mapreduceon Spark® and Hadoop® clusters, and parallel pools.
You can use Parallel Computing Toolbox™ to evaluate tall-array expressions in parallel using a parallel pool on your desktop. Using tall arrays allows you to run big data applications that do not fit in memory on your machine. You can also use Parallel Computing Toolbox to scale up tall-array processing by connecting to a parallel pool running on a MATLAB Distributed Computing Server™ cluster. Alternatively, you can use a Spark enabled Hadoop cluster running MATLAB Distributed Computing Server™. For more information, see Big Data Workflow Using Tall Arrays and Datastores.
|Create tall array|
|Create datastore for large collections of data|
|Programming technique for analyzing data sets that do not fit in memory|
|Define parallel execution environment for mapreduce and tall arrays|
|Partition a datastore|
|Number of datastore partitions|
|Create parallel pool on cluster|
|Get current parallel pool|
Learn about typical workflows using tall arrays to analyze big data sets.
Discover tall arrays in Parallel Computing Toolbox and MATLAB Distributed Computing Server.
This example shows how to access a large dataset in the cloud and process it in a cloud cluster using MATLAB capabilities for big data.
Create and use tall tables on Spark clusters without changing your MATLAB code.
mapreduce for advanced analysis of big data using
mapreduce for advanced big data analysis on a
partition to split your
datastore into smaller parts.
Learn about starting and stopping parallel pools, pool size, and cluster selection.
Specify your preferences, and automatically create a parallel pool.
Find out how to work with cluster profiles and discover cloud clusters running on Amazon EC2.