTION:Saved in Palaner as: saved\n\nPalaner match: 45% · why: lots of
  people going\n\nWe've got something special for Indonesia's data communit
 y! 🚀\n\nClickHouse Indonesia community is back. Join us in Jakarta on Augu
 st 5 for an evening of learning from database experts and great conversati
 ons with the ClickHouse community.\nCome connect with fellow data enthusia
 sts\, hear insights from speakers in the field\, and dive into what's new 
 in the world of ClickHouse.\n\n🗓️ AGENDA:\n\n6:30 PM: Registration\, Dinne
 r & Chitchat\n7:00 PM: Welcome and Introductions\n7:05 PM: Stream\, Ingest
 \, Monitor: Scaling OTT Analytics with ClickHouse by Rafif Abdus Salam\, S
 enior Data Engineer @ Vidio\n7:35 PM: Why Real-Time Makes Batch Data Model
 ing Harder Than Expected: Lessons from a Personal Data Engineering Project
  by Yunata Gunawan\, Data Engineer @ Global IT consulting firm (withheld f
 or privacy)\n8:00 PM: Talk - Document Analytics at Columnar Speed: The Int
 ernals of ClickHouse's JSON Type by Andi Pangeran\, Head Of Engineering @ 
 amartha.com\n8:25 PM: Q&A\n8:40 PM: Networking & Close\n\n👉🏼 RSVP to secur
 e your spot!\n\nIf anyone from the community is interested in sharing a ta
 lk at future events\, complete this CFP form and we’ll be in touch.\n\n\n🎤
  Session Details: Stream\, Ingest\, Monitor: Scaling OTT Analytics with Cl
 ickHouse\nAs OTT platforms grow\, managing telemetry data streams from act
 ive viewers becomes a significant engineering challenge. Processing millio
 ns of events per minute—ranging from playback initializations and user int
 eractions to performance indicators like buffering—requires a robust pipel
 ine architecture to prevent infrastructure cost inflation or system perfor
 mance degradation.\nIn this session\, we will discuss how ClickHouse serve
 s as a core component in our OTT analytics data pipeline architecture. The
  discussion will be broken down into three key pillars aligned with our ti
 tle: how large-scale telemetry data is streamed\, efficiently ingested in 
 high volumes\, and processed to monitor performance metrics in near real-t
 ime. We will share the end-to-end architecture used to handle millions of 
 events per minute directly from edge devices.\nSpeaker: Rafif Abdus Salam\
 , Senior Data Engineer @ Vidio\nRafif Abdus Salam is a Data Engineer at Vi
 dio. He is passionate about building scalable data pipelines\, driving dat
 a governance\, and enabling AI from data to solve complex business challen
 ges. His work centers on managing high-velocity streaming data and buildin
 g efficient architectures for large-scale analytics.\n\n🎤 Session Details:
  Why Real-Time Makes Batch Data Modeling Harder Than Expected: Lessons fro
 m a Personal Data Engineering Project\nOrganizations are increasingly adop
 ting real-time analytics to enable faster decision-making and more respons
 ive applications. However\, many existing data platforms were built for ba
 tch processing\, where data is collected\, transformed on a schedule\, and
  stored as stable historical snapshots. Enabling real-time capabilities th
 erefore requires not only infrastructure changes but also a fundamental sh
 ift in data modeling.\nTraditional batch models assume datasets are comple
 te\, consistent\, and immutable after processing. In contrast\, real-time 
 systems continuously process streaming events that may arrive late\, out o
 f order\, or be corrected after ingestion. As a result\, analytical output
 s must be updated continuously rather than generated as fixed batch result
 s.\nThis transition introduces several modeling challenges\, including han
 dling late-arriving events\, maintaining consistency between fact and dime
 nsion data over time\, and ensuring reproducible analytics despite continu
 ous updates. To address these issues\, data models must support event-time
  processing\, incremental updates\, upserts\, versioned records\, and slow
 ly changing dimensions instead of relying solely on static star schemas.\n
 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 carefu
 l data model design\, as correctness still depends on effectively managing
  state\, time\, and consistency.\nThis work presents the implementation of
  a real-time lakehouse architecture to support both batch and streaming wo
 rkloads within a unified platform\, illustrating that adopting real-time a
 nalytics requires rethinking traditional data modeling approaches rather t
 han simply introducing new technologies.\nSpeaker: Yunata Gunawan\, Data E
 ngineer @ Global IT consulting firm (withheld for privacy)\nData Engineer 
 working in a consulting environment\, focused on building reliable data pi
 pelines and analytics infrastructure. Passionate about data architecture\,
  system design\, and scalable backend systems\, with a goal of becoming a 
 Solution Architect.\n\n🎤 Session Details: Document Analytics at Columnar S
 peed: The Internals of ClickHouse's JSON Type\nJSON has always been the aw
 kward guest in analytical databases: flexible to write\, painful to query.
  This talk goes under the hood of ClickHouse's production-ready JSON data 
 type to show how it stores every JSON path as a true columnar subcolumn. W
 e'll start with the foundations. The JSON type is built on two new general
 -purpose types: Variant\, which efficiently stores values of different dat
 a types within the same column without coercing them into a common type\, 
 and Dynamic\, which handles values whose types aren't known in advance. Th
 en we'll make it concrete with real numbers. Using the JSONBench benchmark
 . Finally\, we'll cover practical guidance: when the JSON type is the righ
 t choice versus explicit columns or Tuple types. Plus when you add others 
 indexing capability like fulltext search\nSpeaker: Andi Pangeran\, Head Of
  Engineering @ amartha.com
URL:https://www.meetup.com/clickhouse-indonesia-user-group/events/314943794
 /
CATEGORIES:palaner,saved
STATUS:TENTATIVE
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