Big Data and Cloud Computing — bring those two topics up at your next office party and you’re bound to get more dazed and confused looks than knowing nods. But thanks to the democratization of big data — opening up data analytics to mainstream information workers and developers, as well as data scientists and engineers — that’s all about to change.
As data storage costs continue to drop and storage capacity grows, both large and small enterprises in all kinds of industries stand to benefit from Big Data and analytics. And cloud computing will play a bigger role.
What the Cloud Provides
Proponents of cloud computing claim that the flexibility of the cloud makes it a good fit for big data analytics. Since the practice involves analyzing huge volumes of unstructured data to detect patterns and gain insights for improving business strategies, organizations utilizing the cloud can scale their usage on demand and pay accordingly.
The ability to store and analyze more data at lower costs accounts for the rise in popularity of two cloud technologies, Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). The advantage of PaaS is that it gives companies a very affordable way to increase or decrease storage capacity as needed.
IaaS technology ups processing capabilities by rapidly deploying additional computing nodes. With this kind of flexibility — allowing resources to be deployed rapidly and only as needed — big data cloud computing puts big data within the reach of companies that could never afford the high costs associated with buying sufficient hardware capacity to store and analyze large data sets.
Drawbacks to the Cloud
Despite the aforementioned benefits, the cloud does have some drawbacks when it comes to big data analysis.
For one thing, running sophisticated analytics software in the cloud can compromise storage capacity. And as data retention doubles and triples each year, cloud providers will have to figure out how to scale capacity to keep up with demand.
In addition to affecting storage capacity, the cloud’s highly virtualized and distributed nature also compromises storage performance — and the increasing demands of big data analytics aren’t helping matters in that area. As a result, the cloud is not yet capable of performing real-time data processing without producing outdated results due to latency.
Still, as demand for big data in the cloud continues to grow, cloud providers are gearing up for the challenge, finding new ways to tweak cloud architectures to improve storage capacity, performance and flexibility.
As the democratization of big data expands, the main issue businesses will need to consider is whether or not big data is right for them. Despite the urge to jump on the big data bandwagon, companies will need to evaluate their data sets to determine if big data analysis is actually warranted. In cases where the data sets are relatively small, other tools such as Excel, which has been seriously ramped up to take on big data with “Data Explorer,” will probably perform the analysis just as well.
But for those large and ever growing sets of unstructured data, big data analytics is the go-to tool for getting answers and gaining insights, many of which will be found within the flexible and affordable confines of the cloud.
Title image courtesy of Cameron Whitman (Shutterstock)
About the Author
Gil Allouche is the Vice President of Marketing at Qubole. Gil began his marketing career as a product strategist at SAP while earning his MBA at Babson College and is a former software engineer.
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