Real-time Data Warehouse Architecture and Hologres Technology Overview

The presentation begins with an analysis of real‑time data warehouse demands, highlighting the shift from batch‑only analytics to hybrid workloads that support both fine‑grained operational reporting and online services such as recommendation, profiling, and risk control.

It then introduces Alibaba's Hologres, a hybrid serving and analytical processing (HSAP) engine that unifies batch and streaming data in a single system, supporting high‑throughput writes, low‑latency point queries, and multi‑load capabilities.

Key technical features of Hologres are described, including its storage‑compute separation, use of the Pangu file system, row‑column coexistence, LSM‑style write path with Memtable and WAL, vectorized query engine (HQE), and fallback to PostgreSQL engine (PQE) for unsupported operators.

The article outlines three typical architecture patterns: ad‑hoc query, minute‑level near‑real‑time, and incremental computation, each illustrating how Flink preprocessing, materialized views, and binlog integration can achieve different latency‑cost trade‑offs.

Security capabilities are covered, emphasizing data encryption, access control, and disaster‑recovery mechanisms.

A Q&A section addresses common concerns such as update performance, row‑column coexistence, and the advantages of Hologres over multi‑stream Flink joins.

Overall, the content provides a comprehensive guide to designing, operating, and optimizing modern real‑time data warehouses using Hologres.