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Not embracing the Big Data trend can cost your company. According to an Accenture study, 79 percent of enterprise executives agree that companies not embracing Big Data will lose their competitive edge. With data creation on track to grow tenfold by 2025, it is extremely important for businesses to be able to process it in a quick and efficient manner.
So what does “Big Data” mean? The term is often thrown around in the business and tech sectors. Simply put, Big Data describes large data sets that can be used to identify trends and patterns in order to make better business decisions. According to a recent McKinsey Analytics survey nearly 50 percent of responders say that Big Data has fundamentally changed the business practices of their sales and marketing functioning.
Despite the multitude of statistics supporting the use of Big Data, few businesses are actually using it successfully. In a recent survey done by Capgemini, just 27% of executives described their Big Data initiatives as ‘successful.’ Other business leaders remain hopeful, but fail to employ any of the actual technology.
To ensure your company is relevant, it is imperative to effectively implement fast data processing. With data becoming more diverse every day it is extremely important to be able to analyze accurately and creatively.
Let’s take a look into “Big Data Analytics.” As Cloud Computing continues to dominate the production environment, it is important for to recognize the competitive edge that crunching Big Data is bringing to companies.
Combining Big Data and Cloud Computing
Two key components in computing data within a data system are data processing engines and frameworks. Engineers are the component responsible for operating on data while frameworks are typically a set of components designed to do the same. While there is no key difference between the two, it is important to define them separately.
Although systems designed to handle the data lifecycle at this stage are complex, they ultimately share similar goals — to work with data in order to broaden understanding and surface patterns while gaining insight on complex interactions.
However, in order to do all of this, there needs to be an infrastructure that supports large workloads. Enter the cloud. Enterprises value the cloud because it is a beneficial tool that can harness Business Intelligence (BI) in Big Data. The scalability of cloud environments makes it much easier for Big Data tools and applications such as Cloudera and Hadoop to function.
Different types of programming frameworks available
There are several Big Data tools available, and some of these include:
Hadoop: As a Java-based programming framework, Hadoop supports processing and storage of extremely large data sets. Hadoop, an open source framework, is part of the Apache project and is sponsored by the Apache Software Foundation, which works in a distributed computing environment. It supports software packages and components, with the ability to be deployed by organizations in a local data center.
Apache Spark: This tool is a fast engine used for Big Data processing and is capable of streaming and supporting SQL, graph processing and machine learning. As an alternative, 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
Modelled after Google’s MapReduce and File System technologies, Hadoop has gained widespread adoption in the industry. CloudStack has similar framework and implemented in Java.
CloudStack is the first-ever cloud platform to join the Apache Software Foundation. Because of this, the company has quickly become the cloud choice for companies that prefer open source options for their Big Data infrastructure and the cloud.
Hadoop and CloudStack make a perfect match, waiting to be used and deployed in order to analyze Big Data more successfully.