The Power of Crunching Big Data Effectively


According to an Accenture study, 79% of enterprise executives agree that companies not embracing big data will lose their competitive edge, with a further 83% affirming that they have pursued big data projects at some point to stay ahead of the curve. Considering that data creation is on track to grow 10-fold by 2025, it’s crucial for companies to be able to process it more quickly, and meaningfully.

The expression “big data” is often bandied around in the business and tech world, but what does that really mean? Big data is a term that’s used to describe extremely large data sets that can be analyzed for trends and patterns in order to make better business decisions.

That may sound easy enough, and although there are extensive research and written information on big data technologies, few companies are actually using big data successfully. In a survey undertaken by Capgemini, just 27% of executives surveyed described their big data initiatives as ‘successful’. Most businesses remain ambitious, knowing they should be employing the technology without actually doing so.

Effectively implementing fast data processing will ensure your company is more up-to-date and relevant, and this is extremely important given how diverse data is becoming — a factor that gives us all the ability to analyze more innovatively.

As cloud computing continues to dominate the production environment, it’s time to 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.

Combining big data and cloud computing

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 at this stage 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.

To do all this, however, there needs to be infrastructure that supports large workloads – this is where the 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.

Different Types of Programming Frameworks Available

There are several big data tools available, and some of these include:

Hadoop: This Java-based programming framework supports the 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 is a 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 modeled 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 organizations 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, waiting to be used and deployed to crunch big data more effectively.

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