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Select Datastore for File Format or Application

A datastore is a repository for collections of data that are too large to fit in memory. Each file format and application uses a different type of datastore, which contains properties pertinent to the type of data or application that it supports. MATLAB® provides datastores for standard file formats such as Excel® files and datastores for specific applications such as Deep Learning. In addition to the existing datastores, if your data is in a proprietary format, then you can develop a customized datastore using the custom datastore framework.

Datastores for Standard File Formats

For a collection of data in standard file format use one of these options.


Text files containing column-oriented data, including CSV files


Spreadsheet files with a supported Excel format such as .xlsx


Image files, including formats that are supported by imread such as JPEG and PNG

ParquetDatastoreParquet files containing column-oriented data

Files with nonstandard file format

Requires a custom file reading function

ArrayDatastoreIn-memory data

Transform or combine existing datastores.

CombinedDatastoreDatastore to combine data read from multiple underlying datastores
SequentialDatastoreSequentially read data from multiple underlying datastores
TransformedDatastoreDatastore to transform underlying datastore

Datastores to integrate with MapReduce and tall arrays.


Key-value pair data that are inputs to or outputs of mapreduce


Datastore for checkpointing tall arrays

Datastores for Specific Applications

Based on your application use one of these datastores.

ApplicationDatastoreDescriptionToolbox Required

Simulink Model Data

SimulationDatastore (Simulink)

Datastore for simulation input and output data that you use with a Simulink® model


Simulation Ensemble and Predictive Maintenance Data

simulationEnsembleDatastore (Predictive Maintenance Toolbox)

Datastore to manage simulation ensemble data

Predictive Maintenance Toolbox™

fileEnsembleDatastore (Predictive Maintenance Toolbox)

Datastore to manage ensemble data in custom file format

Predictive Maintenance Toolbox

Measurement Data Format (MDF) Files

mdfDatastore (Vehicle Network Toolbox)

Datastore for collection of MDF files

Vehicle Network Toolbox™

mdfDatastore (Powertrain Blockset)

Datastore for collection of MDF files

Powertrain Blockset™

Deep Learning

Datastores for preprocessing image or sequence data

pixelLabelDatastore (Computer Vision Toolbox)

Datastore for pixel label data

Computer Vision Toolbox™ and Deep Learning Toolbox™

boxLabelDatastore (Computer Vision Toolbox)

Datastore for bounding box label data

Computer Vision Toolbox and Deep Learning Toolbox

signalDatastore (Signal Processing Toolbox)Datastore for collection of signal files

Signal Processing Toolbox™ and Deep Learning Toolbox

randomPatchExtractionDatastore (Image Processing Toolbox)

Datastore for extracting random patches from images or pixel label images

Datastore is nondeterministic

Image Processing Toolbox™ and Deep Learning Toolbox

denoisingImageDatastore (Image Processing Toolbox)

Datastore to train an image denoising deep neural network

Datastore is nondeterministic

Image Processing Toolbox and Deep Learning Toolbox

augmentedImageDatastore (Deep Learning Toolbox)

Datastore for resizing and augmenting training images

Datastore is nondeterministic

Deep Learning Toolbox

Audio DataaudioDatastore (Audio Toolbox)

Datastore for collection of audio files

Audio Toolbox™
Out-of-Memory Image DatablockedImageDatastore (Image Processing Toolbox)Datastore to manage blocks of a single image that is too large to fit in memoryImage Processing Toolbox
Database DatadatabaseDatastore (Database Toolbox)

Datastore for collections of data in a relational database

Database Toolbox™

Custom File Formats

For a collection of data in a custom file format, if each individual file fits in memory, use FileDatastore along with your custom file reading function. Otherwise, develop your own fully customized datastore for custom or proprietary data using the class. See Develop Custom Datastore.

Nondeterministic Datastores

Datastores that do not return the exact same data for a call to the read function after a call to the reset function are nondeterministic datastores. Do not use nondeterministic datastores with tall arrays, mapreduce, or any other code that requires reading the data more than once.

Some applications require data that is randomly augmented or transformed. For example, the augmentedImageDatastore (Deep Learning Toolbox) datastore, from the deep learning application augments training image data with randomized preprocessing operations to help prevent the network from overfitting and memorizing the exact details of the training images. The output of this datastore is different every time you perform a read operation after a call to reset.

See Also

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