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CALSCALE:GREGORIAN
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UID:luma-47101417d2@palaner.app
DTSTAMP:20260802T155539Z
DTSTART:20260805T113000Z
DTEND:20260805T130000Z
SUMMARY:ClickHouse Jakarta Meetup - August 2026
LOCATION:GoWork Treasury Tower - Coworking and Office Space
DESCRIPTION:Saved in Palaner as: saved\n\nPalaner match: 25% · why: matches
  your usual pattern\n\nWe've got something special for Indonesia's data co
 mmunity! 🚀\n\nClickHouse Indonesia community is back. Join us in Jakarta o
 n August 5 for an evening of learning from database experts and great conv
 ersations with the ClickHouse community.\nCome connect with fellow data en
 thusiasts\, hear insights from speakers in the field\, and dive into what'
 s new in the world of ClickHouse.\n\n🗓️ AGENDA:\n6:30 PM: Registration\, D
 inner & Chitchat\n7:00 PM: Welcome and Introductions\n7:05 PM: Stream\, In
 gest\, Monitor: Scaling OTT Analytics with ClickHouse by Rafif Abdus Salam
 \, Senior Data Engineer @ Vidio\n7:35 PM: Why Real-Time Makes Batch Data M
 odeling Harder Than Expected: Lessons from a Personal Data Engineering Pro
 ject by Yunata Gunawan\, Data Engineer @ Global IT consulting firm (withhe
 ld for privacy)\n8:00 PM: Talk - Document Analytics at Columnar Speed: The
  Internals of ClickHouse's JSON Type by Andi Pangeran\, Head Of Engineerin
 g @ amartha.com\n8:25 PM: Q&A\n8:40 PM: Networking & Close\n👉🏼 RSVP to sec
 ure your spot!\n\nIf anyone from the community is interested in sharing a 
 talk at future events\, complete this CFP form and we’ll be in touch.\n🎤 S
 ession Details: Stream\, Ingest\, Monitor: Scaling OTT Analytics with Clic
 kHouse\nAs OTT platforms grow\, managing telemetry data streams from activ
 e viewers becomes a significant engineering challenge. Processing millions
  of events per minute—ranging from playback initializations and user inter
 actions to performance indicators like buffering—requires a robust pipelin
 e architecture to prevent infrastructure cost inflation or system performa
 nce degradation.\nIn this session\, we will discuss how ClickHouse serves 
 as a core component in our OTT analytics data pipeline architecture. The d
 iscussion will be broken down into three key pillars aligned with our titl
 e: how large-scale telemetry data is streamed\, efficiently ingested in hi
 gh volumes\, and processed to monitor performance metrics in near real-tim
 e. We will share the end-to-end architecture used to handle millions of ev
 ents per minute directly from edge devices.\nSpeaker: Rafif Abdus Salam\, 
 Senior Data Engineer @ Vidio\nRafif Abdus Salam is a Data Engineer at Vidi
 o. He is passionate about building scalable data pipelines\, driving data 
 governance\, and enabling AI from data to solve complex business challenge
 s. His work centers on managing high-velocity streaming data and building 
 efficient architectures for large-scale analytics.\n\n🎤 Session Details: W
 hy Real-Time Makes Batch Data Modeling Harder Than Expected: Lessons from 
 a Personal Data Engineering Project\nOrganizations are increasingly adopti
 ng real-time analytics to enable faster decision-making and more responsiv
 e applications. However\, many existing data platforms were built for batc
 h processing\, where data is collected\, transformed on a schedule\, and s
 tored as stable historical snapshots. Enabling real-time capabilities ther
 efore requires not only infrastructure changes but also a fundamental shif
 t in data modeling.\nTraditional batch models assume datasets are complete
 \, consistent\, and immutable after processing. In contrast\, real-time sy
 stems 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.
 \nThis transition introduces several modeling challenges\, including handl
 ing late-arriving events\, maintaining consistency between fact and dimens
 ion data over time\, and ensuring reproducible analytics despite continuou
 s updates. To address these issues\, data models must support event-time p
 rocessing\, incremental updates\, upserts\, versioned records\, and slowly
  changing dimensions instead of relying solely on static star schemas.\nMo
 dern lakehouse technologies such as Delta Lake\, Apache Iceberg\, Apache H
 udi\, and Apache Spark help unify batch and streaming workloads through fe
 atures including ACID transactions\, schema evolution\, and incremental pr
 ocessing. However\, these technologies do not remove the need for careful 
 data model design\, as correctness still depends on effectively managing s
 tate\, time\, and consistency.\nThis work presents the implementation of a
  real-time lakehouse architecture to support both batch and streaming work
 loads within a unified platform\, illustrating that adopting real-time ana
 lytics requires rethinking traditional data modeling approaches rather tha
 n simply introducing new technologies.\nSpeaker: Yunata Gunawan\, Data Eng
 ineer @ Global IT consulting firm (withheld for privacy)\nData Engineer wo
 rking in a consulting environment\, focused on building reliable data pipe
 lines and analytics infrastructure. Passionate about data architecture\, s
 ystem design\, and scalable backend systems\, with a goal of becoming a So
 lution Architect.\n\n🎤 Session Details: Document Analytics at Columnar Spe
 ed: The Internals of ClickHouse's JSON Type\nJSON has always been the awkw
 ard guest in analytical databases: flexible to write\, painful to query. T
 his talk goes under the hood of ClickHouse's production-ready JSON data ty
 pe to show how it stores every JSON path as a true columnar subcolumn. We'
 ll start with the foundations. The JSON type is built on two new general-p
 urpose types: Variant\, which efficiently stores values of different data 
 types within the same column without coercing them into a common type\, an
 d Dynamic\, which handles values whose types aren't known in advance. Then
  we'll make it concrete with real numbers. Using the JSONBench benchmark. 
 Finally\, we'll cover practical guidance: when the JSON type is the right 
 choice versus explicit columns or Tuple types. Plus when you add others in
 dexing capability like fulltext search\nSpeaker: Andi Pangeran\, Head Of E
 ngineering @ amartha.com
URL:https://luma.com/clickh-552k
CATEGORIES:palaner,saved
STATUS:TENTATIVE
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