¶ … Gabriel's Rebellion: The Virginia Slave Conspiracies 180
There seems to be a growing consensus of analysts and product reviewers who believe that the most efficient form of BI for Big Data involves Hadoop. The increasing number of software solutions offered by a host of vendors in the last six months attests to this fact. This claims is further bolstered by the fact that there are several aspects of Hadoop that make it ideal for Big Data -- its virtually unlimited scalability, the real-time speed in which it grants access, and its cost efficiency (it's an open source platform that uses commodity hardware). Factor in the amount of variegated data that it can accommodate (which makes it desirable for little data projects as well) and the fact that it has its own system of analytics, and its popularity becomes understandable. Support for Hadoop is increasing, a fact which many NoSQL Big Data platforms cannot claim.
Although Hadoop has its own analytics, much of its BI value lies in its integration with existing software. Its components are powered by batch processing technologies which reduce the efficiency of its data storehouse, Hive, and its analytics. These technologies -- principally MapReduce and Hadoop Distributed Filing System -- are more rigid and slower than traditional BI, making it difficult to analyze data expediently. It's usually more advantageous to use external BI solutions that integrate with Hadoop either with or without Hive.
With the former option, BI products simply accesses data in Hive through their cache and use their own tools to analyze it without involving Hive's ponderous querying system, HiveQL. In addition to expediting the analytics process, Hive-based BI products can utilize its standards based approach to position reports within various distributions of Hadoop. These solutions offer all of the convenience associated with traditional BI including tools for visualization, analysis, forecasting and more, although they frequently aren't as fast as those that don't involve Hive.
Hadoop Without Hive: Real-Time
Users can optimize Big Data's value by integrating BI products with Hadoop without Hive. This...
Data Mining Determine the benefits of data mining to the businesses when employing: Predictive analytics to understand the behaviour of customers "The decision science which not only helps in getting rid of the guesswork out of the decision-making process but also helps in finding out the perfect solutions in the shortest possible time by making use of the scientific guidelines is known as predictive analysis" (Kaith, 2011). There are basically seven steps involved
Data Mining in Health Care Data mining has been used both intensively and extensively in many organizations.in the healthcare industry data mining is increasingly becoming popular if not essential. Data mining applications are beneficial to all parties that are involved in the healthcare industry including care providers, HealthCare organizations, patients, insurers and researchers (Kirby, Flick,.&Kerstingt, 2010). Benefits of using data mining in health care Care providers can make use of data analysis in
Data Mining Businesses can receive many benefits from data mining. Which benefits they receive, however, can also depend on the way in which their data mining is undertaken. Predictive analytics are used to understand customer behavior, and businesses use the behavior of the customer in the past to attempt to determine what the customer will do in the future (Cabena, et al., 1997). While it is not an exact science, many
Data Warehousing and Data Mining Executive Overview Analytics, Business Intelligence (BI) and the exponential increase of insight and decision making accuracy and quality in many enterprises today can be directly attributed to the successful implementation of Enterprise Data Warehouse (EDW) and data mining systems. The examples of how Continental Airlines (Watson, Wixom, Hoffer, 2006) and Toyota (Dyer, Nobeoka, 2000) continue to use advanced EDW and data mining systems and processes to streamline
Data mining, a process that involves the extraction of predictive information which is hidden from very large databases (Vijayarani & Nithya,2011;Nirkhi,2010) is a very powerful and yet new technology having a great potential in helping companies to focus on the most important data in their data warehouses. The use of data mining techniques allows for the prediction of trends as well as behaviors thereby allowing various businesses to make proactive
The use of databases as the system of record is a common step across all data mining definitions and is critically important in creating a standardized set of query commands and data models for use. To the extent a system of record in a data mining application is stable and scalable is the extent to which a data mining application will be able to deliver the critical relationship data,
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