Table of contents
Why big data analytics demands robust infrastructure Why choose dedicated server Common big data use cases How to choose the right dedicated server When to consider FAQs Next steps
Get the industry’s best dedicated server hosting ◦ 99.999% uptime
◦ Security rich
◦ Built to spec
Explore options

Dedicated Server → Use Cases → Big Data Analytics

Dedicated server hosting for big data analytics

Big data is only as powerful as the infrastructure behind it. If your analytics workloads are getting bottlenecked by limited resources or unpredictable performance, it’s time to look at something more serious.

Dedicated servers offer raw compute, fast storage, and full control, making them an ideal environment for data-intensive workloads.

Explore dedicated servers
Build now

Why big data analytics demands robust infrastructure

Data analytics has gone well beyond spreadsheets and dashboards. Modern pipelines often include real-time ingestion, distributed processing, and machine learning … each with demanding hardware needs.

High-performance compute and memory

Big data workloads thrive on CPU cores and memory bandwidth. Whether you’re running Spark jobs, training ML models, or querying large datasets, you’ll want:

  • Multi-core CPUs (16+ threads)
  • ECC memory up to 1TB or more
  • High memory throughput for in-memory analytics

This ensures you can process large datasets efficiently without hitting memory or CPU bottlenecks.

Fast, scalable storage

Analytics workloads often involve reading and writing massive volumes of data—think petabytes of logs, IoT streams, or user interactions. The right storage setup makes or breaks performance.

  • NVMe SSDs for high-speed reads/writes
  • RAID 10 or ZFS for data redundancy and throughput
  • Optional SAN/NAS for massive-scale storage

Storage IOPS and latency directly impact ETL processes, batch jobs, and real-time query speeds.

Reliable, consistent network throughput

Data doesn’t live in a vacuum. Whether you’re syncing from data lakes, APIs, or other systems, consistent network performance is critical.

  • Dedicated 1–10 Gbps network connections
  • Low-latency internal networking for clusters
  • Private VLANs or IPMI for secure remote access

Why choose dedicated server hosting for big data?

Unlike cloud VMs or shared hosting, single-tenant dedicated servers give you full control over the entire machine without noisy neighbors, overage fees, or virtualization overhead.

Raw performance without neighbors

On a dedicated server, you get direct access to:

  • 100% of the CPU and memory
  • High-speed local disks
  • Predictable network I/O

There’s no hypervisor in the middle, and no surprise CPU throttling because someone else on the node maxed out their resources.

Complete control over environment

Install your own analytics stack, configure your own storage layout, and fine-tune performance for your specific tools.

  • Full OS-level control (Linux, Windows, custom distros)
  • Support for Spark, Flink, Kafka, Presto, ClickHouse, and more
  • Root access to deploy scripts, manage users, or scale services

Better cost-to-performance ratio over time

For long-running or always-on workloads, dedicated servers often beat cloud pricing. You’re paying for hardware, not per-hour usage or bandwidth egress.

  • No surprise bills from overage or API throttling
  • Consistent monthly pricing
  • Ideal for fixed-budget operations or predictable data loads

Common big data use cases for dedicated servers

From warehousing to real-time analysis, dedicated servers support a wide range of big data applications.

Data lakes and data warehouses

Store raw and structured data in systems like HDFS, Ceph, or columnar databases. You’ll get:

  • Scalable storage for historical data
  • Fast access for BI queries
  • Flexibility to integrate with tools like Apache Hive or Trino

Stream processing and real-time analytics

For log processing, fraud detection, or live telemetry dashboards, dedicated servers run:

  • Kafka brokers and consumers
  • Spark Streaming or Apache Flink
  • Custom real-time ETL pipelines

Consistent CPU and RAM access are critical for sub-second data processing.

Machine learning and predictive modeling

Training machine learning models on large datasets? Dedicated servers let you scale vertically or horizontally:

  • CPU-only servers for preprocessing and small models
  • GPU servers (e.g. with NVIDIA L40S or H100) for deep learning
  • Persistent, high-speed local storage for datasets

BI tools and analytics platforms

Host analytics dashboards or APIs that query big datasets directly:

  • Tableau, Metabase, Power BI Report Server
  • REST or GraphQL endpoints for internal data teams
  • Full control over user permissions and data locality

No vendor lock-in, and no egress fees when your data stays local.

How to choose the right dedicated server for big data

The right configuration depends on the size of your data, the speed you need, and how often your workloads run.

CPU and memory requirements

  • Start with 16–64 core CPUs for multi-threaded tasks
  • Aim for at least 256GB RAM if working with Spark or in-memory analytics
  • Consider NUMA-aware systems for memory-bound jobs

Storage configuration

  • NVMe SSDs for hot data and heavy reads
  • RAID 10 or ZFS for redundancy and write performance
  • SATA HDDs or NAS for cold archival storage

Keep hot and cold data tiers separate to avoid IOPS bottlenecks.

Network connectivity

  • 1–10 Gbps network interfaces
  • Private networking for multi-server clusters
  • DDoS protection and traffic shaping for production environments

If pulling data from external APIs or devices, prioritize low latency.

Management and support

If your team doesn’t have a full-time sysadmin:

  • Look for fully managed options
  • Ask about OS updates, firewall configs, and hardware monitoring
  • Choose a host with 24/7 expert support and SLAs

When to consider a multi-server cluster

If one server isn’t enough, you don’t have to go cloud-native. Many analytics teams run clusters on bare metal using:

  • Spark or Dask with distributed workers
  • Kafka with broker replication
  • Sharded Postgres or ClickHouse nodes
  • Ceph or Gluster for distributed storage

A good hosting provider can help you plan out node topology, rack layout, and internal networking to keep things simple and scalable.

FAQ: Dedicated server hosting for big data

It depends. SQL Server is powerful for structured data and supports tools like PolyBase for integrating with big data sources. But it’s not ideal for massive-scale analytics or unstructured datasets. For true big data, look at tools like Spark, Hadoop, or ClickHouse.

Liquid Web, of course. 😉 The best option depends on your workload. Look for providers offering:

  • High-core CPU configurations
  • NVMe or SAN storage
  • 24/7 expert support
  • Managed services if you need help with OS, monitoring, or security

“Hosting” is a general term. A dedicated server means you get an entire physical machine for yourself—no shared CPU, memory, or disk. It’s the highest-performing, most customizable hosting type available, ideal for demanding workloads like big data analytics.

Additional resources

What is a dedicated server? →

Benefits, use cases, and how to get started

Why dedicated servers are essential for SaaS applications →

​Discover why dedicated servers are essential for SaaS applications, offering unparalleled control, performance, and scalability for your platform. ​

Fully managed dedicated hosting →

What it means and what fully managed services cover on dedicated hosting

Chris LaNasa is Sr. Director of Product Marketing at Liquid Web. He has worked in hosting since 2020, applying his award-winning storytelling skills to helping people find the server solutions they need. When he’s not digging a narrative out of a dataset, Chris enjoys photography and hiking the beauty of Utah, where he lives with his wife.

Let us help you find the right hosting solution

Loading form…