Big Data with MATLAB
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Big Data with MATLAB

Big Data with MATLAB


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About the Book

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions.The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term "big data," businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.There's no single technology that encompasses big data analytics. Of course, there's advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your informationIn MATLAB(R) you can access remote data using datastore objects. You can create a datastore to work with data stored in remote locations, such as cloud storage using Amazon S3(TM) (Simple Storage Service) and Hadoop(R) Distributed File System (HDFS(TM)). Use the datastore to examine part of your data from your desktop version of MATLAB. Then, after prototyping your code locally, you can scale up to a cluster or cloud. Scaling up improves execution efficiency as it is more efficient to run large calculations in the same location as the data.Tall arrays provide a way to work with data backed by a datastore that can have millions or billions of rows. You can create tall numeric arrays, cell arrays, categoricals, strings, datetimes, durations, or calendar durations, and you can use any of these tall types as variables in a tall table or tall timetable. Many operations and functions work the same way with tall arrays as they do with in-memory MATLAB(R) arrays, but most results are evaluated only when you request them explicitly using gather. MATLAB automatically optimizes the queued calculations by minimizing the number of passes through the data.As the number and type of data acquisition devices grows annually, the sheer size and rate of data being collected is rapidly expanding. These big data sets can contain gigabytes or terabytes of data, and can grow on the order of megabytes or gigabytes per day. While the collection of this information presents opportunities for insight, it also presents many challenges. Most algorithms are not designed to process big data sets in a reasonable amount of time or with a reasonable amount of memory. MapReduce allows you to meet many of these challenges to gain important insights from large data sets.Parallel Computing Toolbox can immediately speed up your tall array calculations by using the full processing power of multicore computers to execute applications with a parallel pool of workers


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Product Details
  • ISBN-13: 9781092649759
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 229 mm
  • No of Pages: 388
  • Spine Width: 22 mm
  • Width: 152 mm
  • ISBN-10: 1092649751
  • Publisher Date: 03 Apr 2019
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Weight: 567 gr


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