Data Science in the Business World with HADOOP — KYVOS Insights

The competence of Hadoop to store and process giant amounts of information and data is frequently accessary with “data science.”

In business, the term is often used interchangeably with business analytics. In actual, the two disciplines are quite different. Business analysts study patterns in existing business operations to renovate them. The goal of data science is to extract information from data. The work of data scientists is based on maths, statistic analysis, pattern recognizance, machine learning, data warehousing, high-performance computing, and much more. They analyze data to look for trends, statistics, and new business probability based on collected data. Over the past decade, many business analysts much familiar with databases and programming have become data scientists, using higher level database tools in the Hadoop ecosystem and running analytics to make informed business judgments. NOT JUST “ONE BIG DATABASE”

Hadoop is simply “one hug database” meant only for data anbalytics. Because some of Hadoop’s tools provide a low entry barrier to Hadoop for people more familiar with database queries, some people limit their knowledge to only a few database-centric tools in the Hadoop ecosystem. Moreover, if the problem that you are trying to solve goes beyond big data analytics and involves true “data science” problems, data mining SQL is becoming significantly less useful. Most of these problems, You have many ways to do that — and you can use multiple tools, which often must be combined with other capabilities that require software-design and development skills.

Current Hadoop development is operated by a goal to superior support data scientists. Hadoop provides a strong computational platform, providing high level scalable, parallelizable execution that is well-suited for the creation of a new generation of powerful data science and enterprise applications. Implementers can levitation both scalable distributed storage and MapReduce processing. Businesses are using Hadoop for solving business problems, with a few notable examples.

➤ Enhancing fraud detection for banks and credit card companies — Enhancing fraud Companies are utilizing Hadoop to detect transaction fraud. By providing analytics on big clusters of commodity hardware, banks are using Hadoop, applying analytic models to a full set of transactions for their clients, and providing near-real-time fraud-in-progress detection.

➤ Social media marketing analysis — Companies are currently using Hadoop for brand management, marketing campaigns, and brand protection. By monitoring, collecting, and aggregating data from various Internet sources such as blogs, boards, news feeds, tweets, and social media, companies are using Hadoop to extract and aggregate information about their products, services, and competitors, discovering patterns and revealing upcoming trends important for understanding their business.

➤ Shopping pattern analysis for retail product placement — Businesses in the retail industry are using Hadoop to determine products most appropriate to sell in a particular store based on the store’s location and the shopping patterns of the population around it.

➤ Traffic pattern recognition for urban development — Urban development often relies on traffic patterns to determine requirements for road network expansion. By monitoring traffic during different times of the day and discovering patterns, urban developers can determine traffic bottlenecks, which allow them to decide whether additional streets/street lanes are required to avoid traffic congestions during peak hours.

➤ Content optimization and engagement — Companies are focusing on optimizing content for rendering on different devices supporting different content formats. Many media companies require that a large amount of content be processed in different formats. Also, content engagement models must be mapped for feedback and enhancements.

➤ Network analytics and mediation — Real-time analytics on a large amount of data generated in the form of organization usage transaction data, network performance data, cell-site information, device-level data, and other forms of back office data is allowing companies to reduce operational expenses, and enhance the user experience on networks.

➤ Large data transformation — The New York Times need to make PDF files for hug data that is 11 million articles (every article from 1851 to 1980) in the form of images scanned from the original paper. By the help of Hadoop, the newspaper was able to convert 4 TB data of scanned articles to 1.5 TB of PDF documents in 1 day.

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