How to Optimize a Server for Big Data Analytics
Ninety percent of the world’s data was generated since 2016. The amount of data generated by businesses, government agencies, and consumers continues to grow exponentially. In 2020, over 40 trillion gigabytes of data are stored around the world according to market intelligence firm IDC.
Much of the data generated by businesses and their customers contains valuable information. But only companies with the infrastructure, software, and expertise to analyze large amounts of data can extract that value.
In this article, we look at what big data is and the software and hardware that businesses use to extract valuable insights from it.
What Is Big Data?
Big data does not have a precise technical definition. We might think of big data as any collection of data that exceeds a business’s ability to store and process in-house on consumer computers and smaller servers. It might be a few terabytes for a small business or many petabytes—a petabyte is 1,024 terabytes—for a large enterprise organization.
One possible definition categorizes big data according to the “Five Vs.” They are Velocity, Volume, Value, Variety, and Veracity. Volume refers to how much data there is. Velocity refers to how quickly data is generated; large businesses may generate many terabytes of data each day. Variety refers to the fact that big data may include many types of data, often unstructured.
Learn more: What Are the 5 V’s of Big Data
Businesses collect as much data as possible in the hope of analyzing it for useful insights. For example, they may want to perform a cohort analysis on sales data to discover which groups of customers have the highest lifetime value. To do so, they need to collect, transform, and analyze as much sales data as possible.
What Are the Best Tools for Big Data Analytics?
There are many specialist tools designed to accelerate big data analysis. They store data efficiently and use optimized algorithms such as MapReduce to process vast amounts of data quickly. They are engineered to make optimal use of the hardware available to them.
Among the most popular big data tools are:
- Hadoop, a framework for the distributed storage and processing of large amounts of data.
- Cassandra, a distributed NoSQL database initially developed by Facebook.
- Apache Spark, a distributed big data processing framework widely used by financial institutions, telecoms companies, governments, and technology businesses such as Facebook and Google.
- ElasticSearch, a distributed search and analytics engine used for everything from enterprise search engines to infrastructure monitoring and security analytics.
- Knime, a data analytics platform that includes machine learning and data mining tools.
It is also possible to use mainstream relational database tools such as MySQL and PostgreSQL for big data analytics, depending on the volume and type of data involved.
Clusters vs. Single Servers for Big Data
You may have noticed that the tool descriptions in the last section often include the word “distributed.” That’s because big data tools expect to be deployed on more than one server. They can manage the resources of many servers to process massive volumes of data quickly. Hadoop, for example, is explicitly designed to run on dozens or hundreds of individual servers joined together in clusters.
However, users are not forced to deploy on multiple servers. For smaller big data analytics purposes, a single powerful dedicated server may be enough. It is also possible to launch clusters of virtual machines to act as Hadoop or Cassandra nodes on a high-spec dedicated server. Many businesses bring together a cluster of dedicated servers as a pool of resources in a private cloud. They can then efficiently manage and distribute infrastructure resources to launch multiple big data analytics projects on their private cloud.
The optimal architecture for your business’s big data infrastructure depends on the amount of data involved, scalability and redundancy requirements, and the software you will run.
Our big data analytics server hosting specialists can guide you to the best infrastructure solution for your business. Contact us for a free consultation to learn more.
Optimizing Servers for Big Data Analytics
There are several factors to keep in mind when choosing and optimizing a server for big data analytics.
- You will be transferring large amounts of data to the server for processing.
- If you use a cluster, the backplane—the connections between servers—must be able to handle significant volumes of data.
- Big data tools are optimized for parallel execution, using multiple threads on each server and distributing work between multiple servers.
- Many big data tools—although not all—are optimized for in-memory processing, which is typically much faster than disk-based processing.
There is no one-size-fits-all server hosting solution for big data. The ideal intersection of cost and capability depends on the specifics of each project. But we can give some general guidance here.
You will write large amounts of data to your server, often from a third-party service or data center. The network can become a bottleneck if the network interface doesn’t have sufficient capacity. We recommend a 1 Gbps minimum and more if you expect to send large volumes of data to the server regularly.
To minimize data costs, choose a provider that offers custom bandwidth packages close to the amount of data you expect to transfer. We offer packages that range from 20 TB per month to 1000 TB per month, with unmetered bandwidth for customers with large data transfer requirements.
Your server should have enough storage for the data you intend to analyze, with a generous buffer for storing intermediate data generated during analysis. Fast storage is preferable, but it is not usually necessary to pack a server with terabytes of SSD storage. Spinning hard-drives are slower and less expensive, but may be adequate for your purposes.
Which you choose depends on the particular requirements of your data, but you must be able to store all of the data that you expect to analyze in each period. Spark and Hadoop both work best with multiple drives.
Where RAM is concerned, more is better. Big data analytics applications will consume as much RAM as you can give them. Tools such as Spark and Couchbase prefer to carry out computation in memory, and processing will be much faster if they don’t have to read and write to storage because they run out of RAM.
For production workloads, a server with 64 GB or more is preferable, although there is no general formula. Our consultants can advise you on an appropriate amount of RAM, given your expected workloads and budget.
Big data analytics tools such as Spark divide processing across multiple threads which are executed in parallel across the machine’s available cores. For example, Spark recommends that each server has at least 8-16 cores, and depending on the load may need more. Optimizing for more cores will result in better performance than optimizing for more powerful cores in smaller numbers.
Our Recommended Big Data Server
Based on the above suggestions, we recommend the Dual Intel Xeon Server 4210. It features memory configurations up to 512GB of RAM and with a CPU passmark of 30,000, it is a fantastic value starting at $349/month. It has the processing capacity to handle demanding big data tasks, and coupled with NVMe storage it will deliver blazing fast performance.
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The ideal specifications for a big data analytics server depend on the volume and velocity of the data your business needs to analyze. Our server hosting platform offers a wide range of custom options so that you can choose the server or cluster of servers that best fits your needs and budget.
To talk to our server hosting specialist about which server hosting is right for your big data analytics project, start a conversation in the chat window on this page or contact us by phone or email for a free initial consultation.