That may sound simple enough, and although lots of information is available about big data technologies, few have actually mastered the art of using big data to its full potential. In a survey undertaken by Capgemini, just 27% of executives surveyed described their big data initiatives as ‘successful’, reinforcing that while many are talking about it and ambitions around it, many businesses still have much to learn
Implementing effective, fast data processing can guarantee that your company continues to be successful, and is only growing in importance with the diverse, and large, amounts of data that businesses produce. While this can be seen as daunting, it actually gives us all the ability to analyse more innovatively.
Coupled with the growing dominance and capabilities of cloud computing, now is the perfect time to really take a look into “big data analytics” so you too can recognize how the power of crunching big data is bringing competitive advantage to companies.
Big data and cloud computing – a perfect pair
Data processing engines and frameworks are key components in computing data within a data system. Although there is no key difference in the definition between “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 complex, they ultimately share very similar goals — to operate over data in order to broaden understanding and surface patterns while gaining insight on complex interactions.
In order to do all this however, there needs to be infrastructure that supports large workloads – and this is where cloud comes in. Clouds are considered a beneficial tool by enterprises across the world because they have the ability to harness business intelligence (BI) in big data. Also, the scalability of cloud environments makes it much easier for big data tools and applications, like Cloudera and Hadoop, to function.
Programming frameworks available to find the right fit
Several big data tools are available, and some of these include:
Hadoop:This Java-based programming framework supports processing and storage of extremely large sets of data. 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 organizations 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 Effectively
Hadoop, which is modelled after 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 quickly 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 truly a brilliant match made in the clouds. Considering the availability of big data tools like these, working in the cloud to leverage meaningful BI, now is really the perfect time to harness the power of big data to truly drive your business forward.