Big Data Boom!
Each year, we witness a considerable increase in the volume and variety of data that the companies must manage. This data is commonly referred to as Big Data. The information in Big Data is gathered from various sources like social media posts, audios and images to transaction records, data from sensors and video feeds. All of this is growing at a very fast pace. A statistic by International Data Corporation shows that the data is growing at CAG of 40 percent and will continue to do so for the next ten years.
It is becoming a challenge for the companies to collect and store this fast-growing data in an efficient and cost-effective manner. Having said that, the real benefit of the data can be derived only if we can analyze it to improve product quality, fasten decision-making, enhance the customer service experience and optimize business processes. This has proven to work; a survey done by Dell shows that 89 percent of companies with big data initiatives have reported significant improvements in corporate decision-making. Another report by McKinsey Global Institute shares an estimate that retailers who use data analytics in their companies at scale have a chance of increasing their operating margins by more than 60 percent and those healthcare organizations could lessen their costs by eight percent with the usage of Big data analytics.
To achieve such benefits, the organization requires an IT infrastructure that is fully scalable, flexible and cost-effective. Although it is possible to do data analysis to certain extent with the use of traditional IT architectures, companies tend to run into roadblocks quite early in this scenario which results in limiting the amount of data that can be analyzed and thus, affecting the value achieved from data analysis. All this puts a big strain on traditional IT infrastructure – not only the required storage volume but processing power and networking bandwidth.
It is becoming a challenge for the companies to collect and store this fast-growing data in an efficient and cost-effective manner. Having said that, the real benefit of the data can be derived only if we can analyze it to improve product quality, fasten decision-making, enhance the customer service experience and optimize business processes. This has proven to work; a survey done by Dell shows that 89 percent of companies with big data initiatives have reported significant improvements in corporate decision-making. Another report by McKinsey Global Institute shares an estimate that retailers who use data analytics in their companies at scale have a chance of increasing their operating margins by more than 60 percent and those healthcare organizations could lessen their costs by eight percent with the usage of Big data analytics.
To achieve such benefits, the organization requires an IT infrastructure that is fully scalable, flexible and cost-effective. Although it is possible to do data analysis to certain extent with the use of traditional IT architectures, companies tend to run into roadblocks quite early in this scenario which results in limiting the amount of data that can be analyzed and thus, affecting the value achieved from data analysis. All this puts a big strain on traditional IT infrastructure – not only the required storage volume but processing power and networking bandwidth.
One of the major challenges while using traditional architectures is that it requires data to be reduced to a relational database format, which reduces the size, speed and scale of data processing. This results in forcing you to throw away data or age it out since relational database can only handle so much data which means that you can only analyze a subset of the data.
Converged infrastructure systems have proved to have a lot of required resources for effective big data analytics. This is due to the ability to handle Hadoop to storage scalability. There are three main capabilities required in order to get the biggest data analytics payoff:
1. Hadoop – Hadoop, the open-source software developed by Apache for distributed computing is vital for analyzing Big Data. Hadoop is considered one of the best ways to tackle fast-growing data processing, storage and analysis by far.
In the Hadoop ecosystem, you are allowed to keep all your raw data. This is because Hadoop enables you to scale out as data is added by adding nodes with more local disk. So, if you have a dataset which takes two hours to analyse, it will still take two hours if you double it from 200 TB to 400 TB or even get to 2 Petabytes with one thousand nodes.
2. Storage – As large as the data would be, it would require an equally large storage. Now in a scenario where the data grows at high speeds, it is imperative to have as scalable storage architecture as possible. While some converged infrastructure systems are still using traditional storage arrays – with high amounts of flash array but instead the stress should be laid on using converged infrastructure with an embedded scalable storage solution.
3. Optimized for Big Data – It is true that converged infrastructures are usually able to provide easier and faster scalability as compared to traditional architectures. Having said that the optimum environment for Big Data Analytics is a system which is enabled to scale computing power separately. The aim should be set for as much memory, and as little cost and power, as possible. This is inferred keeping in mind that, companies doing analytics will scale out to hundreds or even thousands of nodes.
Big Data Analytics has been one of the most sought after skills in the industry lately. With the generation of huge amounts of data each day, Big Data Analytics has become an integral part of many organizations. This emerging technology provides great opportunities to those who wish to make a career in the field of analytics. Collabera TACT has one of the finest training programs on Big Data and its related technologies. If Big Data excites you and you are fascinated by words like Hadoop, Spark & Storm etc., then enrol for our Big Data Training and usher yourself into a world of unbelievable opportunities.
