How Alibaba Cloud Flink + Hologres Power Real‑Time Data Warehouses

Why Real‑Time Data Warehouses Matter

With the rapid growth of big data, enterprises need data to reach analysts as quickly as possible to maximize its value. Building a real‑time data warehouse that supports instant writes, agile business response, self‑service analytics, easy operations, and cloud‑native elasticity requires a powerful solution.

Core Capabilities of Alibaba Cloud Flink CDC

Flink CDC, an open‑source data‑integration framework released by Alibaba Cloud, provides full‑incremental, lock‑free, parallel, and fault‑tolerant data synchronization. It can replace traditional ETL components (DataX, Canal, Debezium) and Kafka, merging the extraction and transformation layers to reduce component count, operational overhead, hardware cost, and end‑to‑end latency.

Key features include:

Full‑incremental integrated sync with lock‑free reads, parallelism, checkpoint‑based resume, and no‑duplicate guarantees.

Automatic table‑structure change synchronization via Catalog and CTAS syntax.

Whole‑database sync using CDAS (Create Database AS) syntax.

Sharding and table‑splitting merge sync using regular‑expression‑based source specifications.

Flink SQL> USE CATALOG holo;</code><code>Flink SQL> CREATE TABLE user AS TABLE mysql.`order_db`.`user`;

Hologres: A One‑Stop Real‑Time Warehouse Engine

Hologres is a self‑developed, cloud‑native real‑time warehouse that supports massive real‑time writes, updates, and analytical queries. It offers PostgreSQL‑compatible SQL, PB‑level OLAP, low‑latency serving, and seamless integration with Flink, MaxCompute, and DataWorks.

Key capabilities:

High‑performance real‑time write and update : Up to 2.3 M rows/s for append‑only tables, 200 k RPS for primary‑key inserts, and 70‑80 k RPS for upserts on billions of rows.

Real‑time OLAP : MPP parallelism, column/row storage, vectorized operators, sub‑second query latency on TPCH 100 GB.

High‑performance online serving : Millions of QPS point‑lookup with millisecond latency.

Read/write separation and high availability : Primary‑replica architecture with independent read/write instances.

Binlog subscription : Hologres binlog provides ordered, incremental change streams for downstream processing.

Real‑Time Warehouse Architectures

Two main patterns are described:

ETL (Extract‑Transform‑Load) : Data is extracted from sources, transformed in Flink, and loaded into Hologres, following ODS → DWD → DWS → ADS layers.

ELT (Extract‑Load‑Transform) : Data is loaded directly into Hologres and transformed there, reducing pipeline complexity and latency.

Both patterns benefit from Flink + Hologres’s ability to read full snapshots and then switch to CDC streams, enabling continuous, low‑latency data pipelines.

Typical Use Cases and Performance Benchmarks

Benchmarks show 10 × faster query performance compared with open‑source stacks, millions of QPS point‑lookups, and sub‑second query latency for both OLAP and serving workloads. Real‑world cases include real‑time recommendation, risk control, and large‑screen dashboards.

A top‑20 global game company replaced a Flink + Presto + ClickHouse + HBase architecture with Flink + Hologres, achieving a 100 %+ performance boost, unified storage, and simplified operations.

Business Benefits

Instant data visibility with millisecond‑level write latency.

Automatic schema evolution reduces maintenance effort.

Unified query layer delivers up to 10 × faster joins and queries.

Consolidated architecture lowers operational complexity and cost.

Overall, Alibaba Cloud Flink and Hologres provide a comprehensive, cloud‑native solution for building enterprise‑grade real‑time data warehouses.