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Datameer - Big Data and Hadoop Analytics

Datameer - Big Data and Hadoop Analytics

Data Visualization - Free Trial of Datameer Datameer’s extensive library of widgets includes tables, graphs, charts, diagrams, maps, and tag clouds which enables users to create simple dashboards or beautiful visualizations. The end result, data visualizations that communicate data. Beyond static business intelligence dashboards. Get started today and share your data visually on any device. Features: Import your data Start analyzing Show off your infographics © 2014 Datameer, Inc.

5 trends that are changing how we do big data — Data | GigaOM Rapid application development Rapid Application Development (RAD) Model Rapid application development (RAD) is a software development methodology that uses minimal planning in favor of rapid prototyping. The "planning" of software developed using RAD is interleaved with writing the software itself. The lack of extensive pre-planning generally allows software to be written much faster, and makes it easier to change requirements. History[edit] Rapid Application Development (RAD) is a term originally used for describing a software development process first developed and successfully deployed during the mid-1970s by the New York Telephone Co's Systems Development Center under the direction of Dan Gielan. Rapid application development is a response to processes developed in the 1970s and 1980s, such as the Structured Systems Analysis and Design Method and other Waterfall models. Four phases of RAD[edit] Relative effectiveness[edit] Criticism[edit] Practical implications[edit] References[edit] Jump up ^ Maurer and S.

Big Data Analytics Big data analytics: From data scientists to business analysts - Data The growing popularity of Big Data management tools (Hadoop; MPP, real-time SQL, NoSQL databases; and others1) means many more companies can handle large amounts of data. But how do companies analyze and mine their vast amounts of data? The cutting-edge (social) web companies employ teams of data scientists2 who comb through data using different Hadoop interfaces and use custom analysis and visualization tools. A startup aims to expose Big Data to analysts charged with producing most routine reports. Datameer’s workflow uses the familiar spreadsheet interface as a data processing pipeline. What’s intriguing about DAS is that it opens up Big Data analysis to large sets of business users. The buzz over Big Data has so far centered largely on (new) data management tools6. (1) Splunk is a tool that does both Big Data management and analytics. (2) In fact data scientist is a title that’s increasingly used in companies like Yahoo!

Infographics & Data Visualization | Why Splice | Splice Machine Organizations are eager to tap into the wealth of data that is at their fingertips, but mere analysis is no longer sufficient. Time is a critical factor when it comes to uncovering insights that can be turned into rapid actions. This makes accessing near-real-time data an imperative for companies who are striving to realize the promise of Big Data by becoming real-time, data-driven businesses. Real-time, analytical applications are emerging to collect, analyze, and react to data without the high costs and poor scalability of traditional databases. Splice Machine, the real-time SQL-on-Hadoop database, can be the lynchpin to these applications because of its ability to Process real-time updatesUtilize standard SQLScale out on commodity hardware As a general-purpose database flexible to handle both analytical (OLAP) and operational (OLTP) workloads, Splice Machine can be used anywhere that MySQL or Oracle can be used, but with the advantage of scaling out with commodity hardware.

Microsoft SharePoint 2010, más implantación pero sin estrategia La cuota de mercado de la plataforma de colaboración de Microsoft crece. Así lo revela un estudio de OpenText donde se pone de relieve la creciente preocupación por la falta de estrategia clara de las empresas a la hora de implantar SharePoint. El informe, realizado entre 362 personas habituadas a utilizar Microsoft SharePoint, demuestra que cada día es más frecuente dentro de las corporaciones, sobre todo, en la gestión de procesos de negocio y el flujo de trabajo. Sin embargo, el alcance del despliegue todavía no es claro. En este sentido, Lubor Ptacek, vicepresidente de marketing estratégico y director general de soluciones Microsoft en OpenText explica que “Con esta información tenemos un mejor conocimiento de las necesidades de nuestros clientes lo que nos permite mejorar nuestros productos, resolver los problemas de los clientes y aumentar la presencia de OpenText en el mercado SharePoint”. Las principales conclusiones del estudio son las siguientes:

Основана в 2003 г., большая компания - 560 Market basket analysis - identifying products and content that go well together Affinity analysis and association rule learning encompasses a broad set of analytics techniques aimed at uncovering the associations and connections between specific objects: these might be visitors to your website (customers or audience), products in your store, or content items on your media site. Of these, “market basket analysis” is perhaps the most famous example. In a market basket analysis, you look to see if there are combinations of products that frequently co-occur in transactions. For example, maybe people who buy flour and casting sugar, also tend to buy eggs (because a high proportion of them are planning on baking a cake). A retailer can use this information to inform: Store layout (put products that co-occur together close to one another, to improve the customer shopping experience) Marketing (e.g. target customers who buy flour with offers on eggs, to encourage them to spend more on their shopping basket) Online retailers and publishers can use this type of analysis to: ! !

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