Big Data Analytics
Definition - What does
Big Data Analytics
Big data analytics refers to the strategy of analyzing large volumes of data, which is often called big data. Big data originates from anywhere, such as the sensors designed to collect climate data, social networking sites, videos and digital images, cellphone GPS signals, and sale transaction records, among others. Big data analytics helps to explore concealed patterns, unidentified connections and provide other valuable insights into the data. Big data analytics can give organizations an edge over their rivals and lead to business rewards, including more potent promotion and enhanced revenue.
The key goal of big data analytics is to assist organizations in making superior business decisions.
Big Data Analytics
Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources, which might be left untapped by traditional business intelligence (BI) systems. That's because traditional systems often fail to analyze numerous data sources, which include Internet click-stream data and Web server logs, social networking activity reports, cellphone call detail data and sensor-captured data. Some experts connect big data and big data analytics to unstructured data of that particular type, while others also include transactions and other structured data as real types of big data.
Software programs popular within sophisticated analytics disciplines, including data mining and predictive analytics, are used for big data analytics. However, the unstructured data sources put to use in big data analytics might not be suited to conventional data warehouses. In addition, such warehouses may not be fit to manage big data's processing requirements. As a result, an innovative category of big data concepts has emerged. Several big data analytics environments are making use of this new technology. The technologies related to big data analytics include Hadoop, MapReduce and NoSQL databases. These technologies constitute the central part of an open-source software framework, which facilitates huge data sets processing over clustered systems.
Data Warehouse (DW)
Reining in Real-Time Big Data with SQLstream
- Unlike traditional relational database management systems (RDBMS), which persist data that can later be queried, SQLStream runs continuous queries on targeted data streams - enabling a real-time view of an organization's situation.
Video: Drew Conway and Jake Porway on Data Without Borders
- This talk from the 2011 Strata Conference in New York looks at how big data can be used to solve big problems.
Big Data: How It's Captured, Crunched and Used to Make Business Decisions
- Finding ways to turn the flood of data into useful information for business decisions is a growing challenge to the IT profession and C-level executives.
An Interview with Gil Press: Venturing Beyond the Big Data Hype
- Writer Troy Sadkowsky talks to big data expert Gil Press about big data today and in the future.
Today's Big Data Challenge Stems From Variety, Not Volume or Velocity
- Too many IT departments throw everything they have at the issues of data volume and velocity, forgetting to address the fundamental issue of the variety of data.
Big Data: Experts to Follow on Twitter
- Big data is, well, pretty big right now. Who knows the latest? Here is the ultimate guide to big data experts to follow.
How Big Data's Getting Smaller
- How to collect and analyze big data is only one side of the equation; the other is how to understand it. Here are some ingenious ways that new tools are making big data smaller.
Bigger Than Big Data? Mobile User Expectations
- A Techopedia exclusive: Jeff Hasen, chief marketing officer of Hipcricket, shares his take on how big data is shaping and driving mobile user expectations globally.
5 Things You Need to Know About Big Data
- Big data is like a puzzle. Put it together in a way that works for your organization, and you can help it thrive. Here are five things you need to know first.
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Big Data Analytic
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