Real-Time High-Performance Analytics on Data Lakes with CloudLakehouse Multi-Cluster Architecture

In the fast‑evolving big‑data era, the performance and efficiency of data platforms are critical for enterprises.

CloudLakehouse introduces a Multi‑Cluster elastic architecture to address high‑concurrency and low‑latency analysis on data lakes, enabling real‑time customer‑facing analytics.

Key requirements of real‑time analysis include freshness, low query latency, high concurrency, and the ability to compare real‑time with historical data.

Data‑lake challenges such as storage‑compute separation, write latency, cache dependency, and performance degradation are discussed.

The solution adopts read‑write separation, a serverless elastic resource pool, independent compute clusters, dynamic cache mechanisms, and a self‑developed C++ SQL engine supporting both batch and streaming workloads.

Elastic concurrency scaling allows resources to expand vertically and horizontally on demand, reducing cost while meeting peak loads.

Preload cache pre‑loads hot data into local caches to stabilize query latency for both offline and real‑time scenarios.

Automation and intelligent management provide fully managed SaaS services, self‑service resource control, automatic compaction, sort optimization, index recommendation, and auto‑materialized view generation.

A typical SaaS use case demonstrates CDC‑based real‑time data ingestion, second‑level data freshness, and millisecond‑level query latency even under 32 concurrent queries.

The summary highlights elastic scaling, proactive caching, and automated platform capabilities as key to achieving high‑performance, low‑cost real‑time analytics on data lakes.

Q&A covers transaction support, C++ engine details, incremental processing, and integration with Spark/Flink.