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Processing Big Data with MATLAB
Learn how to adapt algorithms for data too big to fit in memory and take advantage of clusters or a cloud using MATLAB
Course
Highlights
This one-day course focuses on adapting existing algorithms to work with a collection of data files or a single file that is too big to fit in memory. Learn to represent big data in MATLAB®, adjust existing code to work efficiently with it, and scale up the analysis to take advantage of your own computing resources or a cloud. Topics include:
- Creating datastores to read from data sources
- Representing and manipulating big data using tall arrays
- Importing custom data formats and applying custom functions to tall arrays
- Working with clusters of computers and cloud environments
Who Should
Attend
This intermediate level course is intended for data analyst, scientist, and engineers who needs to process data from many sources (fixed structure or irregular format) and too big to fit in memory.
Course
Prerequisites
MATLAB for Data Processing and Visualization, or equivalent experience using MATLAB
Course
Benefits
Upon the completion of the course, the participants will be able to:
- Represent big data in MATLAB using datastores and tall arrays
- Apply existing algorithms to tall arrays
- Run big data applications on a cluster of multiple computers or a cloud
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
Prototyping Big Data Algorithms
Objective: Applying existing algorithms to data sets that do not fit into memory.
- Importing data using datastores
- Creating tall arrays
- Running algorithms on tall arrays
- Optimizing code for tall arrays
- Reading data from cloud environments
Handling Custom Data and Algorithms
Objective: Importing custom formatted data and applying algorithms that are not implemented for tall arrays
- Importing custom formatted data using file datastores and custom datastores
- Partially importing single files
- Applying transformations, reductions, and moving window operations to tall arrays
Working with Clusters and Clouds
Objective: Run big data algorithms on a cluster of computers or on cloud environments.
- Local and remote clusters
- Cluster discovery and connection
- Setup of a cluster on a cloud environment
- File access considerations
