6 Things to Keep in Mind When Choosing an Ideal Server for Big Data Requirements

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.

Conclusion

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!

The Big Data Hosting Dilemma: Is Your Provider Solving it?

Big Data Hosting

You’ve probably heard of how data is like the oil of the digital economy. Last century, everyone wanted to invest in petroleum. Today it’s data that is one of the most valuable resource a business can invest in. Once you start drilling, you may find data in limitless volumes. Hence – “Big data”: the field that helps businesses identify and analyze the deluge of data at their fingertips. So that they can put it to effective use. So, what’s this about a big data hosting dilemma?

Big Data Needs Big Web Hosting

Unlike raw crude oil, data itself has no universal value. Therefore, if you have lots of data but no means of processing it and extracting value, it’s pretty much worthless. Big data is gaining wide popularity across many industries. Mainly because of its capabilities of capturing, storing, and processing data which lets businesses gain that competitive market edge.

However, as the name suggests, big data is all about complex functioning, massive data sets, and intricate multilevel processes. And so, businesses can only get as much out of big data as their hardware allows. To complement big data, you also need strong and dynamic servers that can support sophisticated computing, processing, and storage requirements.

That’s why web hosting companies are key in determining the success of a business’s move into big data. Here we’re exploring some of the best options for big data hosting providers. As well as explore how each can help you boost your big data operations.

AWS (Amazon Web Services)

Amazon Web Services AWS - Big Data Hosting Provider

AWS enjoys the prime position (pun intended) in the big data hosting market. Amazon EC2 (Elastic Compute Cloud) for starters is one of Amazon’s most successful products. Clients love EC2 particularly for its exclusive capabilities and flexibility to scale.

The model lets you enjoy the maximum availability of resources to support fluctuating requirements. All without having to fork out package expenses. Because thanks to a PAYG (Pay as you go) approach, EC2 enables seamless scalability. Plus, it covers the two main bases you need for big data: performance and cost-efficiency.

Here’s a rundown of the main features of Amazon EC2 for supporting big data processing.

Amazon Elastic MapReduce:

Purpose-built and architected for massive data processing operations. EC2 and Amazon Simple Storage Services fuel its hosted Hadoop framework.

Amazon Dynamo DB:

A NoSQL (not only SQL) database service that’s fully managed and promises high tolerance against faults. With seamless scalability and independent provisioning capabilities, DynamoDB significantly reduces any need for active human intervention. Uncomplicated administration makes the experience convenient and smooth.

Amazon Simple Storage Service (S3):

Though thin on features, the Amazon Simple Storage Service is especially for high scale performance and massive storage capacities. It supports seamless scalability by allowing you to insert data in buckets. You can also select specific regions for physically storing your data to address speed or availability issues.

Amazon High-Performance Computing (HPC):

This service supports sophisticated tasks with specific needs. High-end professionals like scientists and academics use HPC for its high performance and rapid delivery, along with other industries too. Mainly because of the rise of big data hosting providers. Undoubtedly, easy reconfiguration provisos and high workload capabilities are the main benefits of Amazon HPC.

Amazon Redshift:

The focus of Redshift is to provide extreme storage capabilities to deliver massive data warehousing. Of course, supported by the strong foundation of MPP architecture. With its high-security ecosystem and reliable performance, Redshift is a powerful substitute for in-house data warehousing. Its architecture aligns well with high-end business intelligence tools. Thus, saving businesses significant infrastructure costs and maintenance hassles – and allowing further boosts in performance.

Google Big Data Services

Google Big Data Services - Big Data Hosting - Plesk

Internet giant Google is another major cloud services player that seems to be especially designed for big data hosting. Firstly, as the leading search engine, Google boasts an in-depth and first-hand experience in big data processing. Secondly, it also possesses the most sophisticated infrastructure out there to support big data operations.

Here are a few of the major features you need to know about Google Big Data services:

Google Compute Engine:

Promising a powerful combo of security and scalability, Google Compute Engine is an advanced computing solution. With its energy-efficient model, it helps enterprises to quickly complete complex computing processes with greater accuracy. It also prevents load imbalance with its reliable workload management solutions.

Google Big Query:

As the name suggests, Google Big Query is a reliable solution for data querying requirements. It supports quick and error-free processing of SQL-like queries against massive data sets. Its specific functionalities make it ideal for presenting an impromptu report or seeking deeper analysis. One limitation to note is that you can’t alter your data once it gets into Big Query.

Google Prediction API:

A powerful machine-learning tool whose advanced features discover and memorize patterns from huge volumes of data. It’s a self-evolving tool, therefore, it gains new, deeper insights about a data pattern each time you use it. Google Prediction API also allows you to use the patterns for purpose-specific analysis. Hence, stuff like customer sentiments and the detection of cyber threats.

Microsoft Azure for Big Data Hosting

Microsoft Azure Big Data Hosting - Plesk

One more major contender in the big data hosting market is Microsoft. Microsoft’s advanced capabilities allowed it to develop sophisticated and sharp big data tech. It’s an especially good option for those who are familiar with its proprietary products like Windows, Net, and SQLServer.

Window AzureHDInsight:

While fully-compatible with Apache, adopting it can connect you with various business intelligence tools as well as Microsoft Excel. You can also deploy In Windows Server.

OpenStack for Big Data Hosting

OpenStack Big Data Hosting - Plesk

As a popular open-source platform for cloud computing, OpenStack is big in Big Data application and processes. It offers clients a choice between public and private clouds. But you do have to follow standard implementation as per the organization’s rules. OpenStack may also limit specific customization efficiency of more sophisticated requirements.

Although not as established as others in our big data hosting provider list, OpenStack has many advantages to offer:

  • A Democratic Approach: OpenStack has a more democratic approach to big data hosting. Once developed, this model could offer huge cost savings to clients.
  • Hardware-agnostic: OpenStack is a hardware-agnostic cloud platform capable of accommodating multiple tenants.
  • In Talks With Leaders: OpenStack is talking to leading computing businesses like IBM, Dell, and Cisco. It’s safe to say that it will spark a revolution in the big data industry.
  • Ubuntu Base: Using Ubuntu as its base, OpenStack is an open-source project that aims to enhance the benefits of big data by making it easier and more affordable to work with.
  • Backed by Rackspace: The project has backing from Rackspace and has Nasa as a partner. Rackspace has plans to launch an OpenStack Hadoop services based on the public cloud.
  • Validated by Hortonworks: The data software company Hortonworks validates OpenStack.

Choosing Your Big Data Hosting Provider

The value of data is growing. And as you would expect, the role of big data is growing along with it.

This innovative field has already helped large global companies achieve a competitive edge in the market. However, due to its heavy and complicated functionalities, a small business’ server is not able to support big data operations. Hence, to get the most out of big data, you need big data web hosting services.

Choosing the right web hosting provider is key in determining the efficiency of your big data operations. Use this guide to get an idea which ecosystem/server is right for your business. Then let us solve the big data dilemma once and for all.