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The pros and cons of harnessing big data in the cloud

This article was originally published on Technology.Info.
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Big data is one of the most talked about topics in IT these days. And big data as a service is really gaining traction as a means for organisations to leverage their own massive data stores for competitive advantage. Still, as it is with any technology, cloud-based analytics has both good and bad aspects that should be considered before adoption. Here’s a look at the pros and cons of big data in the cloud.


Rapid provisioning

While traditional big data analytics platforms require a slow and costly process to obtain the necessary hardware and software, cloud-based platforms give organisations the ability to provision huge databases as needed. Literally thousands of servers can be spun up in only a matter of minutes.

Elastic scalability

Organisations are being overwhelmed with data. Utilising elastic clusters, cloud-based platforms allow organisations to easily scale up or down, provisioning only the computing resources needed to meet current data storage and processing demands.

Higher availability and efficiency

Unlike traditional warehouse platforms, cloud-based infrastructures leverage high availability architecture to minimise downtime. Plus, a virtualised environment balances workload across multiple applications and shares pooled resources, resulting in higher efficiency.

Relevant real-time analysis

Cloud-based platforms utilise an elastic set of resources to expedite the data analysis process, returning result sets rapidly enough to provide business-relevant analysis and insight - in real-time. This is of huge benefit to companies looking to deliver more targeted and personalised experiences to their customers.

Lower up-front costs

Traditional infrastructure necessitates the purchase, installation and configuration of expensive hardware. And studies show that most organisations needlessly spend money on resources they will never actually need to meet their data demands. Big Data cloud-based platforms, on the other hand, give organisations access to a highly distributed, full-featured big data analytics platform at a fraction of the costs of traditional infrastructure.



Of course power outages are going to occur in an organisation’s internal data warehouse. However, cloud failures are more public and can have more lasting negative consequences. A case in point is the Amazon’s cloud outage of 2011 that took down a number of sites for hours and even days. Organisations engaging cloud services need to be aware that such failures could cause current and potential customers to question the security and reliability of the cloud, which in turn could negatively affect business.

Costs of data migration and integration

Although not always the case, transmitting data from internal systems to a cloud-hosted platform could require huge and costly amounts of bandwidth, which could end up being quite costly for organisations.

Lack of best practices

Public cloud-hosted data warehouses are still relatively new. As a result, there is still a learning curve with the cloud. However, best practices will continue to emerge with greater adoption and more use cases.

Potentially higher costs

One of the main attractions of cloud-based analytics platforms is that they can save organisations substantial costs over an on-premise database. However, in some cases organisations may find that their cloud computing costs are much higher than anticipated. Before entering into any agreement with a cloud vendor it’s critical to know what the costs will be.

Due to the data explosion, more and more companies with massive amounts of data waiting to be monetised will be turning to big data analytics solutions. Although there are still some bugs to work out, cloud-based big data platforms offer affordable, scalable and effective solutions over traditional platforms for storing, managing and analysing big data.