Big Data Servers Explained


Big data requires specialized software, storage, and computation techniques for processing large volumes of unstructured data. The diversity of software requires specialized servers which cater to the high demands of big data.

However, with the proper server strategy, businesses can utilize the power of data for deeper analytical insights, accelerating the growth of a company.

This article explains big data servers and the type of requirements needed to cater to big data server processing.

Big Data Servers Explained

What Are Big Data Servers?

Big data servers are dedicated servers configured for working with big data. A big data server must have:

  • High processing power for storage, retrieval, and analytics.
  • Software for collecting large volumes of unstructured data quickly.
  • Parallel computation capabilities with high data integrity.
  • High availability and fast recovery.

Big Data Servers vs. Regular Dedicated Servers

The table below outlines the main distinctions between big data servers and typical dedicated servers:

Big Data ServersDedicated Servers
Writing methodAsynchronous. No writing delays.Synchronous. Simultaneous and categorized with minimal to no writing delays.
StorageNoSQL or NewSQL systems.SQL systems.
TechnologyTechnologies are still in the developmental stages.Mature and well-developed technologies.
CostCostly hardware, affordable software.Affordable for both hardware and software.

The main difference between a big data server and a regular dedicated server is in the performance and cost.

How to Choose a Big Data Server?

Big data servers are challenging to configure and potentially have a steep price tag so choosing the ideal hardware and software requires a well-established strategy.

Most software used in big data recommends using distributed infrastructure. However, deploying on multiple servers is not necessary. Therefore, the size and cost of servers ultimately depend on the technologies the company operates and the amount of data being processed.

A big data company can use a single powerful dedicated server with a high core count. Ultimately, it all depends on the business needs and the quantity of information.

An alternative is a cluster of smaller dedicated servers in a private or public cloud, which provides distributed and versatile infrastructure necessary for big data. For example, automating the provisioning of bare metal cloud instances is perfect for big data analytics. Clustering several different server instances provides the robustness, scalability, and variety required for big data.

Try Bare Metal Cloud for as low as $0.10/HOUR! Mix and match 20 preconfigured instance types and create a robust environment that is fine-tuned to your compute, memory, storage, and networking needs.


How to Optimize Servers for Big Data Analytics?

Since big data servers are costly, choose the optimal hardware configuration to get the maximum out of your information. The following infrastructure parameters are essential for big data analytics:

  • A network with sufficient capacity for sending large volumes of data is necessary for big data servers. Minimize the costs by choosing a custom bandwidth if you roughly know how much data transfers. Unmetered bandwidth is available for large transfers.
  • Ample storage for analytical purposes with room to spare for indirectly generated data from analytics is necessary for big data.
  • Big data analytics applications consume a lot of memory. More RAM means less time taken to write and read from storage.
  • Processors with more cores are preferred instead of fewer powerful cores. Analytics tools spread across multiple threads, parallelizing execution on multiple cores.

What Is the Best Big Data Analytics Software?

The best data analytics tools overcome the challenges posed by big data. However, the amount of software currently available for analytics is overwhelming.

Big Data Analytics Technologies grouped by function

In general, three groupings of software exist based on the field of specialization. Below are some well-known and powerful tools in their respective categories.

1. Storage and Processing

  • HDFS is a fault-tolerant data storage system. As one of the main components of the Hadoop architecture, HDFS specifically caters to the needs of large volumes of data.
  • HBase is an open-source distributed database system that runs on top of HDFS. 
  • Hive is a data warehouse system built on top of Hadoop. The program helps query and process data from HBase and other external data sources.
  • Cassandra is a scalable NoSQL database with high availability created to handle large amounts of data. The database has its query language, CQL, to run data operations.
  • MongoDB is a high-performance NoSQL document database. The database is highly available and easily scalable, which is a must for big data.
  • Elasticsearch is a searchable database engine for storing and managing unstructured data. The database works as an analytics search engine for log files with features such as full-text search.

Note: Although there are some similarities, MongoDB and Cassandra are different databases with different functionalities. Check out our in-depth comparison of Cassandra vs. MongoDB.

2. Computation and Data Feeds

  • Apache Storm is a stream processing computation framework. The data streaming engine uses custom spouts and bolts to create custom distributed batch data streaming.
  • Apache Spark is a framework for cluster computing and analytics. One of Spark's main mechanisms is data parallelism and fault tolerance. Check out our tutorial for automated deployment of Spark clusters on a BMC.

Note: Learn how Apache Storm and Spark compare when working with data streams.

  • Logstash is a data processing streamline that ingests, transforms, and sends out data regardless of format. It works best when teamed with Elasticsearch and Kibana to create the ELK stack.
  • Kafka is an event streaming and processing service used for real-time analytics.

3. Visualization and Data Mining

  • Tableau is an immersive data visualization software with BI.
  • Power BI is a Microsoft service for analytics with interactive dashboards and a simple interface.
  • Knime is an open-source platform for generating reports with a modular pipeline, allowing integration for machine learning.
  • Grafana is a web application for analytics, monitoring, and visualization.  

Note: Add Prometheus as a Grafana data source and create a dashboard to monitor Kubernetes clusters in our step-by-step guide: Grafana Prometheus Dashboard Tutorial.


After reading this article, you should know what big data servers are, and which hardware and software enable big data analytics.

Next, learn more about virtual data centers and how big data companies benefit from the flexible cloud-based infrastructure in our blog article What is a Virtual Data Center (VDC)?

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Milica Dancuk
Milica Dancuk is a technical writer at phoenixNAP with a passion for programming. With a background in Electrical Engineering and Computing, coupled with her teaching experience, she excels at simplifying complex technical concepts in her writing.
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