Building a Real-Time Data Warehouse with Flink: Architecture, Core Concepts, and Practical Implementation
This article explains how to build a unified stream‑batch real‑time data warehouse using FlinkSQL, covering prerequisite knowledge, five core concepts, two implementation approaches, a comparison of traditional versus real‑time architectures, and a comprehensive hands‑on example, illustrated with diagrams.
Building a unified stream‑batch real‑time data warehouse based on Flink is a popular practice in the data‑warehouse field. As Flink evolves, its features make constructing such applications increasingly convenient. This article shares the basic architecture and technical points of building a real‑time data warehouse with FlinkSQL.
Two prerequisite knowledge areas
Five basic concepts
Two concrete implementation methods
Comparison of two architectures
A comprehensive hands‑on exercise
Stream Processing vs. Batch Processing
Five Basic Concepts
Dimension Table JOIN and Dual‑Stream JOIN
Comparison of Two Architectures
Traditional Data Warehouse
Problems
1. Two separate computation pipelines cause duplicated work and waste resources. 2. Two independent data models make consistency hard to guarantee.
Real‑Time Data Warehouse
Unified basic public data
Ensured consistency of stream‑batch results
Improved timeliness of offline warehouse
Reduced component and pipeline maintenance costs
A Comprehensive Practical Exercise
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