Big Data and its relationship with MATLAB

Sep 8th 2015 at 5:01 AM

The commonality between big data and MATLAB is that both are related to data analytics. In today’s world, accurate analysis of data is most important for organisations. It helps forecast future business patterns and can bring out the results of current policies as well. In order to specifically receive data-driven insights that can lead to better decisions and designs, MATLAB is used.


Some of the companies have been able to use predictive analytics in order to gain important insights from big data. Shell is one such company that has managed a significant increase of 6-8% in production. With the help of MATLAB, the company has also been able to make real-time decisions and interventions.

It is advised to always make use of 64-bit MATLAB whenever possible. 32-bit MATLAB may be too small to handle large volumes of data. Make use of sparse matrices and categorical arrays in MATLAB. Always be aware of various overhead cells and structures.

Big Data capabilities in MATLAB are classified under three areas:

1. Memory and data access

· Memory mapped variables

· Disk variables

· Databases

· 64-bit processors


2. Programming constructs

· Streaming

· Block processing

· Parallel-for loops

· GPU Arrays

· SPMD and distributed arrays


3. Platforms

· Desktop (Multicore, GPU)

· Clusters

· Cloud Computing

Sources of MATLAB data

From one integrated environment, MATLAB allows one to access data from a large number of sources like:

· Databases (ODBC compliant), data warehouses

· Financial data servers meant to access historical market data

· Internet of Things devices

· OPC servers that can access live data

· File I/O including spreadsheet, XML, CDF/HDF, audio, video and web content

Integration of data analytics with systems

Analytics developed in MATLAB can be integrated into different production IT environments without needing to create custom infrastructure. MATLAB analytics are packaged as deployable components to be used with various development environments of Microsoft .NET, python, Java, Excel and C/C++. These analytics can be used as a part of web, desktop and enterprise applications. MATLAB analytics can also be managed for low latency and scalable production applications.

Latest tools in MATLAB

MathWorks has now enhanced the capabilities of fourth generation language MATLAB. The users of MATLAB have a number of tools to solve the issues of analyzing big data, which include huge data size and high velocity data. MATLAB comes with built-in MapReduce functionality that analyses data sets that are extremely large to fit into the memory. It is now possible to develop algorithms on a desktop and execute them on a Hadoop cluster. Know more at a MATLAB workshop.


Analytics of big data cannot be handled by any language as good as MATLAB. It can be taught through MATLAB training.

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