AliExpress built a Global Experience Platform that uses large‑model AI to ingest, clean, classify and diagnose multilingual user‑voice feedback at scale, achieving 99% classification accuracy, 90% issue‑resolution rate and significant reductions in alert load, database usage and CPU variance.
With the rapid evolution from Generative AI to Agentic AI, enterprises face both opportunities and challenges in building AI‑Native applications; this article examines the shift in AI development models, outlines key technical hurdles, and presents architectural and tooling strategies—including Chat BI, LangStudio, and multi‑modal data pipelines—to guide successful AI integration.
We Analysis, the official data‑analysis platform for WeChat mini‑program providers, delivers a zero‑learning‑curve user‑profiling system that combines basic tag analysis and flexible, rule‑based segmentation, using an ETL pipeline to store pre‑computed data in TDSQL and online bitmap‑optimized queries in ClickHouse with RoaringBitmap, ensuring low‑latency, stable, and comprehensive analytics.
This article details the architecture, data model, configurable workflow, and implementation techniques of JD Tech's Transaction Fulfillment Order Center, illustrating how a modular, high‑throughput backend system supports billions of annual orders across multiple business scenarios through configurable templates, expression engines, and MQ‑driven data flows.
This tutorial walks through building a real‑time data processing pipeline using ElasticSearch, Kibana and Logstash, covering core concepts such as data volume, velocity, variety and accuracy, detailed installation steps, CRUD operations, bulk data import, Java‑based data conversion, and Logstash pipeline configuration with filters and date parsing.
This guide explains how to collect, store, clean, and aggregate daily user metrics—including new users, active users, and multi‑day retention—using shell scripts, cron jobs, MySQL partitioned tables, and stored procedures, suitable for small‑to‑medium sized products.
This guide walks through preparing the environment, creating a MySQL source table, configuring Flink CDC to ingest data into an Apache Hudi table, and then querying the Hudi data using both Hive and Spark‑SQL, including handling of partitions, realtime input formats, and required configuration settings.
