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