By Eltjo Hofstee, Managing Director, Leaseweb UK According to a global Gartner survey of 196 organisations 91% have not yet…


By Eltjo Hofstee, Managing Director, Leaseweb UK

According to a global Gartner survey of 196 organisations 91% have not yet reached a ‘transformational’ level of maturity in data and analytics, despite it being the number one investment priority for CIOs. And with big data set to solve some of the biggest research challenges around today, this needs to change. It is absolutely vital for businesses to be able to process big data quickly and meaningfully if they are to keep on-track with the rapid growth in data.

Along with all the other contemporary buzzwords, ‘big data’ is increasingly thrown around in business and tech sectors as if everyone truly understands it. But do they really? Big data is the description for very large data sets that can be evaluated and provide insights around trends and patterns to drive better business decision-making. 

That may seem fairly easy to comprehend, and although plenty of information is available about big data technologies, few have actually mastered the knack of using big data to its full potential. A survey from Capgemini found that just 27% of executives described their big data initiatives as ‘successful’. This reinforces the fact that, while many are talking about big data and have ambitions around it, the majority of organisations still have quite a long way to go on their big data journey.

Implementing effective, fast data-processing can ensure your company’s continued success. While this may seem daunting, it actually gives us all the ability to analyse more inventively, even more so considering the large, diverse quantity of data produced by businesses these days.

Additionally, considering the growing dominance and capabilities of cloud computing, now is the perfect time to take a deeper look into ‘big data analytics’ so you, too, can leverage the power of big data to bring a greater competitive edge to your company.

Big data + cloud computing = a perfect match

Data-processing engines and frameworks are vital elements within a data system. While there is no key difference between the definitions of “engines” and “frameworks,” it’s important to define these terms separately — consider engines as the component responsible for operating on data while frameworks are typically a set of components that are designed to do the same.

Although systems designed to handle the data lifecycle are rather complicated, they ultimately share a similar objective: to operate over data with the aim of broadening understanding and surface patterns while gaining insight on complex interactions.

To be able to do all this, however, requires an infrastructure that supports large workloads. This is where cloud comes in. Cloud is considered a beneficial tool by enterprises globally because it has the ability to harness business intelligence (BI) in big data. In addition, the scalability of cloud environments makes it much easier for big data tools and applications, like Cloudera and Hadoop, to function.

Available programming frameworks to find a suitable fit

Several big data tools are available, some of which include:

Hadoop: This Java-based programming framework supports processing and storage of extremely large data sets. This is an open source framework and is part of the Apache project, sponsored by Apache Software Foundation, which works in a distributed computing environment. Hadoop supporting software packages and components can be deployed by organisations in their local data centre.

Apache Spark: Apache Spark isa fast engine used for big data processing that is capable of streaming and supporting SQL, graph processing, and machine learning. Alternatively, Apache Storm is also available as an open-source data processing system.

Cloudera Distributions: This is considered one of the latest open-source technologies available to discover, store, process, model, and serve large amounts of data. Apache Hadoop is considered part of this platform.

Hadoop on CloudStack to Crunch Data Successfully

Hadoop, which is based on Google’s MapReduce and File System technologies, has gained widespread adoption in the industry. This framework is similar to CloudStack and is implemented in Java.

As the first ever cloud platform in the industry to join the Apache Software Foundation, CloudStack has fast become the logical cloud choice for organisations that prefer open-source options for their cloud and big data infrastructure.

The combination of Hadoop and CloudStack is really a great match made in the clouds. Considering the availability of big data tools such as these, working in the cloud to leverage meaningful business intelligence, now is the perfect time to harness the power of big data so that your business can think, and achieve, big.


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