How ByteHouse Cuts Data Warehouse Costs While Boosting Performance

With data volumes growing explosively, modern enterprises face huge challenges in storing, processing, and analyzing data. In IT architecture, data warehouses play a critical role; if they operate inefficiently, costs soar and decision‑making slows, making cost‑reduction and efficiency improvement a perpetual goal for IT departments.

OLAP (online analytical processing) systems enable real‑time data analysis and decision support, but balancing cost and efficiency is difficult. Fast‑paced business demands shorten processing and analysis time while maintaining data accuracy, leading to higher hardware, server, and storage expenses, as well as increased algorithmic, operational, and migration costs.

1. Explicit Cost Challenges

Hardware costs: Deploying a data warehouse requires substantial compute (CPU) and storage (disk, storage clusters) resources, especially for TB‑to‑PB‑scale data.

Performance costs: Low energy efficiency forces the use of more resources to meet task deadlines. Improving compute efficiency and scaling storage capacity are needed to handle growing data volumes while reducing energy consumption.

2. Implicit Cost Challenges

Operations costs: Managing a complex data warehouse demands skilled personnel and significant time, especially when multiple components (ClickHouse, Elasticsearch, GreenPlum, etc.) increase system complexity.

Migration costs: Moving from legacy warehouses or analytical databases to ByteHouse incurs substantial labor and time, due to syntax and architectural differences.

Solution: ByteHouse

ByteHouse, a cloud‑native data warehouse from Volcano Engine’s VeDI platform, builds on ClickHouse technology. Since its internal launch in 2017, it has grown to over 18,000 nodes, with the largest analytical cluster exceeding 2,400 nodes and handling more than 700 PB of data.

Architecturally, ByteHouse follows modern cloud‑native principles: containerization, compute‑storage separation, multi‑tenant management, and read‑write separation. It supports both real‑time and massive offline analytics, optimizing for high throughput, concurrency, and complex queries, delivering sub‑second query responses for 99 % of requests.

The system uses a shared‑nothing compute layer and a shared‑everything storage layer, enabling elastic horizontal scaling of both layers. ByteHouse also offers managed, zero‑maintenance services, comprehensive cluster management tools, and full‑stack monitoring to simplify operations and troubleshooting.