As your data sets grow in both size and complexity, it becomes more and more difficult to work with them, particularly when the data does not fit in memory. MATLAB provides a single, high-performance environment for working with big data that makes it easy, convenient, and scalable to analyze and process big data.
In this webinar, you will learn strategies and techniques for handling large amounts of data in MATLAB. New big data capabilities in MATLAB R2016b will be highlighted, including tall arrays. Using tall arrays, there's no need to learn big data programming or out-of-memory techniques -- simply use the same code and syntax you're already used to.
Topics covered include:
- Accessing data in large text files, databases, or from the Hadoop Distributed File System (HDFS)
- Leveraging tall arrays to analyze and process data that does not fit in memory
- Using Parallel Computing Toolbox for increased performance
- Running on compute clusters, Hadoop, or Spark
About the Presenter
Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.
Prior to R2019a, MATLAB Parallel Server was called MATLAB Distributed Computing Server.
