Separating Storage and Compute for Big Data Benefits

Every single business out there is getting benefits of big data. The need of the hour is to gather and analyze massive amounts of data. To be benefited as much as from big data many organizations are utilizing big data platforms capable of sorting all that information into actionable data. This type of platform needs intensive computing capabilities, which has lead to the rise of data-parallel programming systems, Hadoop is just one of various notable options.

There are many IT organizations who are struggling with where and how to store data in the cloud, the opportunity that a truly hybrid cloud creates for solution providers in terms of federating the management of data promises to be significant. There is an intense need to scale these systems, that is the reason Hadoop has traditionally tightly coupled storage and computation together. But this strategy does have some limits, especially when the computation to storage ratio is unknown or is subject to change. This is the reason why many organizations are looking at the possibility of separating storage and computation. In the recent past, this option supposed to be very expensive to achieve, but with an increased speed of the network, the separating strategy has become more beneficial, particularly in the kind of benefits it provides.

It helps to dissect and enrich data according to the customer needs

The top advantage of separating storage and compute is that it will give ease to the organizations which will allow them to analyze data in real-time. This real-time analysis allows organizations enrich their data, sort through it, and query it interactively in real time. With the help of this separating strategy, organizations can do easier analysis of data to tailor it to the specific needs of the organization at the exact moment they need it. Possibly, data is needed on customer interaction or sales numbers, and that information needs to be cross-referenced with customer profile data. All of this can be done with relative ease by separating storage and computation.

Data Protection and Security will be Improved

Because of the availability of the extensive amount of resources it requires to recruit and pay in-house security experts, offloading security monitoring and maintenance can free up the organization to focus more on crucial business operations while taking advantage of real-time security analysis. With this strategy, by simplifying more complex search queries, data analysis can pinpoint specific anomalies found within networks, essentially improving the ability to detect any intrusions in the moment they occur. Now, this automatic process can send out the necessary alerts to security teams, making them to act quickly to stop further infiltration into a system and prevent malicious code from spreading. The end result will be, the data that remains protected and secure.

Security is the subject of concern for those considering cloud adoption. Nevertheless, the numbers are increasingly indicating that on-premise data centers face just as many threats as cloud environments. For some last 4 to 5 years the vulnerability scanning attacks has increased immensely on the on-premise data centers and cloud-hosted environments. IaaS also offers the advantages of automatic updates or patching and the ability to easily implement the latest security tools, such as VPC and identity-based access controls. Additionally, rather than putting resources toward maintaining data availability, organizations can rely on the provider’s availability.

Makes Management Easier with Added Features

Big data platforms such as Hadoop truly grow to meet new business demands, giving more affordable shared storage options. One of the benefits is easier management of the platform. The method of installing standalone servers and architectures on a large scale is now considered outdated and hard to manage, so going with a shared storage strategy simplifies management tasks. Additionally, it also allows companies to adopt enterprise class features that make businesses more competitive. These features include scaling out NAS, virtualized environments, and SANs, and as those features become more common, they’ll provide organizations with their own benefits as well.

Helps to Secure, highly Available Communication between Applications

There is one more benefit added by separating storage and computation is greater versatility for the enterprise, especially when it comes to sharing data across different applications. By configuring real-time log analytics, separating helps organizations move important information from one program to another, creating an environment that’s quick to respond to changing conditions with ease. There is a thing to emphasize and that is the ability to pull that data from a variety of different sources. Businesses can get a full understanding of what the data entails and how best to use it only by pulling and gathering varied information can businesses get a full understanding of what the data entails and how productively use it. Without separating storage and compute it will be more difficult.

Day by day, big data is becoming a more essential component of any business, and using it to the fullest requires separating storage and computation. Shared storage solutions are plentiful, and the obstacles that impeded organizations before are being torn down one by one.

But now, businesses came to realize the benefits of big data, in some time soon they will also come to understand why separating storage and compute is a necessary move if they want to unlock all of big data’s potential. With the right strategy, soon organizations will realize the right potential of it.

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