Clusters for ClickHouse, Spark, Kafka, and Hadoop: high-density storage, massive memory, and 10/25GbE networking for terabyte-scale analytics.
Big Data is all about throughput: nodes must read terabytes quickly and exchange them over the network. Architecture matters more than the power of a single server: three mid-range nodes almost always outperform one flagship.
ClickHouse on a single node can handle billions of rows: 16+ cores, 128 GB RAM, NVMe. Scale by adding nodes, not replacing servers: plan for identical platforms and 10GbE from day one.
Hot data and indexes — NVMe; warm partitions — SATA SSD; cold archive — HDD. ClickHouse and Spark support multi-tier storage — we use it instead of buying extra NVMe.
Kafka brokers require consistent write performance and network throughput; SATA SSDs/HDDs and 10GbE are sufficient. As you scale, separate your brokers and compute nodes onto different machines.
Describe your task — an engineer will select a server, calculate the price in tenge, and provide a delivery timeline. Custom assembly for any technical requirements.
16–24 cores, 128–192 GB RAM, 4–8 TB NVMe: Dell R650/R750. Standard reports on billions of rows in seconds.
Three mid-range nodes: parallelism, fault tolerance, and room to grow. A single 'fat' server is an anti-pattern for distributed tasks.
From 2 million ₸ for a node with NVMe; a three-node cluster — 6–9 million ₸. Refurbished platforms reduce entry costs by 1.5–2 times.
Yes: training and inference require GPU servers. We have a dedicated section for H100/A100/L40S models and rental options.
A single server often covers multiple tasks — check out our related solutions.
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