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Big Data Workflow Using Tall Arrays and Datastores

The illustration shows a typical workflow that uses tall arrays to analyze a large data set. In this workflow, you analyze a small subset of the data before scaling up to analyze the entire data set. Parallel computing can help you scale up from steps six to seven. That is, after checking that your code works on the small data set, run it on the whole data set. You can use MATLAB® to enhance this workflow.

ProblemSolutionRequired ProductsMore Information
Is your data too big?

To work with out-of-memory data with any number of rows, use tall arrays.

This workflow is well suited to data analytics and machine learning.

MATLAB

Tall Arrays (MATLAB)

Use tall arrays in parallel on your local machine.

MATLAB

Parallel Computing Toolbox™

Use Tall Arrays on a Parallel Pool

Use tall arrays in parallel on your cluster.

MATLAB

Parallel Computing Toolbox

MATLAB Distributed Computing Server™

Use Tall Arrays on a Spark Enabled Hadoop Cluster

If your data is large in multiple dimensions, use distributed instead.

MATLAB

Parallel Computing Toolbox

MATLAB Distributed Computing Server

Distributing Arrays

Running Tall Arrays in Parallel

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. If you already have Parallel Computing Toolbox installed, then you probably do not need to do anything special to take advantage of these capabilities. For more information about using tall arrays with Parallel Computing Toolbox, see Use Tall Arrays on a Parallel Pool.

Use mapreducer to Control Where Your Code Runs

When you execute tall arrays, the default execution environment uses either the local MATLAB session, or a local parallel pool if you have Parallel Computing Toolbox. The default pool uses local workers, typically one worker for each core in your machine. Use the mapreducer function to change the execution environment of tall arrays to use a different cluster.

One of the benefits of developing your algorithms with tall arrays is that you only need to write the code once. You can develop your code locally, then use mapreducer to scale up and take advantage of the capabilities offered by Parallel Computing Toolbox and MATLAB Distributed Computing Server.

See Also

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