MATLAB Data Capabilities in R2017a

Got Data?

Working with it just got easier in MATLAB.

Capabilities introduced in recent releases make working with data in MATLAB® even easier. New features help you access, preprocess, and analyze your data, regardless of its format or size.


Managing and Preprocessing Data

Preparing your data for analysis is often the most time-consuming task. Learn about new functionality for storing, managing, and preprocessing a range of data types.

Manage time-stamped tabular data with time-based indexing and synchronization​​​. 

Introduced in R2016b

Manipulate, compare, and store text data efficiently​​​​. 

Introduced in R2016b

  • Find, fill, and remove missing data
  • Detect and replace outliers
  • Smooth noisy data 

Detect formats and automatically return appropriate data types with the Import Tool and functions for import and export.


Big Data

Whether you’re on a desktop, or using Spark or Hadoop, new features help you work with those files that are too big to fit in memory, so you don’t need to learn big data programming.

Manipulate and analyze data that is too big to fit in memory. 

Introduced in R2016b

Perform statistical analysis and machine learning on out-of-memory data.

Requires Statistics and Machine Learning Toolbox

Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters.

Requires Parallel Computing Toolbox

Run applications on your desktop or Spark using tall arrays or the MATLAB API for Spark.

Requires MATLAB Compiler


Data Formats

Read and write more of your data directly with MATLAB using added support for the following formats:

iPhone and Android Sensors

Log data from iPhone and Android mobile sensors on devices or MathWorks Cloud.

Retrieve graph data from Neo4j

Requires Database Toolbox

Read and write PCD files using Point Cloud File I/O functions.

Requires Computer Vision System Toolbox

Import data and attachments stored in MDF files

Requires Vehicle Network Toolbox