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Datameer - Big Data and Hadoop Analytics

Datameer - Big Data and Hadoop Analytics
It takes three vendors, three processes, and three sets of people to do what Datameer does with a single application. Conventional BI requires a complicated, multi-step, multi-vendor process, and is limited to a highly trained set of specialized users. Datameer provides a single application for Big Data Analytics that requires no ETL, no static schemas, and puts powerful analytics and data visualizations directly in the hands of any user. Welcome to analytics at the speed of business.

The DataSift Platform Social data is noisy. Whether you’re trying to social analyze trends within an industry, or mentions of your products or brands, you need a platform that can filter out the noise and allow you to focus on the data that’s most relevant to you. This is especially important when you are paying for the social data you receive. At the heart of the DataSift platform is a high-performance filtering engine with which you can find the exact content and conversations that are relevant to your business. Go beyond keywords and filter on more than over 300 unique fields including author, location, language, and demographics. Filter with greater precision by using advanced search operations including regular expressions, text pattern matching, and substrings to get the most relevant data.

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.

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. 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. Other companies integrate their MPP databases with familiar Business Intelligence tools. For companies that already have large amounts of data in Hadoop, there’s room for even simpler tools that would allow business users to directly interact with Big Data.

About Kaggle and Crowdsourcing Data Modeling Kaggle is the world's largest community of data scientists. They compete with each other to solve complex data science problems, and the top competitors are invited to work on the most interesting and sensitive business problems from some of the world’s biggest companies through Masters competitions. Kaggle provides cutting-edge data science results to companies of all sizes. 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.

Big Data Jobs at Jive Software Jive is on a singular mission to transform the way we work. We’re the first company to bring the social innovation of the consumer web to the enterprise. And in doing so, we’re making work great. Big Data: 9 Steps to Extract Insight from Unstructured Data The increasing digitization of information in recent years, coupled with the proliferation of multi-channel processes and transactions, has resulted in a data deluge. The ever-increasing pace of digital information has led the world's aggregate creation of data to double in even shorter intervals than ever before. According to Gartner, about 80% of data held by an organization is unstructured data, comprised of information from customer calls, emails and social media feeds. This is in addition to the voluminous diagnostic information logged by embedded and user devices.

Scalable Machine Learning for Big Data Using R and H2O - Data Science Las Veg... Part I Part II H2O is an open source parallel processing engine for machine learning on Big Data. Big Data and Apache Hadoop for Financial Services How Big Data and Hadoop Help Financial Services Firms Manage Risk and Stay Competitive Financial services organizations around the world are experiencing drastic change. The global financial crisis of 2008 resulted in the failing of scores of banks, which also impacted incomes, jobs, and wealth.