This article explains how the Tianji advertising platform consolidated heterogeneous MySQL, ClickHouse, and TiDB data sources into a single StarRocks data warehouse, addressing data silos, real‑time performance, and query complexity while improving accuracy, latency, and development efficiency.
The article explains how Baidu's commercial advertising platform leverages generative AI agents—covering their core capabilities of understanding, planning, execution, and persona—to overcome challenges such as hallucination and integration, describing a multi‑layer architecture, key technologies, real‑world case studies, and the resulting performance and operational benefits.
This article presents a detailed case study of how EasyPoint built a scalable, stable advertising platform using ClickHouse Enterprise, covering company background, data architecture with Kafka and Druid, ClickHouse advantages, serverless resource scaling, and extensive performance benchmarks.
In the AI‑native era, Baidu's ad platform adopts a multi‑agent architecture that combines large and small LLMs, SOP‑driven workflows, long‑term memory, and vector databases to achieve high query accuracy, low latency, and significant business gains while tackling challenges such as hallucination, planning, execution, and personalization.
To support rapid overseas expansion, the article outlines a comprehensive backend architecture—including management, data ingestion, device tracking, attribution, and offline tasks—while detailing fine-grained user permission controls, automated product onboarding, batch ad creation, and server‑side attribution workflows, plus future enhancements.
NetEase’s advertising engine platform evolved from a monolithic, high‑concurrency system handling over a billion daily requests into a layered, distributed architecture that unifies indexing, billing, user‑tagging, and monitoring services, leverages Elasticsearch and custom extensions for fast retrieval, and plans further upgrades such as a custom retrieval kernel and Go‑based services.
Meituan Waimai’s advertising engine team built a platformized solution that standardizes business capabilities, data handling, and development workflow through reusable Action/Stage units, a unified context container, and a robust scheduling engine, achieving 28% faster development, over 65% Action reuse, and improved quality and stability.
The article details the architecture of 58.com’s SSP advertising platform, identifies three key reliability challenges—data consistency, interface regression, and storage synchronization—and presents a three‑layer testing quality system comprising web‑layer validation, service‑layer automated testing, and data‑layer monitoring with concrete tools and future improvement plans.
This article presents a comprehensive overview of the technical architecture behind a commercial advertising platform, covering business fundamentals, microservice migration, service governance, distributed storage, real‑time billing, and large‑scale reporting, and explains the practical engineering choices made at Alibaba.
In this interview, Xiaomi R&D architect Ouyang Chen shares the design philosophy, evolution stages, core modules, pitfalls, and future challenges of the Xiaomi advertising platform, offering practical insights for building scalable backend systems.
