Big data refers to a massive volume of data sets that can not be processed by typical software or conventional computing techniques. Along with high volume, the term also indicates the diversity in tools, techniques, and frameworks that make it challenging to tackle and process the data. When stored and processed properly, this massive data can offer deep insights to the businesses. There are a number of ways in which big data can help businesses grow at an accelerated rate.
How Can Businesses Benefit From Big Data?
The businesses can store and process high amounts of data from diverse internal and external sources. Like company databases, social networks, and search engines to get excellent business ideas. It can also allow them to forecast the events that can have a direct impact on business operations and performance. On the marketing front, it can help you increase the conversion rate by offering only relevant schemes, launches, and promo offers to the customers based on their buying behavior. The progressive companies are using big data for new product development, understanding the market conditions, and utilizing the present and upcoming trends for direct business benefits.
The Role of Server in Big Data
For enjoying the optimum business benefits out of big data it’s important to choose the ideal hardware that can proactively assist in big data operations without significantly inflating the costs or complications. There are some challenges to address like determining the processing requirements, high volume data storage at superfast speed, and supporting simultaneous computations of massive levels without compromising with the output. An important part of this strategy is to choose the right type of server.
The standard servers generally lack the resource volume and technical configuration required for various big data operations. So you would need the premium, purpose-built servers that are specially tailored to accommodate the massive data volume. As well as support the computational, analytical, and processing tasks. However, the final decision should be based on your specific requirements as no two customers are the same. You can find additional information on big data hosting in this previous article.
In this blog we are going to present some of the ideal factors to keep in mind while deciding on the ideal server for ensuring optimum big data benefits:
1. Choose Servers with High-Capacity
The ideal properties of a big data server are massive storage, ultra-fast recovery, and high-end analytical capability. So, you need the servers that have the right configuration and capacities to meet all these requirements without any compromise.
- Volume. As the name suggests, the big data feeds on loads of data that can go up to petabytes. For the uninformed, a single Petabyte is equal to 1,000,000 GB. So, make sure that your server can not only handle this massive amount of data but can also continue working consistently while handling it.
- Real-Time Analysis. The USP of big data is organizing and structuring a huge volume of diverse and unstructured data and seamlessly adding the latter to the available structured data. So, you would need the servers with very high processing capacities to handle this requirement efficiently without fail.
- Retrieval capabilities. Big data has big objectives too. For instance, real-time stock trading analysis where even a fraction of seconds matters a lot and can introduce multiple changes. For that, your server should fully support multiple users who are concurrently adding multiple inputs every second.
2. Sufficient Memory
RAM is one of the prime requirements for big data analytics tools and applications. Using RAM instead of storage will significantly accelerate the processing speed and help you to gain more output in relatively less time. It translates to better productivity and quicker time-to-market – the two factors that offer you a competitive edge in the industry. Due to varying requirements in terms of volumes and operations, it is not possible to advise on a typical RAM volume. However, to be on the safer side it is good to go with at least 64GB RAM. The readers are advised to discuss their requirements with the providers to know about the ideal memory requirements for their purpose.
3. Better RoI with NoSQL Databases, MPP and MapReduce
You also need to assist your clients in neatly segregating the analytical and operational requirements. It requires wisely optimizing the server hardware to meet the purpose. It is best to go for the NoSQL databases.
Unlike traditional databases, the NoSQL databases are not limited to a single server but can be widely spread across multiple servers. It helps it in dealing with tremendous computations by multiplying its capabilities manifolds and instantly scale up to the changing requirements in a fraction of seconds.
NoSQL databases can be defined as a mechanism that doesn’t use the tabular methodology for saving the data. Its non-relational data storage technology efficiently helps the businesses overcome the limitations and complexity inherent in traditional relational databases. To the end-users, this mechanism offers high speed scaling at relatively very less cost.
To accelerate the analytical big data capabilities you can rely on MPP databases (massively parallel processing) and MapReduce. These databases can significantly outscale the traditional single severs. You may also look for the NoSQL systems with inbuilt MapReduce functionality that allows it to scale to the cloud or a cluster of servers along with NoSQL.
4. Sufficient Network Capacity
You would need to send massive data volumes to the server. Lack of sufficient network capacity can throttle your operations. Be considerate of the fluctuations as well. You wouldn’t regularly be writing huge data volumes, which means that buying high bandwidth plans isn’t a cost-efficient solution for you. So, opt for the bespoke bandwidth solutions that allow you to select the ideal bandwidth to competently fulfill your data transfer requirements.
You can choose different bandwidth packages starting from 20 TB and going up to 1000 TB per month. To make things easier you may like to inform your provider about your expected data transfer requirements and ask them about the ideal bandwidth volume. Reputed providers can also offer you unmetered bandwidth for more demanding enterprise clients. Depending upon the volume and frequency of data 1Gbps is the least amount of bandwidth you require for your server.
5. Purpose-Specific Storage Capabilities
Along with storing permanent data your server also needs to accommodate huge amounts of intermediate data produced during various analytical processes. So, you would need sufficient data storage, Instead of choosing storage based on their capacity, think about their relevance for your purpose. The reputed vendors would always suggest you check your requirements before buying the storage. For instance, investing huge amounts on expensive SSD storage doesn’t make sense if your data storage requirements are modest and the traditional HDD can solve your purpose at much lower prices.
6. High-End Processing Capacity
The analytics tools related to big data generally divide the processing operations across different threads. These threads are distributed across different cores of the machine and are executed simultaneously. For a modest, to average load, you need 8-16 cores but may require more than that depending on the load. The rule of thumb is to prefer a higher number of cores rather than a smaller volume of highly powerful cores if you are looking for more competent performance.
Should You Use Software for Server Optimization to Meet Big Data Requirements?
The big data ecosystem has very specific needs that standard data servers with limited capabilities in terms of multitasking, output, and analytical insights can’t support. It also lacks the ultra-speed needed for real-time analytical data processing. So, you would require bespoke enterprise servers that seamlessly adapt to your particular needs in terms of volume, velocity, and diverse logical operations. For massive big data operations, you may need white box servers.
While technically it’s possible to employ software for optimizing the server environment. It may prove to be an expensive option in the long run by significantly reducing the RoI.
It also exposes your system to various security risks while at the same time increasing the management hassles like license acquisition/maintenance, etc. Moreover, you would have limited opportunities to fully utilize the available resources and infrastructure.
On the other hand, using a purpose-specific server for the big data requirements offers multiple benefits like:
- More operations per I/O that translate to better computational power
- Higher capabilities for parallel processing
- Improved virtualization power
- Better scalability
- Modular design benefits
- Higher memory
- Better utilization of the processor
Additionally, specially tailored servers can smartly work in collaboration. To assure the best possible utilization, virtualization, and parallel processing requirements. Due to their specific architecture, it’s easier to scale and manage them.
Big data can help your business grow at a very high rate. However, in order to get the best benefits out of your big data strategy, you need to build a purpose-specific ecosystem that also includes ideal hardware.
So, we mentioned some major factors to keep in mind while choosing the ideal server for your big data requirements. And now it’s time for you to let us know in the comments section below how do you think you can benefit from it. We want to hear from you!
Great article. The volume of stories I’ve read of people cutting corners to save a few pounds here and there. Get the best set up for the job, from the start! Thanks for sharing.
Thanks for your feedback Andrew 🙂