Streaming Analytics

Bilibili Tech

Feb 18, 2025 · Artificial Intelligence

Algorithmic Empowerment of Bilibili Streaming: VOD Transcoding Decision, Resource Estimation, and Live Comment Semantic Analysis

The article details how Bilibili leverages AI algorithms—including XGBoost, statistical rules, XDeepFM, and fine‑tuned SBERT—to optimize VOD transcoding decisions, estimate compute resources and processing time, and analyze live comments, thereby boosting streaming efficiency, utilization, and user experience.

AIMachine LearningResource Estimation

0 likes · 19 min read

Algorithmic Empowerment of Bilibili Streaming: VOD Transcoding Decision, Resource Estimation, and Live Comment Semantic Analysis

DataFunTalk

Sep 20, 2023 · Big Data

Enhancing Flink CEP: Dynamic Multi‑Rule Support, SQL Extensions, and Performance Optimizations

This article presents Alibaba Cloud's open‑source big‑data team's enhancements to Flink CEP, covering its core concepts, typical use cases, dynamic multi‑rule loading via FLIP‑200, extended CEP SQL syntax, performance optimizations, and real‑world risk‑control scenarios.

Complex Event ProcessingDynamic Rule LoadingFlink CEP

0 likes · 21 min read

Enhancing Flink CEP: Dynamic Multi‑Rule Support, SQL Extensions, and Performance Optimizations

DataFunTalk

Sep 7, 2020 · Big Data

Real‑time Data Warehouse Architecture and Best Practices in Alibaba Search Recommendation

This article presents Alibaba's search‑recommendation real‑time data warehouse, describing its business background, typical use cases, key requirements, the evolution from architecture 1.0 to 2.0 with Flink and Hologres, best‑practice patterns such as row/column storage, stream‑batch integration, high‑concurrency updates, and future directions like real‑time joins and persistent dimension storage.

FlinkHologresOLAP

0 likes · 13 min read

Real‑time Data Warehouse Architecture and Best Practices in Alibaba Search Recommendation

DataFunTalk

Jun 14, 2020 · Big Data

Practical Experience and Optimization of Apache Druid for Real‑Time OLAP at iQIYI

This article describes how iQIYI evaluated various OLAP engines, selected Apache Druid for real‑time analytics, detailed its architecture, identified performance bottlene‑cks in Coordinator, Overlord and indexing, applied configuration and resource‑allocation optimizations, and built a user‑friendly RAP platform to democratize real‑time data analysis.

Apache DruidReal-time OLAPStreaming Analytics

0 likes · 15 min read

Practical Experience and Optimization of Apache Druid for Real‑Time OLAP at iQIYI