Why Hologres Dynamic Table Beats Traditional Full Refresh for Real‑Time Data Warehousing

The article explains how Hologres Dynamic Table uses a stateful incremental refresh model to efficiently handle massive historical data with tiny daily updates, dramatically reducing latency and resource consumption compared with conventional full‑refresh pipelines across several real‑world join and aggregation scenarios.

Dynamic TableHologresIncremental Refresh

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Why Hologres Dynamic Table Beats Traditional Full Refresh for Real‑Time Data Warehousing

Big Data Technology Architecture

Aug 7, 2019 · Big Data

Why Choose Apache Flink for Real‑Time Stream Processing: Features and Lessons Learned

This article explains why the author chose Apache Flink for real‑time stream processing, highlighting its unique combination of high throughput, low latency, event‑time support, stateful computation, flexible windows, and fault tolerance, while also reflecting on the challenges of adopting a less‑documented technology.

Event TimeFlinkReal-Time

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Why Choose Apache Flink for Real‑Time Stream Processing: Features and Lessons Learned

Xianyu Technology

Mar 21, 2019 · Big Data

Design and Implementation of the Mahé Real-Time Product Selection System Using Blink Stream Computing

Mahé, Xianyu’s real‑time product selection platform, uses Alibaba’s Blink stream engine to merge, evaluate roughly 300 rule‑based filters per item and emit only changed results, processing 1.4 billion daily messages at up to 50 k TPS through a four‑layer, stateful architecture.

Big DataFlinkStateful Computation

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Design and Implementation of the Mahé Real-Time Product Selection System Using Blink Stream Computing