How Kuaishou’s Data Platform Powers Intelligent BI: Architecture, Challenges, and Solutions
This article outlines Kuaishou Data Platform's mission to boost data decision efficiency, describes its three‑layer architecture, explains the BI process from data ingestion to application, and shares practical experiences and future outlook for intelligent BI powered by AI and big data.
Introduction
Kuaishou Data Platform Department aims to improve data decision efficiency by building advanced compute engines and high‑performance data services, providing comprehensive data analysis support for business. As a top‑ranked domestic data platform, Kuaishou’s BI toolchain covers everything from basic data ingestion to advanced data applications, offering self‑service analytics.
Main Content
Background introduction
Challenges and solution ideas
Solution
Application practice
Future outlook
Q&A
Kuaishou Data Platform Overview
The department’s responsibilities include using advanced compute engines, high‑quality data warehouses, high‑performance data services, and a series of data solutions to boost decision efficiency for analysis and experimentation, helping business growth. Kuaishou’s big‑data scale ranks among the top in China.
Kuaishou Big Data Analysis Mission
To build an industry‑leading one‑stop data analysis toolchain that provides comprehensive scenario solutions and improves decision efficiency.
Architecture Overview
The platform consists of three layers:
Business layer : DA products for personal users, platform products for operations, and online/B‑end services for main‑site business.
Product layer : General analysis product (KwaiBI) and specialized analysis products for domains such as the main site and e‑commerce.
Service layer : Supports two platforms – Gaia standard metric middle‑platform and API platform – and offers three services: data set query, KV point‑lookup API, and SQL table query.
What Is BI?
Business Intelligence (BI) platforms transform complex data into useful information to aid business decision‑making and planning.
Typical BI Process
Data ingestion: Connectors import data from multiple sources into the BI platform following a unified table definition.
Relationship modeling: Define user‑friendly metrics and dimensions on the ingested tables.
Data application: Perform analytical calculations based on data set query services (e.g., compute GDP distribution per city).
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