big data

Tips to leverage the cloud for Big Data.

In the competitive business atmosphere of today, the big data is considered as an asset that can be critical to achieving business success. The data help us to interpret the behavior of customers, to improve the quality and cost of operations, to carry out innovative products, and ultimately increase the bottom line.

With each click, ‘I like’, ‘tweet’, ‘check-in’, ‘share’ and calls through a PPP, we are generating data. Big Data is all that is to store, process, analyze, organize, share, distribute and display all this wealth of data, so that companies can gain valuable information and make better business decisions as quickly as possible .

Cloud computing ensures that our ability to analyze large amounts of data is not limited by the capacity and computing power. The cloud gives us access to virtually unlimited capacity, to the letter, and that companies only pay for the resources they consume. Thus, the total cost is reduced, maximizing revenue and obtains a data processing on a large scale.

Elasticity, which is the ability to grow or reduce technological resources, is a fundamental property of cloud computing and improved benefits. While traditional data warehouses, which are configured to respond regularly to topics such as generating sales reports every night, have a capacity that can be easy to predict, while analyzes to discover new trends and correlations in the data is an activity that requires an unpredictable amount of computing cycles and storage. For example, to process large volumes of data in traditional installations, companies have to provide the maximum power that may need at some point in the future. To process large volumes of data in the cloud, companies can expand their technological resources and decrease depending on the amount needed at the time. no longer they have to wait weeks or months to obtain and install physical storage servers with cloud computing companies can deploy hundreds or thousands of servers in hours. To get the most out of the data, here are some ideas of how to use cloud services to access analysis and great potential of Big Data:

Increase your data

Possessing good quality data usually is usually better than having a lot of data. The data that are incorrect or inconsistent could lead to biased results. For example, when you have to analyze data from hundreds of different sources, inconsistency in the structure and format of the data sets usually they lead us to have a partial view of it, especially when data are not incorporated or transformed in a common format. In order to obtain accurate and consistent data, it is important to improve them, which may include cleaning, validation, standardization, deduction and collating data.

Companies can improve their data programmatically through scripts and programs, however, some data analysis such as photo tagging, regulation of catalogs or simply spell checking requires human intervention to ensure accuracy. The use of a template broad, adaptable and scalable workers is the key to improving data. By dividing large amounts of data analysis tasks in short, it allows them to complete quickly and be able to distinguish the quality and reliability of the data, which is something that computers can not easily do.

Direct your source data to the cloud

If your philosophy is to gather as much data as possible and measure everything, you will have a massive storage capacity. The cloud storage is scalable, durable, reliable, high availability and most importantly, it is cheap. Another benefit that characterizes the cloud storage is that instead of moving the data regularly, you can direct your source data directly to the cloud, bringing data closer to the computing resources for analysis, and thus reduce latency.

In addition, the cloud storage makes it easier to share data with partners and other stakeholders, and have access to information anytime, from anywhere, and exploit the resources demanded and ‘paid per use’ to extract and calculate data.

Analyze your data in parallel using an elastic Supercomputer

The main challenges in the effective implementation of Big Data analysis include hardware installation and management, the ability to scale up and down elastically, and incorporating data from multiple sources. In addition, data processing systems must allow economic experience of being with the Big Data, as the data are likely to change over time. The open platform Hadoop and its ecosystem of tools, helps solve these problems as scale horizontally to accommodate the growing volumes of data and process structured and unstructured data in the same environment. Hadoop integrates many technologies such as statistical packages and a variety of programming languages to accommodate complex data analysis.

Hadoop platform hosted in the cloud, eliminating the cost and complexity of creating and managing a Hadoop installation. This means that any developer or business has the power to do analysis without large capital expenditures. Today, it is possible to adapt a cluster of Hadoop in the cloud in just minutes, the last high-performance network and computer hardware without making a capital investment for the purchase of resources in advance. Organizations have the ability to expand and reduce the size of a cluster, which means that if you need answers more quickly, immediately can scale the size of your group to make data calculations faster.

Are Spanish companies betting on the bigdata already are?

Spanish companies have used cloud technology Amazon Web Services (AWS) for itswork for years. For example, financial sector clients have used AWS for their work Big Data and High Performance Computing (HPC) to help their organizations save money and be more agile. A good example of this is Bankinter. Bankinter has been uploaded to the Amazon Web Services cloud for competitive advantage, carrying out credit risk simulations to assess the financial health of its customers. By incorporating cloud their IT environment, Bankinter has led the average time of their simulations of 23 hours 20 minutes. Bankinter also estimated to have saved a hundred times the amount they had invested in hardware.

In Spain, another example is MAPFRE. MAPFRE is using AWS for HPC in order to calculate its solvency as a company. Each month the insurance companies have to make a solvency test to test your risk in the worst possible scenario. This requires making mathematical calculations with policies all customers to check if the company would have the ability to meet the payment of all debts. This means that if the company had to pay all your debts at once, they would have enough assets to meet payments. Perform these calculations it requires high performance machines that only a few times a month are used. AWS gives the possibility to MAPFRE to have a supercomputer on demand and get rid of it when I’m done, paying only for what they use. This is helping to make substantial savings MAPFRE. The initial investment in hardware for 3 years is estimated at more than 1 million euros compared with less than 180000 euros to pay for the use of AWS infrastructure in the same period.

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