Massive Storage Density
Store Petabytes of data cost-effectively. Choose servers with 100TB+ of Raw Storage (High-Capacity HDDs) for Data Lakes, or Ultra-fast NVMe for real-time analytics.
Do you offer a private network for cluster communication?
Yes.This is critical for Big Data shuffling. We provide a private VLAN that connects all your servers. This traffic is completely unmetered(no bandwidth usage caps). We offer 1Gbps, 10Gbps, and even 25Gbps private networking options to ensure your "Shuffle" phase never bottlenecks.
Can I mix NVMe and HDD on the same server?
Yes. This is a standard Tiered Storageconfiguration for Hadoop. You can use NVMe drivesfor the OS and "Scratch" space (intermediate data) to speed up processing/shuffling, while using high-capacity Enterprise HDDsfor HDFS data storage. This gives you the best balance of speed and cost per TB.
How does bare metal compare to AWS EMR or Google Dataproc?
Predictable Cost & Better Performance.With AWS EMR, you pay an extra fee on top of the EC2 cost, plus expensive egress fees. With ServerMO, you pay a flat monthly feefor the hardware. Since there is no hypervisor overhead (virtualization), your jobs typically run 20-30% fasteron bare metal due to direct hardware access.
Do you manage the Hadoop/Spark software?
No. We provide the Unmanaged Bare Metalinfrastructure. You have full root access to install and configure your own stack (e.g., Cloudera, Hortonworks, or vanilla Apache). This ensures you have no vendor lock-inand absolute control over your data security and versioning.
Is RAID recommended for HDFS Datanodes?
Generally, No.HDFS (Hadoop Distributed File System) manages data redundancy by replicating blocks across different servers (usually 3x replication). Using RAID 0/5/10 on the hardware level is often redundant and reduces write performance. We recommend presenting disks as JBOD (Just a Bunch Of Disks)for HDFS Datanodes. However, for the OS drive or NoSQL databases (MongoDB/Cassandra), RAID is highly recommended.
Can I use LACP Bonding to double my network throughput?
Yes. For extreme data ingestion (like Kafka or Spark Streaming), network bandwidth is often the bottleneck. We support LACP (Link Aggregation Control Protocol)(802.3ad). We can bond two 10Gbps ports to give your server a massive 20Gbps dedicated pipe, ensuring your node can ingest streams without dropped packets.
What is the best hardware config for Apache Kafka brokers?
Kafka relies heavily on Sequential Disk I/Oand Page Cache. We recommend:
- RAM:64GB-128GB (to maximize OS Page Cache).
- Storage:NVMe SSDs (for low latency) OR multiple SATA SSDs in RAID 10. Avoid spinning HDDs for high-throughput brokers as seek times will kill performance.
Why is Bare Metal NUMA awareness important for Spark & Redis?
In a virtualized cloud, you don't control which physical RAM stick your VM accesses, creating latency. On ServerMO bare metal, you have full visibility into the NUMA (Non-Uniform Memory Access)topology. You can pin your Spark executors or Redis instances to specific CPU cores and local memory banks. This massive reduction in memory latency significantly boosts performance for in-memory processing.
Best storage strategy for Cassandra/ScyllaDB Commit Logs?
For write-heavy NoSQL databases, the "Commit Log" is the bottleneck. We recommend a Split Storage Strategy:
- Drive 1 (NVMe):Dedicated exclusively for the Commit Log(Sequential Writes).
- Drive 2+ (NVMe/SSD):Dedicated for the SSTables/Data(Random Reads/Writes).
Separating these I/O streams on physical hardware prevents write contention and ensures consistent low latency during compaction storms.
Can I build an S3-compatible Data Lakehouse on bare metal?
Yes. Many clients use our High-Density HDD servers to run MinIOor Ceph Object Gateway. This gives you a private, S3-compatible object storage layer that costs a fraction of AWS S3. You can then point your Spark or Presto/Trino compute nodes (running on separate NVMe servers) to this private Data Lake, creating a high-performance, cost-effective Lakehouse Architecture.
